5.1 Selection of use cases and testing infrastructure at ...

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Distribution grid planning and operational principles for EV mass roll‐out while enabling DER integration

Deliverable (D) No: 5.1 Selection of use cases and testing infrastructure at DSOs

Author: Date: Version:

Dr. Armin Gaul, RWE 12.11.2014 1.0 www.PlanGridEV.eu

Confidential (Y / N): N

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007‐2013) under grant agreement No. 608957.

D5.1 Selection of use cases and testing infrastructure at DSOs

Title of the Selection of use cases and testing infrastructure at DSOs Deliverable WP number 5 Task title Main Author Project partners involved

WP title

WP leader

Validation / Real Testing EDP T5.1 Selection of use cases and testing infrastructure at DSOs Armin Gaul/ RWE Eduardo Zabala, Raúl Rodríguez / Tecnalia Stefan Übermasser / AIT Luís Filipe Matos Silvestre / EDP Cristina Silvestri, Giovanni Coppola / ENEL Senan McGrath, Donal Herraghty / ESB

Type (Distribution level)  PU, Public PP, Restricted to other program participants (including the Commission Services) RE, Restricted to other a group specified by the consortium (including the Commission Services) CO, Confidential, only for members of the consortium (including the Commission Services) Status  In Process  In Revision  Approved Further information

www.PlanGridEV.eu

141113_AG PGEV_WP5_D5 1_V1.0_submitted.docx


Executive Summary The scope of this document is to describe the DSO test beds and to identify the exact use cases for the validation of operational methods in each of the four test beds. Initial situation The main objective of WP5 in total is to validate and test in a real environment operational methods for intelligent charging and active load and congestion management relying on the infrastructure and existing pilot projects by the involved DSOs EDP, ENEL, ESB and RWE. Also, a validation of the planning tool (WP4) will be conducted. As a basis to set up the test beds the input from the previous deliverables has to be considered and implemented, respectively mapped with the test beds. As there are 

WP1, D1.1, Current requirements, regulatory gaps and expected benefits Considering especially the general grid description (e.g. rural/urban)



WP1, D1.2, Future Scenarios Considering especially the basis for the future scenarios developed in D2.1 (e.g. expected DER integration and EVs).



WP1, D1.3, Gap for Energy grids and KPI Report Considering especially the selected KPIs



WP2, D2.1, EV-Integration Business Scenarios Table 1 ‐ PlanGridEV scenarios Conventional No

Safe Soft, fleet-focused

Proactive Massive

None

On/off

On/off

Yes Yes

Yes Minimal

Minimal No

No No

None

Grid  EV

Grid  EV

Grid () EV

Provider of the service

None

EVSE Operator (fleet manager)

Remuneration scheme

None

ToU

EVSE Operator/EVSP Regulated contract

Competitive market

Centralised None None None None

Centralised Centralised None None None

Centralised Decentralised None None None

Centralised Decentralised Decentralised Decentralised Decentralised

Charge management Type of charge management Expected grid reinforcements Non EV-related EV-related Energy flow in EVs that are used to provide services

Type of power flow control for Emergency constraint mgt. Forecasted constraint mgt. Ancillary services for the TSO Energy trade DER integration


Smart grid Massive, local Charge modulation

EVSP

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

WP2, D2.2, Technical requirements for tools/methods for smart grid integration of EVs Considering especially the additional use cases and the first rough description/thoughts of the Test beds.



WP3, D3.2 Specification of Energy Grid/ Functional & Service Architecture (in process) Considering especially the smart charging services.

Goal of D5.1 The target of D5.1 is to describe, how the relevant scenarios, use cases and services can be applied in the test beds from the four participating DSOs.


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Method To achieve this goal, first of all, we matched the use cases with the DSOs test beds. The test beds of the DSOs involved in PlanGridEV cover the above named scenarios and cover the specific framework in the involved European countries. The test beds are located in the grids of the participating DSOs in Europe. Figure 1 shows the approximated geographical location of the four DSO test beds. They cover different climatic zones differ in type of grids and represent medium and low voltage grids as well as rural and urban topologies.

Figure 1 - Overview of the DSO test bed grids1 Based on currently available statistical information, the relevant data for each test bed was collected and is presented in this document. This includes description and classification of the grids, like location, urban/rural, number of households, installed renewable generation, charge poles, EVs etc. For the DSO test beds also the grid data like network length, number of nodes etc. is provided. Additionally, available statistical information on population and weather data for each of the test beds were collected. The future potentials of EVs, charging infrastructure and DERs (PV systems only) were calculated. Due to the historical background and the national positions in renewable energy integration and e-mobility, the capabilities of the test beds differ from DSO to DSO. The proposed KPI can be calculated with this data for the test beds.

1

Source: www.plangridev.eu


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In total the DSO test beds represent two rural and two urban grid areas and the corresponding topology types identified during the WP 1. In terms of population within the grid areas, the test beds cover small LV branches consisting of four households up to larger MV nodes supplying 1925 households and several commercial and industrial customers. Using this data also scenarios for island grid can be covered. Combining the capabilities the test beds cover all the scenarios and use cases proposed by PlanGridEV. Table 2 gives an overview of the capabilities of the different test beds. Table 2 ‐ Test bed relation with scenarios and services Test Bed Portugal Ireland Italy Germany

Scenario Conventional-Safe (EV and DER on/off control possible but no reinforcements expected, no EVSEO, no ToU tariffs) Conventional-Safe (charging at nights: kind of toU tariff) Smart Grid - Proactive (no V2G, no competitive market) Proactive - Smart Grid (no V2G, no competitive market, centralised power control, provider of the service is the DSO, charge modulation done by the HEC)

On this basis use cases have been proposed for each of the test beds. Aspects such as scenario steps, involved actors, triggering events, pre and post-conditions, information interfaces and characteristics have been addressed, following a format used at European level (SG-CG proposal). Breaking down complex use cases into simpler steps additionally permits a better understanding and an easiest translation of operation processes for operational methods validation with the help of WP4 tool. Following this generic approach the 4 test beds are described in detail in this document. WP4 as well as WP5 will benefit from this work because D5.1 builds the basis and delivers the necessary data for evaluating the test beds.


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Table of contents

Executive Summary ................................................................................................................... 5 Table of contents ........................................................................................................................ 9 List of figures ........................................................................................................................... 12 List of tables ............................................................................................................................. 14 Abbreviations and Acronyms ................................................................................................... 16 1. Introduction ....................................................................................................................... 18 2. Use cases analysis for the test beds................................................................................... 19 2.1. Introduction ............................................................................................................... 19 2.2. The use cases to be test bed ....................................................................................... 21 2.3. The KPIs to judge the outcome of the use cases ....................................................... 21 2.4. Use cases in the test beds ........................................................................................... 24 2.4.1. Portugal .............................................................................................................. 24 2.4.2. Ireland................................................................................................................. 29 2.4.3. Italy..................................................................................................................... 32 2.4.4. Germany ............................................................................................................. 38 3. Overview of the test beds grids ......................................................................................... 43 3.1. Demographical data of the specific area (statistical data) ......................................... 44 3.2. Share of In-/intra-/out commuters ............................................................................. 44 3.3. Customer structure / network node (DSO data) ........................................................ 45 3.4. Local DERs, and EV charging infrastructure ............................................................ 46 3.4.1. EV charging infrastructure ................................................................................. 46 3.4.2. PV Systems ........................................................................................................ 46 3.4.3. Wind turbines ..................................................................................................... 47 3.5. Local historical weather data ..................................................................................... 47 3.5.1. Temperature ....................................................................................................... 47 3.5.2. Solar Radiation ................................................................................................... 47 3.6. KPIs at DSOs test beds .............................................................................................. 49 4. Overview of the Portuguese (EDP) test bed ..................................................................... 50 4.1. Description of the process and of the different actors involved ................................ 51 4.2. Business objects and system interfaces ..................................................................... 56 4.3. Test bed location ........................................................................................................ 56 4.3.1. Objectives ........................................................................................................... 57 4.3.2. Criteria ................................................................................................................ 57 4.3.3. Test bed description ........................................................................................... 58 4.4. Test bed timeline ....................................................................................................... 60 4.5. Test bed exploitation and dissemination ................................................................... 60 4.6. Test cases overview ................................................................................................... 61 4.6.1. Description of main validation tests ................................................................... 62 5. Overview of the Italian (ENEL) test bed .......................................................................... 64 5.1. Description of the process and of the different actors involved ................................ 64 5.2. Business objects and system interfaces ..................................................................... 67 5.2.1. Interface 1: EV-EVSE ........................................................................................ 67


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5.2.2. Interface 2: EVSE-EVSE Operator .................................................................... 69 5.2.3. Interface 3: EVSE Operator - DSO .................................................................... 69 5.2.4. Interface 4: EVSE Operator - EVSP .................................................................. 70 5.2.5. Interface 5: EV User - EVSP.............................................................................. 71 5.2.6. Interface 6: EVSP – Energy Vendor .................................................................. 71 5.2.7. Interface 7: DSO – RES Operator ...................................................................... 72 5.2.8. Interface 8: DSO – LV/MV Grid ....................................................................... 72 5.3. Test bed location ........................................................................................................ 72 5.4. Test bed timeline ....................................................................................................... 75 5.5. Test bed exploitation and dissemination ................................................................... 78 5.6. Test cases overview ................................................................................................... 79 5.6.1. Test Case 1: Customer preferences for smart charging...................................... 79 5.6.2. Test Case 2: Power modulation .......................................................................... 79 5.6.3. Test Case 3: DMS-EMM connection for load management .............................. 80 6. Overview of the Irish (ESB) test bed ................................................................................ 82 6.1. Description of the process and of the different actors involved ................................ 83 6.1.1. DSO - ESB Networks ......................................................................................... 83 6.1.2. EVSO – ESB eCars ............................................................................................ 83 6.1.3. Electricity Service Provider ............................................................................... 83 6.1.4. EV User .............................................................................................................. 83 6.2. Business objects and system interfaces ..................................................................... 84 6.2.1. Technical Interface ............................................................................................. 85 6.2.2. Commercial Interface ......................................................................................... 86 6.3. Test bed location ........................................................................................................ 87 6.4. Test bed timeline ....................................................................................................... 90 6.5. Tested exploitation and dissemination ...................................................................... 90 6.6. Test cases overview ................................................................................................... 91 6.6.1. Test Case 1 – No Electric Vehicles Connected .................................................. 92 6.6.2. Test Case 2 – Electric Vehicles deployed but charging restricted ..................... 92 6.6.3. Test Case 3 – Electric vehicles deployed with unrestricted charge times .......... 92 6.6.4. Test Case 4 – Intelligent charging ...................................................................... 92 7. Overview of the German (RWE) test bed ......................................................................... 93 7.1. Description of the process and the different actors involved .................................... 93 7.2. Business objects and system interfaces ..................................................................... 94 7.3. Test bed location ........................................................................................................ 94 7.3.1. Field test region Kisselbach ............................................................................... 94 7.3.2. Field test region Wertachau................................................................................ 95 7.3.3. Planning and project structure ............................................................................ 96 7.3.4. Criteria for the selection of the test beds ............................................................ 96 7.4. Test bed timeline ....................................................................................................... 98 7.5. Test bed exploitation and dissemination ................................................................. 100 7.6. Test cases overview ................................................................................................. 101 7.6.1. The environment for the lab test ...................................................................... 102 7.6.2. Forecasting ....................................................................................................... 105 7.6.3. Test R-1: Cycle test .......................................................................................... 106 7.6.4. Test R-2: Functional test .................................................................................. 107


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7.6.5. Test R-3: Stable state........................................................................................ 109 7.6.6. Test R-4: False forecast .................................................................................... 110 7.6.7. Test R-5: Communication fault/component fault: ........................................... 114 7.6.8. Test R-6: Total black-out in the MV grid ........................................................ 115 7.6.9. Test R-7: Data read error .................................................................................. 116 7.6.10. Test R-8: Putting the Smart Operator in passive mode ................................ 116 7.6.11. Test R-9: A voltage rise in the MV grid ....................................................... 116 7.6.12. Test R-10: Managing a single LV section .................................................... 122 8. DSOs Future Scenarios at test beds grids ....................................................................... 126 8.1. EV population .......................................................................................................... 126 8.2. Charging Infrastructure............................................................................................ 126 8.3. PV Systems .............................................................................................................. 127 9. Conclusions ..................................................................................................................... 128 10. References ................................................................................................................... 129 10.1. Project documents ................................................................................................ 129 10.2. External documents .............................................................................................. 129 11. Revisions ..................................................................................................................... 131 11.1. Track changes ...................................................................................................... 131


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List of figures FIGURE 1 - OVERVIEW OF THE DSO TEST BED GRIDS ................................................................ 7 FIGURE 2 - OVERVIEW OF THE DSO TEST BED GRIDS .............................................................. 43 FIGURE 3 - AVERAGE MONTHLY TEMPERATURES AT DSO TEST BEDS ....................................... 47 FIGURE 4 - PHOTOVOLTAIC SOLAR ELECTRICITY POTENTIAL IN EUROPEAN COUNTRIES ............. 48 FIGURE 5 - EDP DTC AND EDP ENERGY BOX ....................................................................... 50 FIGURE 6 - CUSTOMER LOAD PROFILE .................................................................................... 50 FIGURE 7 - LV GRID SIMULATION WITH DPLAN ........................................................................ 52 FIGURE 8 - LV + DER GRID SIMULATION WITH DPLAN ............................................................. 52 FIGURE 9 - PORTUGUESE SYSTEM INTERFACES AND ACTORS INVOLVED .................................. 56 FIGURE 10 - EDP TEST BED LV NETWORK ............................................................................. 58 FIGURE 11 - GANTT CHART FOR PLAN GRID EV TEST BED ....................................................... 60 FIGURE 12 - FRAMEWORK ARCHITECTURE OF ELECTRIC MOBILITY AND ITS INFORMATION INTERFACES (IF 1… 8) ...................................................................................... 65 FIGURE 13 - DEPLOYMENT OF SMART CHARGING SERVICE IN ITALY TEST BED ........................... 66 FIGURE 14 - L’AQUILA AND ABRUZZO REGION ........................................................................ 73 FIGURE 15 - ENEL AC CHARGING STATION FOR PUBLIC CHARGING .......................................... 73 FIGURE 16 - EXECUTION OF “PLANNED DEMAND RESPONSE: ENHANCED RES INTEGRATION” SERVICE ........................................................................................................... 74 FIGURE 17 - GANTT CHART FOR PLAN GRID EV TEST BED ....................................................... 76 FIGURE 18 - MATCHING OF ITALY’S TEST BED AGAINST “SMART GRID” SCENARIO DEFINED IN D2.1. .. 77 FIGURE 19 - EXAMPLE OF PWM DUTY CYCLE VARIATION DURING CHARGING PROCESS............. 80 FIGURE 20 - EXAMPLE OF DMS TEST CASE ............................................................................ 81 FIGURE 21 – TECHNICAL INTERFACE ...................................................................................... 85 FIGURE 22 – COMMERCIAL INTERFACE................................................................................... 86 FIGURE 23 – IRELAND TEST BED LOACTION ............................................................................. 87 FIGURE 24 - PV POTENTIAL - EUROPEAN UNION, 2012 ........................................................... 88 FIGURE 25 - ESB ECARS HOME CHARGER............................................................................. 88 FIGURE 26 - ESB ECARS 16A PUBLIC CHARGER AND 50KW DC FAST CHARGER .................... 88 FIGURE 27 - NETWORK TOPOLOGY TEST BED LOCATION ........................................................ 89 FIGURE 28 - TIMELINES FOR PLANGRIDEV TEST BEDS ........................................................... 90 FIGURE 29 - TEST BED KISSELBACH ...................................................................................... 95 FIGURE 30 - TEST BED WERTACHAU ...................................................................................... 97 FIGURE 31 - TEST BED WERTACHAU ...................................................................................... 97 FIGURE 32 - GANTT CHART FOR PLAN GRID EV TEST BED ....................................................... 98


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FIGURE 33 - SET UP OF THE EXPERIMENTAL NETWORK WITH LOADS AND GENERATORS OF THE SMART OPERATOR .......................................................................................... 103 FIGURE 34 - GENERATING HOURLY FORECASTS .................................................................... 105 FIGURE 35 - VOLTAGE DURING TEST R-1 .............................................................................. 107 FIGURE 36 - VOLTAGE PROFILE IN TEST R-3 ......................................................................... 110 FIGURE 37 - STATUS OF CONTROL OVER LOW PV FEED AFTER TEST R-4 ............................... 111 FIGURE 38 - VOLTAGE PROFILE DURING LOW DER (PV) FEED IN TEST R-4 ............................ 112 FIGURE 39 - THE SMART OPERATOR CONTROL IN TEST R-4 .................................................. 113 FIGURE 40 - VOLTAGE PROFILE WITH STRONG PV FEED IN TEST R-4 ..................................... 114 FIGURE 41 - VOLTAGE PROFILE IN TEST R-9 ......................................................................... 118 FIGURE 42 - TEST R-9: VRDT AND SWITCH .......................................................................... 120 FIGURE 43 - TEST R-9: BATTERY STORAGE .......................................................................... 120 FIGURE 44 - R-9: CHARGE POLE (EVSE).............................................................................. 120 FIGURE 45 - VOLTAGE IN TEST R-10 .................................................................................... 122 FIGURE 46 - TEST R-10: GRID-BATTERY STORAGE ................................................................ 124 FIGURE 47 - TEST R-10: CHARGING BOX .............................................................................. 124


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List of tables TABLE 1 - PLANGRIDEV SCENARIOS ........................................................................................ 5 TABLE 2 - TEST BED RELATION WITH SCENARIOS AND SERVICES ................................................ 8 TABLE 3 - ACRONYMS............................................................................................................ 16 TABLE 4 - PLANGRIDEV SCENARIOS ...................................................................................... 19 TABLE 5 - TEST BED RELATION WITH SCENARIOS AND SERVICES .............................................. 21 TABLE 6 - USE CASES DESCRIBING TEST BEDS ........................................................................ 21 TABLE 7 - KEY PERFORMANCE INDICATORS ............................................................................ 23 TABLE 8 - USE CASE RELATED KPIS ....................................................................................... 23 TABLE 9 - USE CASE NO. 1: LOAD MANAGEMENT THROUGH NETWORK CONFIGURATION AND EV/DER ON/OFF CONTROL................................................................................ 25 TABLE 10 - USE CASE NO. 2: OFF-LINE MONITORING AND EV DEMAND RELATED RISK IDENTIFICATION ................................................................................................. 30 TABLE 11 - USE CASE NO. 3: EV SMART CHARGING THROUGH OPTIMUM SCHEDULE DEFINITION . 33 TABLE 12 - USE CASE NO.4: EV CHARGING SCHEDULE MODIFICATION BASED ON REAL TIME CONTROL .......................................................................................................... 35 TABLE 13 - USE CASE NO.5: REAL TIME OPTIMIZATION OF THE DISTRIBUTION NETWORK OPERATION ....................................................................................................... 39 TABLE 14 - DSO TEST BEDS STATISTICAL DATA ...................................................................... 44 TABLE 15 - STATISTICAL COMMUTER DATA ............................................................................. 45 TABLE 16 - COMMUTER SHARE AT DSO TEST BEDS ................................................................ 45 TABLE 17 - CUSTOMER STRUCTURE AT DSO TEST BEDS ......................................................... 46 TABLE 18 - EXISTING CHARGING INFRASTRUCTURE ................................................................. 46 TABLE 19 - EXISTING PV SYSTEMS AT DSO TEST BEDS .......................................................... 46 TABLE 20 - SOLAR RADIATION AT DSO TEST BEDS .................................................................. 48 TABLE 21 - PLANGRIDEV PROJECT KPIS ............................................................................... 49 TABLE 22 -SUMMARY OF MAIN CHARACTERISTICS OF THE DIFFERENT SCENARIOS ADAPTED TO EDP TEST BED.................................................................................................. 53 TABLE 23 - SUMMARY OF CUSTOMER STRUCTURE FOR EACH MV/LV SUBSTATION. ................... 59 TABLE 24 - MATCHING OF PORTUGUESE TEST BED AGAINST “CONVENTIONAL SCENARIO” DEFINED IN D2.1 ............................................................................................................. 61 TABLE 25 - SUMMARY OF MAIN CHARACTERISTICS OF THE DIFFERENT SCENARIOS ADAPTED TO ENEL TEST BED............................................................................................... 64 TABLE 26 MAIN CHARACTERISTICS OF THE DM7N LV NETWORK NODES ................................. 75 TABLE 27 - SUMMARY OF MAIN CHARACTERISTICS OF THE DIFFERENT SCENARIOS ADAPTED TO ESB TEST BED ................................................................................................. 82


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TABLE 28 - MATCHING OF IRELAND TEST BED AGAINST “CONVENTIONAL SCENARIO” DEFINED IN D2.1 ................................................................................................................ 91 TABLE 29 - SUMMARY OF MAIN CHARACTERISTICS OF THE DIFFERENT SCENARIOS ADAPTED TO RWE TEST BED ................................................................................................ 94 TABLE 30 - MATCHING OF GERMANY’S TEST BED AGAINST “PROACTIVE” AND “SMART GRID” SCENARIO DEFINED IN D2.1 ............................................................................... 99 TABLE 31 - COMPARISON OF ELECTRICAL DIMENSIONS REFERRING TO TEST R-2 .................... 109 TABLE 32 - RESULTS OF TEST R-9 ....................................................................................... 117 TABLE 33 - COMPARISON OF VOLTAGES IN TEST R-10........................................................... 123 TABLE 34 - EV POPULATION AT DSO TEST BEDS .................................................................. 126 TABLE 35 - CHARGING INFRASTRUCTURE AT LOCATION TYPES BASED ON CUSTOMER STRUCTURE126 TABLE 36 - PV SYSTEMS AND FUTURE POTENTIAL AT DSO TEST BEDS .................................. 127 TABLE 37 - TEST BED RELATION WITH SCENARIOS AND SERVICES .......................................... 128 TABLE 38 - TRACK CHANGES ............................................................................................... 131


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Abbreviations and Acronyms Table 3 ‐ Acronyms ASIS

As it is (Scenario)

BAU

Business as usual

CAPEX CBA CEER DER DG

CAPital EXpenditure Cost balance Analysis Council of European Energy Regulators Distributed Energy Resources Distributed Generation

DMS

Distribution Management System

DoW

Description of Work

DER

Distributed Energy Resources

DR DSO

Demand Response Distribution System Operator

EC

European Commission

EU

European Union

EV

Electric Vehicles

EVSE EVSEO EVSP GA

Electric Vehicle Supply Equipment EVSE operator Electric Vehicle Service Provider General assembly

GHG

Green house gases

HEC

Home Energy Controller

KPI

Key performance indicator

LV

Low Voltage

MB

Management Board

MV

Medium Voltage

NTC

net transfer capacity

OPEX

OPerational EXpenditure

PC

Project Coordinator

QA

Quality Assurance

QAP

Quality Assurance Plan

QAS

Quality Assurance System


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QM QMO

Quality Manager Quality Management Office

QO

Quality Objective

QoS

Quality of supply

O/D

Origin /Destination

SAIDI

System Average Interruption Duration Index

RES

Renewable Energy Source

SAIFI

System Average Interruption Frequency Index

SG-CG TB

Smart Grid Coordination Group Technical Board

TFO

Tranformer

TM

Technical Manager

TOTEX

TOTal Expenditure

ToU

Time of Use (tariff)

TSO

Transmission System Operator

UC UCMR

Use Case Use Case Management Repository

V2G

Vehicle to Grid

V2H

Vehicle to Home

VRDT

Voltage Regulated Distribution Transformer

WP

Workpackage

WPL

Workpackage leader


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1. Introduction The scope of this document is to identify the exact use cases for the validation of operational methods. The goal is to cover all the proposed scenarios and use cases from WP 2/3 in the four test beds at the participating DSOs. This includes description end classification of the test bed grids like, location, urban/rural, number of households, installed renewable generation, charge poles EVs etc.. The structure of this document is as follows: It starts with a use cases analysis for the test beds in chapter 2. In this chapter firstly the use cases to be tested are introduced. The format selected for the description of the use cases is based on the example found in [10], in order to keep coherence with the current developments carried out by the Smart Grid Coordination Group at European level. For all the test beds a link to the appropriate scenarios and a detailed description are given and summarized in a table of attributes. This is followed in chapter 3 by a first brief description of the test bed facilities at the different DSO sites. Besides the location key figures for the grids like no. of households, population, etc., are described. Also an EV usage scheme for the future is estimated for the test beds. An overview on the weather data is provided. Afterwards the KPIs to compare the outcome of the use cases are described. Chapters 4 to 7 then give a detailed description of each of the test beds. The content of these chapters is as follows. Firstly a detailed overview about each of the test beds is given. Following, a description of the process and the different actors involved, Business objects, system interfaces and further information about the test bed locations as well as the project time line for each test bed. Whenever available, first tests and results for the evaluation of the text are presented. Finally the future scenarios are described in chapter 8 and a conclusion is given in chapter 9.


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2.

Use cases analysis for the test beds

2.1. Introduction The operational methods developed in the project will be validated taking different use cases (UC) into account. The development of use cases is Task 3.2 in WP3 of PlanGridEV and will be further described in D3.2. To show the feasibility of the use cases particular use cases have been selected to be tested in real test beds. For instance use case 1 - Load management through network configuration and EV/DER on/off control - will be tested in Portugals test best (see Table 6). The use cases selected are those which can be proved in test beds today with the available components (e.g. charge poles, electric vehicles). The target is to put scenarios developed in Task 2.1 and the smart charging services described in D2.2 in the context of the selected use cases in the DSOs test beds. This is to show, which of the use cases and scenarios can already be implemented on the basis of existing equipment. Use cases that relay on equipment not available (e.g. V2G) are not analysed in the test beds. The following table shows the scenarios considered within the project to describe charge management options (check D2.1. for additional information). Table 4 ‐ PlanGridEV scenarios Conventional No

Safe Soft, fleet-focused

Proactive Massive

None

On/off

On/off

Yes Yes

Yes Minimal

Minimal No

No No

None

Grid  EV

Grid  EV

Grid () EV


None


Remuneration scheme

None

ToU


Competitive market

Centralised None None None None

Centralised Centralised None None None

Centralised Decentralised None None None

Centralised Decentralised Decentralised Decentralised Decentralised

Charge management Type of charge management Expected grid reinforcements Non EV-related EV-related Energy flow in EVs that are used to provide services

Type of power flow control for Emergency constraint mgt. Forecasted constraint mgt. Ancillary services for the TSO Energy trade DER integration

Smart grid Massive, local Charge modulation

EVSP

D2.2. document introduced test beds and they are further described in chapter 4-7. Below, based on D2.2, the use cases most suitable for the test beds are introduced: 

Portugal: EDP will try to demonstrate that, in some locations, it is possible to postpone grid investments by simply operating switches remotely. DER generation, EV charge and customer


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 



data will be used as input for network reconfiguration (meshed networks) through either a static daily configuration or a dynamic configuration within the day (use case 1 in Table 6). Ireland: ESB will study through monitoring the effect (mainly on voltage) of the introduction of EVs on a typical single-phase rural network. (use case 2 in Table 6). Italy: ENEL will evaluate smart charging based on constraints by the final user (time and energy) and the DSO (power load curve). The EVSE operation system finds a trade-off between both requirements considering local DER, controllable loads and stationary storage system availability (use case 3, 4 in Table 6). Germany: newly installed smart grid components will be used by RWE in order to demonstrate network operation and planning optimization. Remotely controllable assets include: transformers (through tap changers), battery storage, LV switches (able to connect two branches resulting in a closed ring topology), public and home EV charging stations, home appliances (trough Home Energy Controllers). State estimation is calculated every 60 seconds and forecast (demand and supply) for the next 24 hours is determined and constantly revised (use case 5 in Table 6).

In addition, D2.2 presented new services supported by EVs that could be introduced in a safe, proactive and smart grid scenario since they all have a charge management. These services are listed below, from 1 to 9: 1. Frequency regulation: secondary/tertiary. Requested by the TSO. 2. Voltage regulation: EVs are operated at low power rate to be able to increase demand (EVs as controllable passive loads). Requested by the DSO. 3. Planned DR: Load management according to long term minimization of electricity grid investments: customer preferences are traded with power availability at LV level. The DSO sets a target demand load curve in a Load Area. DSO/TSO requests this service. 4. Planned DR: Load management for fleets: The fleet operator sets a target demand load curve for his/her EVs in order to reduce the electricity bill, increase DER usage, or both. The service is requested by fleet operators, VPP operators... 5. Planned DR: Load management due to electricity market price: the target load curve definition is generated following an electricity price signal (ToU tariff, for example). The service is requested by EV users. 6. Planned DR: Enhanced RES integration: EV charging processes are planned in accordance with the forecasted availability of RESs/DERs in order to increase the hosting capacity of the network. The service is requested by DSO or DER operators. 7. Quasi Real Time DR: Enhance RES integration: Like the previous one but actions are planned with a few minutes / seconds notice in this domain. Either DSOs or final users (house with DER) could request this service. 8. Quasi Real Time DR: Load Balancing: This service is the short-term version of services no. 3, 4 and 5 with tight timing constraints, depending on the driving force generating the load curve (DSO, electricity price, or both of them). Service buyer depends on the specific scenario. 9. V2H: quasi real time DR domain. Objectives of this service could be the minimization of power back-up from the electricity grid, maximization of production coming from household/building RES installations, exploitation of ToU tariffs, using the EV as storage, etc. The service is


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requested by the final user. (In theory, services from 1 to 8 could be enhanced by the use of V2G technology). From the 9 services defined in D2.2 the test beds are capable of demonstrating all services besides services 1 and 5 as shown in Table 5. Service 1 cannot be covered because there is no process that covers frequency regulation from DSOs in the regulatory framework. Therefore this cannot be implemented in real distribution grid with customers. Service 5 cannot be demonstrated because there is no real time electricity market for end customers. For service 9 V2H only charging of vehicles driven from home or grid parameters is taken into account since there are no cars available in Europe which are capable of feeding energy back into the grid (There is a NISSAN approach for this in Japan, but this is not available for testing in PlanGridEV.). Table 5 ‐ Test bed relation with scenarios and services Test Bed Portugal Ireland Italy Germany

Scenario Conventional-Safe (EV and DER on/off control possible but no reinforcements expected, no EVSEO, no ToU tariffs) Conventional-Safe (charging at nights: kind of toU tariff) Smart Grid - Proactive (no V2G, no competitive market) Safe - Proactive - Smart Grid (no V2G, no competitive market, centralised power control, provider of the service is the DSO, charge modulation done by the HEC)

Main Services 3 2, 3, 4 3, 6, 7, 8 2, 6, 7, 9

2.2. The use cases to be test bed Following test bed descriptions, use cases fitting each of them were selected. The next table introduces the names given to them, while in section 2.4 below their content is further developed. Five use cases have been considered, one per test bed with the exception of the Italian case, since it was deemed more appropriate to define it through two related, but separated, cases. Table 6 ‐ Use cases describing test beds Use Case no. 1 2 3 4 5

Title Load management through network configuration and EV/DER on/off control Off-line monitoring and EV demand related risk identification EV smart charging (optimum schedule definition) EV smart charging (real-time control) Real time optimization of the distribution network operation

Test Bed Portugal Ireland Italy Italy Germany

2.3. The KPIs to judge the outcome of the use cases Task 1.3 gave as result, among others, a selection of project specific KPIs (use D1.3. report as reference) which will be used to quantify the quality of a specific solution / test (see Table 7). These KPIs are aligned with project objectives and they will be validated throughout the project period, which means that new versions of this set might be presented in the future. Find below the list of 16 project KPIs:


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Table 7 ‐ Key performance indicators KPI

KPI-Name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Quantified reduction of Carbone-dioxide emission Hosting capacity for distributed energy resources in distribution grids Share of electrical energy produced by renewable sources Measured satisfaction of grid users for the grid services they receive Duration and frequency of interruptions per customer Voltage quality performance of electricity grids Level of losses in distribution networks Percentage utilization of electricity grid elements Availability of network components and its impact on network performances Actual availability of network capacity with respect its standard value Societal benefit/cost ratio of a proposed infrastructure investment Overall welfare increase Negative impact on consumer Minimum amount of investment Activation of flexibility (DR, DG or other distributed controls) Duration of flexibility usage Portugal

The following Table 8 shows the KPIs that could be especially relevant for the evaluation of the five use cases introduced in Table 6 - Use cases describing test beds. Because they have already been selected considering project objectives, most of them are applicable to test beds. The KPI 9, 10, 11 are not evaluated in WP5 since they are not related to the test cases evaluated in the test beds. They will be used in WP 6 and 7. The Irish case, which is based in monitoring more than in a new solution implementation, is a kind of exception to this statement. KPIs related to end-users performance have been linked to use cases considering the interaction with these actors. Table 8 ‐ Use case related KPIs Use Case 1 2 3 4 5

KPI 1 X X X X

2 X X X X X

3 X X X X X

4

X X X

5 X X X X X

6 X X X X X

7 X

8 X

X X X

X X X


9

10

11

12 X

13

14 X

15 X

16 X

X X X

X X

X X X

X X X

X X X

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2.4. Use cases in the test beds The present section describes the 5 selected use cases from Table 6 in relation to the four test beds giving the details in a standardized format. The format selected for the description of the use cases is based on the example found in [10], in order to keep coherence with the current developments carried out by the Smart Grid Coordination Group at European level. Coherence with the use cases collected in the Use Case Management Repository (UCMR) has also been checked and sought, however, this was more difficult to achieve.

2.4.1. Portugal This test bed is linked to the Conventional scenario when switching is used as only means for network operation and it is closer to the Safe scenario when on/off type control over EV and DER is performed. However, unlike for the latter, no grid reinforcements are expected, no EVSE operator (EVSEO) is involved and no ToU tariff is foreseen. In order to show a most interesting case, on/off EV/DER control is considered in the description presented in the following table.


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Table 9 ‐ Use case no. 1: Load management through network configuration and EV/DER on/off control Use Case no. 1: Load management through network configuration and EV/DER on/off control The DSO analyses demand and DER generation forecasts for the following day and chooses the optimum configuration of the LV network. If, during the day, demand goes beyond expectations, two options arise (2 scenarios): network re-configuration and/or EV/DER control. The best option among them should be chosen according to economic (losses, payment to customers if required) and Definition of the Use Case (scope, objective, technical (supply interruptions....) aspects. In this use case EV/DER control is considered. Business case) Network reconfiguration does not require any interaction with external actors. EV/DER control is based upon on/off signals sent by the DSO (presumably, some information should be sent also to the owners in order to explain the control action). The response of EV/DER is of high importance. In a conventional scenario, EVSEO and DER operators might be obliged through regulation to respond to DSO requirements or allow for direct control. This assures a high demand response level.

Diagram of the Use Case (interaction between system and external actors)

Technical Details (actor and system description)


DSO load forecasting tool: applications running in DSO backend system forecasting load and generation from different inputs such as weather forecast, demand historic data, DER generation forecast... (forecast data). Network analysis software (SW): software permitting an off-line simulated performance of the network (power flow analysis, for example). Network control system (SCADA): it provides the DMS the communication with the substations and other assets to monitor and control the grid.

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No.

Name

1

Load and forecasting

generation

(L&G)

2

Network analysis

3

Controlling the grid

4

Monitoring the distribution grid

5

Controlling load and generation (L&G)

Scenario no.

Step no.

Name

1

1

Obtaining weather forecast and L&G schedule

1

2

L&G forecasting

2

1

Power optimisation

2

2

3

1

flow

Network configuration selection Network status

Distribution assets: controllable assets in the network (switches, transformer tap changers...), controllable generators (DER) and controllable loads (EV, home appliances...). Data base: it contains, among others, the historical data of network performance. Scenario conditions Primary Actor Triggering Event Pre-condition Post-condition Historical data, weather forecasts, DER Load and generation for the DSO load forecasting tool Daily analysis operator forecast... is available and following day are forecasted (backend system) applications for the analysis Load and generation data for the next The optimum network configuration Network analysis SW Daily analysis day is forecasted is obtained for operation Network control system Network analysis Network analysis is performed. Topology Network topology is modified (SCADA) results needs to be modified through switching Metering devices and communications Real time network parameters are SCADA system Real time monitoring are working and correctly configured known Alarms related to network parameter Network parameter/s behaviour are transmitted and identified Load/generation control permits to SCADA system go out boundaries Load/generation control is possible (EV, stabilise network parameters DER...) Scenario: Steps Information Information Requirement Description Service Information from to exchanged ID Meteo services, Weather, load and generation forecast Get DER operators, DSO External input data F-1 are received/obtained EVSEO... Load & gen. input Based on external and historical data, DSO forecast for the forecast load and generation are forecasted for Execute DSO data bases application application the next day Using the demand and generation DSO forecast Network Load & gen. for forecasts for the next day, power flows Execute application analysis SW the next day are run in order to select the optimized network configuration Based on the power flow analysis the Network analysis Network Network topology best network configuration is chosen Report SW analysis SW for the following day The optimized topology is checked Execute Network analysis DSO SCADA Network topology


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monitoring

with the current status to identify SW system required actions If the network topology needs to be modified according to the operation DSO control Network Control signal to C-1 3 2 Grid asset control criterion of the DSO, this is Execute system assets assets accomplished using the SCADA system The grid is constantly monitored to DSO Monitoring Network state and Network sensors, check any deviation of the most M-1 monitoring Report 4 1 distribution grid magnitude devices... relevant parameters (voltage, current, system parameters temperature...) When a magnitude goes out permitted DSO control DSO operation Alert 4 2 Alert identification ranges, an alarm should be generated Report system HMI by the control system If required, on/off signals are sent to Load & Control signal to DSO control Load and load & generation assets in the C-2 generation Execute 5 1 flexibility provider system generation control involved areas, directly or through control system aggregators/operators Load/generation control observance Metered data Performance must be assessed when direct Report Metered data DSO 5 2 during load control monitoring operation is not possible (control through aggregator/operator) Step: Information exchanged Name Description Requirements to information data In the case of DER operators and electricity consumers, data may be sent in a format specified by the DSO, including information on the asset Data coming from external actors to DSOs (weather services, DER (leading to a contract number and a network area) and energy/capacity on External input data operators, electricity consumers....) the generation/demand (per period, e.g., quarter of an hour). Weather data might include forecasted temperature, wind speed, irradiation, etc. External input and historical data (from DSO data bases) are used Data should have the format required by the application giving the load & Load & generation input the as input to define load and generation forecast profiles for the next generation profiles for the next day. Capacity and energy per time period forecast application days. should be defined. Load & generation for the next Load and generation profiles for the next day for each of the Load and generation forecast data should have the format required by the day distribution network areas. network analysis software to be used as input including period of the day


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Network topology

Control signal

Network state and magnitudes

Alert

Control signal provider

to

Metered control

during

ID

data

flexibility

Category

F-1

Forecast

C-1 M-1

Control Monitoring

C-2

Control

load

and capacity/energy. Network configuration description indicating the asset state The network configuration is in the format used by the DSO to perform offmodification required to move from the current topology to that line simulations. selected for the next day The SCADA system sends control signals to those switches that Open/close or similar signals will meet asset configuration, SCADA system need to be opened or closed to permit an optimised power flow requirements and DSO communications standards. and network performance Network parameter sensors and devices of the network with Communications should be compliant with DSO used standards and communication capabilities send their metered magnitudes and procedures. state to the DSO front-end systems. Alerts, meaning a network operation risk, are generated when the These alerts (referred to network magnitudes: V, I, T...) might be a call for monitoring system identifies a magnitude close to the permitted operators to act manually or a trigger for an automatic response of the limits control system to guarantee a secure operation. When load control is deemed a better option compared to network On/off control signals must reach their destination and be effective. If re-configuration, a signal is sent to load and generation assets, communication is done through an aggregator, this should guarantee that directly or through their operators, in order to operate the network the control signal will have effect in the agreed time. under secure limits. When the asset operation is in charge of EVSEO, DER operator, Metered data (load or generation, kWh during a period) is checked for the VPP operator or any other type of load/generation aggregator the control periods. This could be done manually or using a dedicated DSO should check if control orders were really observed. application. Step: Requirement Description Off-line forecast data acquisition. Proprietary or standardized protocols may exist for information exchange (like WXMM, WMO, METCE for weather...) or proprietary solutions defined by the service provider. Web services, file exchange, web sites might be the communication means. The communication of control signals should conform to the standards used by the DSO (IEC 61850, IEC 60870...) The communication of metered and state data should conform to the standards used by the DSO (IEC 61850, IEC 60870...) The communications between utilities and load or generation operators should be defined. Normally, this will be set by the DSO and the customers connected to its network will have to comply with the requested communication requirements.


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2.4.2. Ireland The Irish test bed is related to monitoring and the later analysis of the resulting data. The demonstration will be used to evaluate the scenarios “conventional” and “safe”. The tests that can be carried out are:    

No Electric Vehicles Connected Electric Vehicles deployed but charging restricted Electric vehicles deployed with unrestricted charge times Intelligent charging


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Table 10 ‐ Use case no. 2: Off-line monitoring and EV demand related risk identification Use Case no.2: Off-line monitoring and EV demand related risk identification Off-line monitoring of network parameters to assess EV impact on rural LV networks. Three scenarios will be studied: no EVs connected, EVs connected with charging permitted at night (between 11pm and 7am); Definition of the Use Case (scope, objective, and EVs connected with unlimited 24 hour charging. Business case) No new communication means are involved (AMI communications, file exchange... are involved). Alarms are generated when values go out of permitted ranges. The data defining these situations should help plan network development and define beneficial technologies and strategies.


DSO/metering operator front-end systems: systems permitting the remote data retrieval from the metering devices in the network. Network analysis software (SW): software permitting an off-line simulated performance of the network (power flow analysis, for example). Technical Details (actor and system description) Monitoring devices: they are in charge of acquiring operation data from the distribution network (mainly voltage and current). AMI systems are included for customer demand information recording. DSO planner: according to the metered data, he/she extracts conclusions on EV impact affecting future network planning decisions. Scenario conditions No. Name Primary Actor Triggering Event Pre-condition Post-condition


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1

Monitoring the distribution grid

2

Network analysis (historical data)

Scenario no.

Step no.

1

1

2

1

Name Monitored data Network state

DSO/metering operator front-end system Network analysis SW

Metering campaign is over

Metering devices and communications are working and correctly configured

Metering campaign is Monitoring data is over metering devices Scenario: Steps Information Name Description Service from Monitoring Data from AMI, metering Metering distribution grid transformers, etc. is retrieved Get devices parameters remotely Network Historical data Monitoring data is used for Execute analysis SW analysis network impact analysis Step: Information exchanged Description Monitored data may be referred either to network parameters and/or customer demand (including EV charge consumption). They come from metering devices such as instrument transformers and AMI systems

Network parameters resulting from off-line simulations

retrieved

from

Information to

Main network parameters are known (voltage and current) EV impact on the network is assessed

Information exchanged

DSO front-end system

Monitored data

Network planners

Network state

Category Monitoring

Description The communication of metered and state data should conform to the standards used by the DSO (IEC 61850, IEC 60870...)


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M-1

Requirements to information data Network voltage and current will be metered. Energy and capacity of the end-user will also be retrieved for the analysed time periods Monitored data should be introduced with the required format in the network analysis SW

Step: Requirement ID M-1

Requirement ID


2.4.3. Italy The Italian test bed is mainly linked to the Smart grid scenario but it does not fulfil all the characteristics defining it, since V2G technology is not used, there is no competitive market and some power flow controls are centralised instead of decentralised. These two aspects coincide with the characteristics of the Proactive scenario, while the rests suit the Smart grid one. The test bed is described by two different use cases for more clarity. One is referred to the EV charging schedule definition and the other to the revision of this schedule after a change in grid conditions. Unlike the previous use cases, the next two consider the EVSEO role as the main one, even if it could be played by different actors, such as the DSO (in Italy, for example, this seems to be the situation). For this reason, demand target calculation scenarios (linked to DSOs in use cases 1 and 2) are not detailed here. Please, check scenarios 1 and 2 in Use Case 1 for further information in this regard.


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Table 11 ‐ Use case no. 3: EV smart charging through optimum schedule definition Use Case no. 3: EV smart charging (through optimum schedule definition) The EVSEO (it could be also the EVSP) collects the target demand for an area set by the DSO. The DSO creates the target demand curve after consideration of EVSE availability and DER generation forecast. From the EVSP, the EVSEO receives EV Definition of the Use Case (scope, objective, user's preferences, which could be sent directly to the EVSP or through the EVSE (depending on the available ICT Business case) infrastructure). From all these inputs, the EVSEO obtains an optimum charging schedule for EVs and controls EV charging process. EV user preferences are respected unless an emergency occurs.




DSO DMS: the DSO control system or an off-line network analysis system, defines an optimum load target for an area from forecasted and monitored data. This target is sent or made public by the DSO. EVSP: it holds a contractual relationship with the end-user. It knows his/her contractual commitments and receives information about his/her charging preferences. Front-end and backend systems are used for communications and data preparation. EVSEO: it owns, controls and maintains the charging infrastructure (EVSE). In this use case, it defines the charging schedule of EVs connected to his infrastructure (backend application), after considering both EV user preferences and DSO requirements. The charging settings are communicated to the EVSEs and, from these, to EVs.

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Scenario conditions Triggering Event Pre-condition Post-condition The DSO has an updated load and The DSO creates an optimum demand Change of demand target in generation forecast and monitors the 1 Obtaining load schedule DSO DMS target in an area and communicates it a load area network for each area The EVSP has EV user charge The EVSP sends EV user preferences to Connection to the EVSE, Obtaining EV user preferences (contractual and/or specific for the EVSEO upon request or EVSP 2 start of charging preferences the current charge) automatically EV is connected to the EVSE. Information EV is charged according to optimum 3 Controlling EV charge EVSEO backend EV connection to an EVSE from the network and/or EVSP is available schedule Scenario: Steps Scenario Step Information Information Information Name Description Service Requirement ID no. no. from to exchanged Periodically or when updates occur Demand target for a DSO provides load S-1 the DSO shares a demand target to Get DSO DSM EVSEO 1 1 network area schedule EVSEOs EVSP EV user's charge EVSP sends EV user charge S-2 Get EVSEP EVSEO 2 1 communicates endpreferences preferences to the EVSEO user preferences The EVSEO creates the charge EVSEO data EVSEO Schedule EV charge schedule schedule for each EV connected at Create 3 1 base control system optimization input definition its infrastructure EVSEO control system sends the Execute EVSEO EVSE/EV Control signal C-1 3 2 EV charge control charging schedule to the EVSE/EV The EVSEO sends the new EV charge schedule forecasted demand profile to the Report EVSEO DSO Demand forecast F-1 3 3 communication DSO Step: Information exchanged Name Description Requirements to information data The DSO sends/publishes the optimum demand for the following hours (24?) for a load area, obtained from network conditions; weather, load and generation forecast; etc. At least time period and limit capacity (as percentage of DSO's demand target The EVSEO will have to translate area demand target to its own demand (capacity % contracted power, for example) should be indicated. for example) EV user's charge EV user charging preferences may come from both contractual data (general aspects) Preferences may include: charge start time, charge end preferences and more specific data on each particular charging process. The EVSP will have to time, minimum SOC after charge, V2G capacity and No.

Name

Primary Actor


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consider all these and communicate user's preferences to the EVSEO.

Schedule input

optimization

Control signal Demand forecast

ID

Category

S-1

Setting

S-2

Setting

C-1

Control

F-1

Forecast

storage service availability, maximum energy price, minimum SOC if DR is used and energy price is reduced below X, etc.

DSO target and EV user preferences must be transformed to the format of the optimization SW. EV user preferences act as boundary conditions for charging schedule definition Charging schedule for the EV during the initiated charging process. It is communicated to the EVSE and EV The charging schedule for the EV is communicated to the DSO

The optimization SW defines the input data format The power (or current) limit is defined for each charging period The foreseen demand power is defined for each charging period

Step: Requirement Description The information should be sent/published in an established format, both from the information and communication points of view. It could be an offline file, an automated process (web service, for example), etc. It could have the same format defined by protocols such as the ISO/IEC 15118 or OCPP to facilitate interoperability and end-to end communications. Same as S-1 above. EV user charging process specific requirements might reach the EVSP directly from a smart phone or from the EV controller; or through the EVSEO from the EVSE HMI or EV (ISO/IEC 15118 necessary) Communications between EVSEO and EVSE could follow OCPP v2 standard (the whole schedule can be sent for the following periods) or previous versions (single power limit settings should be sent at each period start). From the EVSE to the EV, IEC 61851-1 standard may apply (PWM signal though pilot wire). In order to permit negotiation directly with the EV, the ISO/IEC 15118 protocol should be implemented but, in this use case, preferences come from the EVSP See S-1 description

Table 12 ‐ Use Case no.4: EV charging schedule modification based on real time control

Definition of the Use Case (scope, objective, Business case)


Use Case no.4: EV smart charging (real-time control) Once the EV is charging, RES availability or EV demand forecasts may change during the process. These variations are identified and charging schedules are modified accordingly. In addition, both DER and EVSE operators should provide real time data information on consumption and generation to the DSO. Taking this into consideration, the DSO will provide a new target demand curve to the EVSEO whenever needed. This new information together with consumers' preferences will result in new EV charging schedules. The objective is to increase RES hosting capacity as much as possible. DER/EV traceability is required to provide optimum solutions at POD (Point of Delivery) level: electrical vicinity between RES and EVSEs should be known. EV user preferences are respected unless an emergency occurs.

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DSO DMS: the DSO control system or an off-line network analysis system, defines an optimum load target for an area from forecasted and monitored data. This target is sent or made public by the DSO. EVSEO: it owns, controls and maintains the charging infrastructure (EVSE). In this use case, it defines the charging schedule Technical Details (actor and system description) of EVs connected to his infrastructure (backend application), after considering both EV user preferences and DSO requirements. The charging settings are communicated to the EVSEs and, from these, to EVs. Data base: it contains the EV user preferences data received from the EVSP at the start of the charging process. Scenario conditions No. Name Primary Actor Triggering Event Pre-condition Post-condition The EV is charging. DSO communicates a new RES generation forecast DER and EV/EVSE traceability is 1 Obtaining new load schedule DSO DMS demand target to the EVSEO changes in an area required EV is charging. Communications with Receipt of updated load the DSO are available. A new charging schedule is sent 2 Controlling EV charge EVSEO backend target curve EV user preferences for the charge are to affected EVSE/EVs stored and available Scenario: Steps Scenario Step Name Description Service Information Information to Information Requirement ID


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no.

no.

from

1

1

DSO provides new load schedule

2

1

EV charge schedule definition

2

2

EV charge control

2

3

EV charge schedule communication

Name DSO's demand target

Schedule optimization input Control signal Demand forecast

ID

When grid conditions change (event, forecast...), the DSO defines and share a new demand target for an area The EVSEO creates the new charge schedule for the EVs connected at its infrastructure EVSEO control system sends the charging schedule to the EVSE/EV The EVSEO sends the new forecasted demand profile to the DSO

Get

DSO DSM

EVSEO

Create

EVSEO base, DMS

EVSEO system

Report

EVSEO

EVSE/EV

Control signal

C-1

Report

EVSEO

DSO

Demand forecast

F-1

data DSO

Step: Information exchanged Description The DSO sends/publishes the optimum demand for the following hours (24?) for a load area, obtained from network conditions; weather, load and generation forecast; etc. The EVSEO will have to translate area demand target to its own demand (capacity % for example) DSO target and EV user preferences must be transformed to the format of the optimization SW. EV user preferences act as boundary conditions for charging schedule definition Charging schedule for the EV during the initiated charging process. It is communicated to the EVSE and EV The charging schedule for the EV is communicated to the DSO

Category

S-1

Setting

C-1

Control

exchanged New demand target fora network area control

S-1

Schedule optimization input

Requirements to information data At least time period and limit capacity (as percentage of contracted power, for example) should be indicated.

The optimization SW defines the input data format The power (or current) limit is defined for each charging period The foreseen demand power is defined for each charging period

Step: Requirement Description The information should be sent/published in an established format, both from the information and communication points of view. It could be an offline file, an automated process (web service, for example)... It could have the same format defined by protocols such as the ISO/IEC 15118 or OCPP to facilitate interoperability and end-to end communications. Communications between EVSEO and EVSE could follow OCPP v2 standard (the whole schedule can be sent just once) or previous versions (power limit settings should be sent at each period start according to forecasts). From the EVSE to the EV, IEC 61851-1 standard may apply (PWM signal though pilot wire). In order to permit negotiation directly with the EV, the ISO/IEC 15118 protocol should be implemented but, in this use case, preferences come from the EVSP


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2.4.4. Germany The test bed in Germany is mostly linked to the Proactive and the Smart Grid scenarios as defined in PlanGridEV, even if smart grid technologies are widely deployed in the demonstration. The main characteristic of the Smart Grid scenario is the charge modulation and DER integration, which is really provided by Home Energy Controllers (HEC). Most of the other aspects (e.g. Provider of service, remuneration scheme) are more correlated with the proactive scenario. The DSO is the responsible of sending control orders to network assets and of verifying the schedules prepared by HECs. This control orders will by normally of the on/off, open/close or x% of nominal load type. The use case is further described in the following table.


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Table 13 ‐ Use case no.5: Real time optimization of the distribution network operation Use Case no.5: Real time optimization of the distribution network operation Demand and generation forecast for the following 24 hours is determined and constantly revised by the DSO. A state estimation is calculated every 60 seconds. Following LV network conditions, the DSO controls assets including transformers, switches, public and private EVSE or HEC (Home Energy Controllers) in order to maintain grid parameters within limits. The type of communication depends on the asset. Definition of the Use Case (scope, objective, HECs send user preferred demand profiles to the DSO and this chooses among existing options. In emergency situations Business case) incentives and control signals could be sent from the DSO to the HEC (possible implications on retailer's commitments with the market should be checked). Network devices will receive switching commands (on/off, open/close) from the DSO. Decisions are taken by the DSO who is the main actor. End users at home show their preferences through HEC settings or direct switching actions (including overriding). No direct communication between final users and DSOs is considered.




Smart operator: DSO DMS controlling the LV grid and trying to optimize the efficiency of the operation. It performs the network analysis and selects among the best switching actions. Load and generation forecasting tool: applications running in DSO backend system forecasting load and generation from different inputs such as weather forecast, demand historic data, DER generation forecast... Grid estimation: application running in the DSO backend that estimates the current state of the network from available metered values Monitoring devices: they are in charge of acquiring operational data from the distribution network (mainly voltage and

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current). AMI systems are included for customer demand information recording. Network control system (SCADA): it provides the DMS the communication with the substations and other assets to monitor and control the grid DSO Data bases: they store the historical data coming from monitoring devices. This data is used for load and generation forecast. HEC (Home Energy Controller): it optimizes the energy demand and generation within home boundaries. It controls assets in customer premises. It can communicate with the DSO either directly or through an aggregator. Network assets: controllable assets in the network (switches, transformer tap changers...). (DSO/metering operator front-end systems: systems permitting the remote data retrieval from the metering devices in the network) Scenario conditions No. Name Primary Actor Triggering Event Pre-condition Post-condition Metering devices and communications Metered network data is available for Monitoring the distribution SCADA system Real time monitoring 1 are working and correctly configured operators grid The grid conditions that cannot be metered Network analysis Done periodically (every Network metered values are available are estimated. Grid parameters are 2 Grid state estimation SW 60 seconds) and introduced in the software calculated Expected weather conditions are known, Expected grid conditions for the next 24 Load and generation Forecast Checked continuously demand and generation historical data is 3 hours are calculated forecasting application available, info from HEC is received Smart operator State estimation and Network current state and forecasts are Actions leading to an improvement of quality 4 Network analysis SW forecast changes available of supply are identified DMS / SCADA According to A network analysis and optimization has Switching signals are sent to controllable 5 Controlling the grid system optimization results been performed grid assets and HEC After network Switching actions have been taken in the The success of the action is determined and 6 Performance monitoring Smart operator reconfiguration distribution network considered by the system Scenario: Steps Scenario Step Information Information Name Description Service Information to Requirement ID no. no. from exchanged The grid is constantly monitored AMI, grid DSO monitoring Monitoring the Network magnitudes M-1 through smart meters, dedicated Report metering 1 1 system distribution grid metering devices in the grid... devices... DSO Grid state Grid state Grid state is determined based on Execute monitoring analysis Network magnitudes 2 1 estimation metered values system application


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3

1

Obtaining weather forecast and Load &Gen. schedule

Grid current state, historical data, weather and customer demand (HEC) forecast information must be retrieved

Get

Data bases, external servers, HEC

Demand forecasting tool

Weather data, demand and generation data

F-1

Data base, Grid conditions for the next 24 hours grid state Grid conditions Demand and are calculated from current state, Execute 3 2 analysis forecasting tool generation data historic data and expected weather application conditions From the state of the grid analysis, Grid state Control SW Power flow Network magnitudes decisions to improve the quality of Execute analysis 4 1 (smart operator) optimisation supply are taken application Grid asset Switching signals are sent to grid Execute DMS Network assets Control signals C-1 5 1 control controllable assets If needed, emergency signals are sent Execute DMS HEC Control signals C-2 5 2 End-user control to HEC devices Grid metering DSO monitoring Monitoring the Grid behaviour after re-configuration is Network magnitudes M-1 Report 6 1 devices system distribution grid checked If changes are successful the DSO Performance algorithm assigns a higher weight to Execute monitoring Smart operator Value assignment 6 2 rating the switching option system Step: Information exchanged Name Description Requirements to information data Main parameters of the distribution network (voltage, current...), All parameters helping to understand network condition: voltage, Network magnitudes state signals (switches configuration, tap changers...) and other current, storage level, switches state, tap changers position, (temperature, for example) temperature... Forecasted weather data for the next hours, which impacts on Impacting on demand: temperature Weather data demand and DER generation Impacting on generation: irradiation, temperature, wind speed Forecasted demand and generation in an area based on weather Energy demanded or generated in a period, maximum and average Demand and generation data forecast and historical data. HEC might also send forecasted values residential consumptions to the DSO. Signals sent remotely to controllable assets of the network in order Network assets: On/Off or Open/Close signals Control signals to change the topology or performance of the latter, aiming at power HEC systems: incentives (price signals) or on/off signals quality improvement The Smart Operator assigns a weight to network asset switching Established by the Smart Operator algorithm Value assignment options depending on their success. Higher weight when actions are Load and generation forecasting


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successful Step: Requirement ID M-1

Category Monitoring

F-1

Forecast

C-1 C-2

Control Control

Description The communication of metered and state data should conform to the standards used by the DSO (IEC 61850, IEC 60870...) Proprietary or standardized protocols may exist for information exchange (like WXMM, WMO, and METCE for weather...). Web services, file exchange, web sites might be the communication means. Regarding load and generation data, the information should be sent from the HEC to the DSO in a pre-defined format. The communication of control signals should conform to the standards used by the DSO (IEC 61850, IEC 60870...) The communication with the HEC signals should conform to the standards used by the DSO (Open ADR - IEC 62746-, for example)


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3. Overview of the test beds grids The test beds of the DSOs involved in the project PlanGridEV are selected from each specific DSO within their own requirements. By this reason, the test beds are scattered all over Europe (Figure 2 shows the approximated geographical location of the four DSO test beds). They cover different climate zones. Besides this, the test beds differ also in type of grids and voltage levels. Medium and low voltage grids are represented, as well as rural and urban topologies. Only “island” grids are not directly covered by any of the DSOs test beds. The assessment of common grid topologies is more relevant than the assessment of networks operated in an island. In the European context, island grids take a very minor share in European electricity networks and are used to be avoided in current grid operation practice. However, the chosen approach for the gap analysis in WP1, calculating the EV and PV hosting capacity of the considered grids, also applies when these grids are operated as an island. Only in this case the transformer would be the limiting factor to the hosting capacity, operating the grid as an island could increase the hosting capacity temporarily, but in grid connected mode, the local production would have to be reduced to avoid critical loading of this transformer.

Figure 2 ‐ Overview of the DSO test bed grids2 Based on currently available statistical information, the following section provides an overview of relevant data for each test bed. This information will be used as basis for the development of future scenarios for these networks.

2

Source: www.plangridev.eu


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3.1. Demographical data of the specific area (statistical data) Table 14 shows basic statistical data from the DSO test beds, which cover the population and household data as well as information about numbers of vehicles. Table 14 ‐ DSO Test beds statistical data

Grid Type GiS Area NUTS 2 Code Nr. Households Persons/Households Population Cars/Household Cars/1000 Persons Number of Vehicles

EDP MV/LV Urban Lisbon PT17 1238 2.4 2971 0.975 406.1 1207

DSO Test Beds population Data ENEL ESB MV/LV MV/LV Rural Urban L´Aquila Limerick ITF1 IE02 1925 4 2.8 2.73 5390 11 2.065 1.16 737.5 4255 3975 5

RWE MV/LV Rural Kisselbach DEB1/DE27 173 3.2 5654 1.85 567 320

The individual test beds cover different types of areas and differ in size. The number of persons per household varies from 2.4 to 3.2. Also the number of cars per household and cars per 1000 persons are fluctuating on a broad basis. These numbers impact the expected maximum number of EVs for a specific area.

3.2. Share of In‐/intra‐/out commuters Table 15 shows the basic statistical reference data for commuter shares for rural and urban areas. This data is derived from a large data base which represents the area of Austria.

3

Source: http://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/saveselections.asp Source: https://de.wikipedia.org/wiki/Kisselbach 5 Source: http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Transport_statistics_at_regional_level/de 4


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Table 15 ‐ Statistical commuter data6

In-Commuter OutCommuter

Min. Ave. Max. Min. Ave. Max.

Rural Urban 2% 15% 10% 61% 38% 641% 4% 5% 20% 28% 35% 40%

Table 16 shows the assumed values for commuter shares based on the area type and the information from Table 15. Table 16 ‐ Commuter Share at DSO test beds

Area Type In-commuter Share Out-Commuter Share

DSO Test Beds existing PV Systems EDP ENEL ESB RWE Urban Urban Rural Rural 61% 61% 10% 10% 28% 28% 20% 20%

According to the data in Table 16 in urban areas 28% of the local population (and cars) are out commuters, whilst 61% (also based on the number of the local population) in commuters are addressing this specific area during the day. In the case of rural areas less in commuters can be expected (in this case 10%) and around 20% of the local population is out commuting during an average day.

3.3. Customer structure / network node (DSO data) Table 17 shows the structure of customers at DSO test beds which are of relevance for the allocation of charging infrastructure.

6

Source: http://www.statistik.at/web_de/downloads/webkarto/pendlermatrix_bez_2012/index.html


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Table 17 ‐ Customer structure at DSO test beds

DSO Test Beds existing Charging infrastructure EDP ENEL ESB RWE 1238 1925 4 173 53 381 0 15 1 0 0 0 0 0 0 2 0 3 0 0

Customer Group Households Commercial Industrial/Work Agriculture Others

3.4. Local DERs, and EV charging infrastructure The following chapters list the currently existing installed DER systems and EV charging stations.

3.4.1. EV charging infrastructure Table 18 - Existing charging Infrastructure provides an overview of currently existing charging infrastructure at DSO test beds and the location type for each charging point. Table 18 ‐ Existing charging Infrastructure

DSO Test Beds existing charging infrastructure EDP ENEL ESB RWE 0 0 0 0 0 0 0 0 0 2 0 0

Charging location Home Shops Work Else

0 0 0 2

3.4.2. PV Systems Based on data from DSOs, the currently installed peak power of PV systems is shown in Table 19. Table 19 ‐ Existing PV systems at DSO test beds

EDP Installed systems Installed Peak Power [kW]

DSO Test Beds existing PV Systems ENEL ESB RWE 17 n.A. 0 14 64 972 0 183


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3.4.3. Wind turbines Currently, at none of the DSO test beds, wind turbines are installed. Because all of the DSO test beds are located in rural or urban residential areas, it is not expected to have any installed wind power in future.

3.5. Local historical weather data The generation of synthetic generation (of DERs) and load (EVs) profiles, is based on historical weather data (preferably local and measured data in high resolution). The following section provides information about the different climatic conditions at DSO test beds in the European area.

3.5.1. Temperature

Temperature

Figure 3 - Average monthly temperatures at DSO test beds shows the average monthly temperatures at DSO test beds. The temperatures vary around 4 and 8 degrees between the different test beds and month. The temperature influences the energy consumption of electric vehicles (due to on-board heating and cooling systems). 30 °C ESB 25 °C ENEL EDP 20 °C RWE 15 °C 10 °C 5 °C 0 °C Jan

Feb

Mar

Apr

May

Jun Jul Month

Aug

Sep

Oct

Nov

Dec

Figure 3 ‐ Average monthly temperatures at DSO test beds7

3.5.2. Solar Radiation Figure 4 provides an overview of the annual solar radiation at the European area and the DSO test beds. Depending on the geographical location, the average values can vary from 1200 to 2000 kWh/m².

7

Source: http://de.windfinder.com/


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Figure 4 ‐ Photovoltaic solar electricity potential in European Countries According to Figure 4 the global irradiation for each test bed area can be outlined as shown in Table 20: Table 20 ‐ Solar radiation at DSO test beds

DSO Test Beds existing PV Systems EDP ENEL ESB RWE Global annual irradiation 1900-2000 1700-1800 1200-1300 1200-1300 [kWh/m²] Annual energy generated by 1 kWp [kWh/kWp] 1400-1500 1250-1350 900-1000 900-1000 (performance 0,75)


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3.6. KPIs at DSOs test beds The role of KPIs will be crucial in terms of analysing the performance of DSO test beds in their ability to tackle the challenges that are faced across Europe. The KPIs will enable an overall assessment of all the test beds on a common platform. The challenge that will be faced is to isolate the specific benefit of the proposed solution and relate this to every member state involved in the project. Through the use of KPIs it will be possible to apply a scale to the benefits of the proposed solution and provide an overall European solution rather than to confirm an applicable solution for each individual member state. Also, it will be possible to identify the most optimum solution by identifying which test bed satisfies most KPIs and hence provides the greater societal benefit. During the work in WP1 and the KPI report in D1.3 the relevant KPIs (also at DOS test beds) were collected and identified. Table 21 provides an overview of the selected PlanGridEV project KPIs which will be used also at DSOs test beds.

Table 21 ‐ PlanGridEV Project KPIs Project Key Performance Indicators 1

Quantified reduction of carbon-dioxide emission

2

Hosting capacity for distributed energy resources in distribution grids

3

Share of electrical energy produced by renewable sources

4

Measured satisfaction of grid users for the “grid” services they receive

5

Duration and frequency of interruptions per customer [QoS indicator]

6

Voltage quality performance of electricity grids [QoS indicator]

7

Level of losses in distribution networks

8

Percentage utilization of electricity grid elements

9

Availability of network components and its impact on network performances

10 11

Actual availability of network capacity with respect to its standard value Societal benefit/cost ratio of a proposed infrastructure investment

12

Overall welfare increase

13

Negative impact on consumer

14

Minimum amount of investment

15

Activation of flexibility (DR, DG or other distributed controls)

16

Duration of flexibility usage


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4. Overview of the Portuguese (EDP) test bed Based in the Inovgrid Project, the image below shows the equipment technology used to acquire grid data implemented in the MV/LV substations and in the customer facilities that will help us to well characterise the LV grids selected for the tests.

Figure 5 ‐ EDP DTC and EDP Energy Box

The image below shows a customer load diagram obtained with Inovgrid technology to acquire grid data implemented in the MV/LV substations.

Figure 6 ‐ Customer load profile Also EV load diagrams and DER output diagrams can be obtained with the already installed technology. The validation test that will be deployed in EDP Test Bed will seek to evaluate the ability to bring in, not just the flexibility of future demand and micro-generation control (EV included), but also the flexibility of the network itself, as provided by future inexpensive remote-controlled switchgear.


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Dynamic network reconfiguration will be evaluated as a way to mitigate voltage and congestion problems as they appear during the day. The economic benefits of the proposed strategy will be determined in comparison with the traditional grid investments strategy. The ultimate objective is to obtain a set of operational decision rules in order to promote better management of LV network along with the update of the actual Operational Methods used by the DSOs.

4.1. Description of the process and of the different actors involved A new operational procedure will be validated by EDP, taking into consideration the future flexibility of the LV network operation, mainly switching. Even though the Conventional scenario defined in D2.1 is based on the traditional “build and forget” approach to cope with new or increasing electricity demand in particular with the new EV loads, EDP will try to demonstrate that in some locations it is possible to postpone grid investments by simply operating remote switches. Keeping in mind the main objectives of PlanGridEV and in particular what EDP intends to simulate, as a first approach to the main Operational Methods to be developed and applied in the EDP Test Bed, we can refer to the following needs:  ICT and Data Management that allow data access on real-time to DER generation data, EV charging data and Consumers load data, enabling better understanding/characterization the LV network;  Remote Controlled Switching in LV network and ICT to enable load transfer between LV feeders; With knowledge of the above information, having the appropriate ICT systems and the possibility to perform remote operation in the LV network, we will try to demonstrate the possibility to accommodate the different actors (generation and load) along a day performing network reconfigurations, avoiding and/or postponing grid investments. All the information obtained from the grid will be used in a second stage to develop short-term operational planning. With the consolidation of the collected information (longer period of data collection), it will be possible to develop network expansion plans in a medium term (mid-term planning rules). All the tests will be performed with the EDP software planning tool DPlan.


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Figure 7 ‐ LV grid simulation with DPlan

Figure 8 ‐ LV + DER grid simulation with DPlan Taking into account the four scenarios defined in the PlanGridEV project we can say that EDP Test Bed will mainly match the Conventional Scenario. Some minor adjustments were made to the Conventional Scenario especially to predict the possibility of grid investments in grids where the EDP proof of concept cannot be applied and also to describe


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the necessity to have information transferred from the grid (EV load diagrams, DER output diagrams and Consumer load diagrams) to the DSO. To have that information flowing (get on line data from the grid) some ICT technology is needed. These two adjustments are reflected in the following table marked in “blue”.

Table 22 ‐Summary of main characteristics of the different scenarios adapted to EDP test bed

Conventional

Safe

Proactive

Smart grid

No

Soft, fleet‐ focused

Massive

Massive, local

None

On/off

On/off

Minimal Minimal

Yes Minimal

Minimal No

Charge modulation No No

Grid  EV

Grid  EV

Grid  EV

Grid  EV


EVSE Operator


Remuneration scheme

None

ToU


Centralised None None None None None

Centralised Centralised None None None None

Charge management Type of charge management Expected grid reinforcements Non EV‐related EV‐related Energy flow in EVs that are used to provide services

Type of power flow control for8: Emergency constraint mgt. Forecasted constraint mgt. Real‐time constraint mgt. Ancillary services for the TSO Energy trade DER integration

Centralised Decentralised None None None None

EVSP Competitive market Centralised Decentralised Decentralised Decentralised Decentralised Decentralised

Following the four scenarios defined in the D2.1 and the Catalogue of Products (Services) derived from D2.2, for the Portuguese test bed Conventional Scenario, the possible services that can be provided are explained below: 





Charge Management/Type of charge management: o EV customers will be able to charge their EVs as soon as they arrive at the charging point and without any limitation on the power to be demanded. So there will be no Charge Management or Type of Charge management. Expected grid reinforcements: o It is assumed that in some cases/grids the solution could be a mix of placing some remote switching devices along with minor “copper” investments. Energy flow in EVs that are used to provide services/Provider of the service:

8

Centralised control means that the DSO is controlling the charge, while decentralised means that either the Electric Vehicle Supply Equipment (EVSE) Operator or the Electric Vehicle Service Provider (EVSP) are taking control.


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o





It is necessary to have some ICT technology only to get real time load (EV) and generation (DER) values in order to apply the decision criteria developed in the test bed (new Operational Methods). Therefore, EVs, EVSE Operators and EVSPs will not provide any other services to the DSO or other actors (TSO, electricity retailers…).

Remuneration Scheme: o In EDP Test Bed this is out of scope. The possibility to offer different tariffs as it would happen in the Proactive or Smart grid scenarios will only determine the charging behaviour of the EV customer. Type of power flow control: o Only the Emergency Constrain Mitigation is in place in this scenario. In emergency situations, the DSO will be able to cut off the EV charging by remotely disconnecting the portion of the grid where the relevant loads are located.

As remarked in D2.2, within the Planned DR family services, load management for minimization of grid investments and maximization of DER integration are among the expected core services of PlanGridEV demonstration. With this type of family services, DSO aims at lowering EV penetration impact by postponing or avoiding power assets and wire investments in order to sustain EVs adoption. The final purpose of these DR products is to enable electric mobility without burdening the system with additional technological adoption cost for the electricity grid. The proper allocation in the LV grid of the charging demand and PV generation within the day would allow the DSO to keep the LV and MV grid with today’s configuration, without replacing transformers or lines that would otherwise be overloaded due to EV charging. As stated in D2.2, the services “Quasi Real Time Demand Response: Enhanced RES integration” and “Quasi Real Time Demand Response: Load Balancing” [D2.2 Catalogue – Paragraph 2.2.7 and 2.2.8. - page 24 and 25] are a short term version from the “Planned Demand Response” services, with small time constrains, depending on the driving force generating the load curve (DSO, Electricity Price, or both of them). This means that the objectives of Planned and Quasi Real Time DR services holds true, but time responses are fairly shorter (few seconds) due to congestion issues (DSO as driver) or wholesale pricing adjustments (Electricity Price as driver). As EDP will seek to demonstrate for the LV grid that in some locations it is possible to postpone grid investments by simply operating remote switches, after analysing the catalogue of services presented in D.2.2 the two Quasi Real Time Demand Response services can be associated to the EDP Test Bed. Although in EDP Test Bed the Smart Grid scenario will not be considered (EDP will test a Conventional Scenario where grid data obtained by smart metering is managed), the type of tests to


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be run in the Portuguese Test Bed will be the starting point for the definition of procedures, information flows and definition of the actors involved in a near future application of one or more Planned Demand/Response Services presented in D2.2 (Catalogue of Products – page 19). The best approach to the EDP Test Bed that can serve as a basis for what Planned Demand Respond services will be in a future Smart Grid Scenario will be the services associated to the “Quasi Real Time Demand Response: Enhanced DER integration” and the “Quasi Real Time Demand Response: Load Balancing”, although the Smart Grid use cases, their business objects and their relations will not be considered in the EDP test bed. In conclusion, based in the Catalogue of Products (Services) derived from D2.2, also mensioned in this document (paragraph 2.1), the service that can more closely match the Portuguese Test Bed is the Service n.º 3. This service aligned with EDP Test Bed and its correspondent “Conventional Scenario” define the Use Case n.1 which is described in paragraph 2.1 as “Load control through network asset and/or demand side control”, and will be demonstrated in EDP Test Bed.


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4.2. Business objects and system interfaces For the EDP test bed objectives no particular business objects will be defined as there will be no services to be provided in the Conventional Scenario. The System Interfaces actually implemented in Portugal are presented in the next image, where it can be seen both the technical and the commercial system interface.

Figure 9 ‐ Portuguese System Interfaces and Actors involved From EDP Test Bed point of view for conventional scenario and use cases selected none technical and commercial new relations will be implemented.

4.3. Test bed location The test site selected for developing the EDP test is located in Lisbon in an urban area with residential and commercial/services characteristics.


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4.3.1. Objectives The LV network will be selected taking into account the current existence of public EV charging points and private (home) charging possibility and PV generation, along with the conventional loads. As the EDP LV networks typically have radial topology without the possibility of network reconfiguration, when necessary the loop topology with possible reconfiguration schemes will be simulated in the software planning tool in order to perform the reconfiguration tests. Nevertheless, in some urban areas (ex.: Lisbon) it’s possible to find low voltage grids in a ring topology scheme open in a Normal Open Point which may provide an alternative reconfiguration scheme.

4.3.2. Criteria The criteria to select the EDP LV networks to develop the tests are:      

LV grids must be adjacent (whenever possible with reconfiguration scheme) LV grids must be robust LV grids must have Public EV charging points LV grids must have Private (home) charging possibility LV grids must have PV generation Consistent load and generation grid data


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Figure 10 - EDP Test Bed LV network represents the EDP Test Bed LV grids which will be used in the project.

Figure 10 ‐ EDP Test Bed LV network

4.3.3. Test bed description Following the criteria defined in the paragraph 4.3.2, the LV network selected to be used as EDP Test Bed is comprised of four MV/LV substations and its correspondent LV grids. The LV grids are fed through a 10 kV medium voltage (MV) network. The LV grids of the four substations have a loop topology with possible reconfiguration scheme in a N.O.P. and are characterized by underground lines. These LV grids are responsible for feeding 1,292 customers of which 1,238 are domestic customers and the rest (54) are commercial facilities. The typology of the residential area is characterized by the presence of single-family homes and apartment buildings. Some of the existing single-family homes (part of the 1,292 domestic customers) already have PV systems installed (17 systems).


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The surrounding areas to the residential buildings have parking possibilities with the potential installation of new EV charging points. In the next table, the Customer data (node information) associated with each MV/LV substation can be seen.

Table 23 ‐ Summary of customer structure for each MV/LV substation.

Customer structure / network node (DSO data) Study Zone

MV/LV substation

Nº Households

N.º Commercial N.º Working Facilities Places

Totals

Label "H"

Label "C"

Label "W"

PTD 7333

297

12

1

310

PTD 9713

153

14

0

167

PTD 6370

569

23

0

592

PTD 4654

219

4

0

223

1.238

53

1

Study Zone Totals

1.292

For each MV/LV substation it can be seen the number and type of customers that can be fed.


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4.4. Test bed timeline According to the timeline of the activities (Figure 11) for the Portuguese Test Bed, the selection of relevant use cases will be completed in month 17 (Task 5.1), associated to the Conventional Scenario chosen in the task “test beds Requirements” (Task 2.2) for Portuguese Test Bed.

CATEGORY

ACTIVITY

1

(…)

6

7

(…) 10

(…) 13

14

15

16

17

18

19 MS3

20

(…) 23

(…) 31

Task 2.1: Scenario Definition “EV Grid Integration Bussiness Scenarios” Task 2.2: Test Beds Requirements

Technical Requirements for tool/methods for smart grid integration of EVs

Use Cases Selection Task 5.1 Not in the scope of Technical Update of EDPTest Bed IT Infrastruture Not in the scope of EDPTest Bed Acording to Use Cases Technical Update of selection, preparation of Grid Infrastruture the LV grids in DPlan software Test Beds: Implementation phase Test Beds: System Integration Test Beds: On field demonstrations

Not in the scope of EDPTest Bed Not in the scope of EDPTest Bed On field main validation tests/demontrations

Figure 11 ‐ Gantt chart for Plan Grid EV test bed

4.5. Test bed exploitation and dissemination This PlanGridEV test bed and its results will be the basis to demonstrate that in some LV urban grids network reconfiguration concept allows to deal with EV, traditional loads and DER generation, avoiding or postponing assets investments. In other situations/grids, where simply reconfiguration process is not enough, the investment will be minimized by applying the above concept. The results form the basis for identifying the characteristics of the LV networks where EDP can implement the concepts/procedures obtained in this test bed, meaning the new Operational Methods derived from the demonstration.


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4.6. Test cases overview The features of “Conventional Scenario” use case that will be demonstrated in the Portuguese Test Bed are the following (Table 24):

Table 24 ‐ Matching of Portuguese test bed against “Conventional Scenario” defined in D2.1

Conventional

Charge management

Portuguese Test Bed of “Conventional” scenario

No

Type of charge management

None

Expected grid reinforcements Non EV-related

Minimal

EV-related

Minimal

Grid  EV Energy flow in EVs that are used to provide services EV  Grid


EVSE Operator

Remuneration scheme

None

Type of power flow control for: Emergency constraint mgt.

Centralised

Forecasted constraint mgt.

None

Real-time constraint mgt.

None

Ancillary services for the TSO

None

Energy trade

None

DER integration

None


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The Test Cases are linked with the Use Case description made in Table 6(Section 2.2 - Use Case number 1: “Load management through network configuration and EV/DER on/off control”) and is framed with the scenario description presented in Table 5(EV and DER on/off control possible but no reinforcements expected, no EVSEO, no ToU tariffs) which has associated the new service “Load control through network asset and/or demand side control” described in section 2.1 of this document.

4.6.1. Description of main validation tests 4.6.1.1

Business as Usual (BAU)

The Business As Usual tests will be carried out considering that EVs and DERs have no control capability. The loading/generation scenarios will match what was defined in Deliverable D1.2 (Future Scenarios). The tests in EDP test bed will be:      

The representation of the LV grid Test Networks in the network simulation software (DPlan) with actual representative loads and micro generation will be simulated Scale up loads by integrating EV as forecast (Future Scenarios) Scale up micro production by integrating DER as forecast (Future Scenarios) Combine both future loads/generation scenarios and evaluate the capability of switching/reconfiguration to avoid/postpone grid investments Optimal locations for remote controlled switchgear installation will be determined with the help of DPlan Simulation of optimal reconfigurable operation will be tested with DPlan to identify possible congestions and/or voltage rise/drop issues

4.6.1.2

DSM (Dynamic Strategy Management)

Optimal EV and DER control strategies, meaning a set of decision criteria to operate the remote controlled LV switches in order to perform load transfer between LV feeders, will be obtained with the help of the Planning Tool for the InovGrid Test Bed network. The DSM tests will then be carried out considering that EVs and DERs are controlled optimally. The benefits of optimal reconfigurable operation will be measured by comparing daily simulation results from DPlan under two different situations:  

A static daily configuration (optimized for the expected peak load) A dynamic configuration (optimized periodically during the day)


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Finally an evaluation of whether existing networks are sufficient or re-planning is generally required will be performed.

4.6.1.3

Dynamic (Re) configuration Sensitivity Analysis

Taking into account that reconfiguration result in fatigue of switchgears, grid operators should limit the frequency of reconfiguration. Reconfiguring less frequently will avoid unnecessary wear on the switches. Previous studies for MV networks such as [12] found out that by varying the interval between reconfigurations from 1 to 6h, the grid operator sacrifices only about 3% of the potential loss reduction by reconfiguration. Using real solar, load and price data, [12] also showed that grid operators may be able to reduce operational costs while accepting more solar or wind generation using reconfiguration. Reconfiguration allows the grid operator to curtail significantly less wind or solar generation than would be necessary without reconfiguration, depending on the magnitude of the DG resource. Thus, based on [12] and its findings a sensitivity analysis for the EDP Test Bed can be performed to determine the adequate number of reconfigurations for LV networks with EVs and DER.


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5. Overview of the Italian (ENEL) test bed 5.1. Description of the process and of the different actors involved The field test of Italy, run by Enel, will be demonstrating a sub-set of use cases derived from the “Smart Grid” integration scenario as described in D2.1 and highlighted in the table below. These use cases, detailed in D3.2, will be deployed around some “smart charging services” published in the related catalogue of D2.2.

Table 25 ‐ Summary of main characteristics of the different scenarios adapted to ENEL Test Bed

Conventional

Safe

Proactive

Smart grid

No


Massive

Massive, local

None

On/off

On/off

Minimal Minimal

Yes Minimal

Minimal No


Grid  EV

Grid  EV

Grid  EV

Grid  EV


EVSE Operator


Remuneration scheme

None

ToU








The framework architecture of the demonstration will be setup according to Figure 12, where a general e-mobility framework architecture is depicted.

9



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6

5

4 3 7 2

8

1

Figure 12 ‐ Framework architecture of electric mobility and its information interfaces (IF 1… 8)

The framework architecture describes the whole processes and market actors interaction needed to deliver the services of “smart charging services” catalogue of D2.2 (from now on D2.2 Catalogue). This architecture is valid regardless the kind of service delivered to the EV user or other interested party. For basic charging service (simple charging: not included in D2.2 Catalogue) EV and EVSE need to be physically connected in conductive charging according to ISO/IEC 61851 (IF 1, arrow between EV and EVSE) as a consequence of the charging process authorization, which happens by validating the B2C relationship between the EV user and his preferred EVSP (IF 5, arrow between EV user and EVSP). Each EVSP has to guarantee access of his EV users in a set of charging stations (EVSEs) to which the EVSP has established a B2B relationship, either by bilateral contracts (IF 4) or by a Marketplacebased model, according to the demonstrations run within Green eMotion FP7 project, GA n. 265499 (See Green eMotion, Deliverable D3.6). The EVSE Operator has the purpose of performing O&M of charging assets and needs information from the DSO, at least for Point Of Delivery setup and grid connection contract. Energy purchase happens at the level of relationship between EVSP and Energy vendors. This framework describes an unbundled approach, where each actor is independent: EVSE Operator is different than EVSP. In principle, some of them might collide in one actor. For example, in the hypothesis of having a regulated deployment of EVSEs, the DSO might be in charge of installation and O&M of the assets. In this case, multiple EVSPs access the charging stations in a multi-vendor approach and each of them could be backed up by its own Energy Vendor of choice. The pilot test in Italy run by Enel will be focused on the deployment and demonstration of a specific


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value added service written in D2.2 Catalogue: “Planned Demand Response: Enhanced RES integration“. Such a service is run similarly to basic charging for what regards the stakeholders interaction. The only significant change is that the charging process setup according to a set of constraints: Initial SOC, Final SOC and Time Of Departure for the EV User perspective and a power target Load Curve given by a third party, which in “Planned Demand Response: Enhanced RES integration” is the DSO, because the integration happens at the whole LV and MV system level. The interaction described above is depicted in Figure 13 - Deployment of smart charging service in Italy test bed. The EV user sets through his end user application (e.g. mobile app) his charging preferences and the EVSE Operator system finds the trade-off between customer constraints and load curve constraints, which are going taking into account the availability and reliability of DERs production nearby a set of EVSEs (IF 7). “Planned Demand Response: Enhanced RES integration” is delivered hence when a basic charging process is executed properly allocating the process as a controllable load reacting to DERs availability below MV/LV transformer, enhancing the DERs hosting capacity of the local electrical grid.

EVSE Op.

Enabling customers to trade time flexibility with money savings, while enhancing DERs hosting capacity S

t

St t Of Ch Ti

fd

EV t

ki

t

EVSE Figure 13 ‐ Deployment of smart charging service in Italy test bed

The EVSE Operation system is in charge of running the optimization algorithm between customer constraints and DSO load curve target. Nevertheless, there is the need of physical validation of such load curve target sent by the DSO, which is implemented through a Distribution Management System (DMS). DMS system is in charge of simulating power flow at LV level, taking into account availability of EVSEs to be modulated in order to host DERs production uptake in a specific timeframe of the day. This simulation validates such a flow against the description of the physical electrical grid where the EVSEs and DERs are connected. Upon its validation, the target load curve is then sent to the EVSE Operator through a set of services. This means that the interface between EVSE Operator and DSO (IF 4) is not the simple one of basic charging Figure 12 but a wider one, which includes a set of real time services to let EVSE Operator and DSO exchange information. The interfaces of Figure 12


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applied for the delivery of “Planned Demand Response: Enhanced RES integration” service will be hereby detailed.

5.2. Business objects and system interfaces This paragraph will give an overview of description and specs for the relevant business objects and system interfaces needed in order to demonstrated the “Planned Demand Response: Enhanced RES integration” services as described in D2.2 Catalogue. This business objects and system interfaces might be the basis of an open smart charging protocol to be established leveraging the Italy’s test bed.

5.2.1. Interface 1: EV‐EVSE Stakeholders Involved: EV, EVSE, EVSE Operator Description It is a physical and an IT connection established between EV and EVSE. The physical connection allows energy flow after EV User authentication and it has to be compliant to ISO/IEC 62196. The IT connection has a significant role in the way by which services can be delivered to the final customer. The level of depth of this IT Connection in today’s market encompasses 3 major implementations: ISO/IEC 61851 (also referred as PWM modulation from now on) or PLC based communication such as DIN 70121 (for DC charging) and ISO/IEC 15118. At the time of writing this document and planning the demo, only ISO/IEC 61851 is an available tool to the delivery Power Modulation level a demonstration of smart charging service which includes modulation. This means that the only available source of information such as Initial SOC and Final SOC is the EV User, as no advanced scheduling can be traded with the EV. In case of availability of ISO/IEC 15118 compliant EVs, smart charging service could be implemented over PLC based communication, allowing through the same IF 1 the delivery of Power Modulation, Initial SOC and Final SOC. Business objects involved Power Modulation level, Initial SOC, Final SOC Specifications involved ISO/IEC 61851, DIN 70121, ISO/IEC 15118.


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5.2.2. Interface 2: EVSE‐EVSE Operator Stakeholders Involved: EVSE, EVSE Operator Description This interface allows operation and maintenance of EVSEs. Typically all EVSEs are managed through an asset management IT system which allows EV user authentication, EV user billing, EVSE operation and alarm signalling, EVSE parameters setting. For what regards a smart charging service, this interface allows the delivery of set point of EV charging process (Power Modulation level) which could be changed according optimization trade-off between EV user preferences and DSO Target Load Curve. The implementation of this interface is currently not harmonized. There is no international standard in place, although eMI3 Group, an international association for e-mobility IT standardization, (www.emi3group.com) is currently working on such standard proposal, including the Enel’s own proprietary protocol which includes the possibility of updating Power Modulation level for each EVSE. Business objects involved Power Modulation level Specifications Enel EV-EVSE communication protocol, as submitted and continuously updated through eMI3 Group.

5.2.3. Interface 3: EVSE Operator ‐ DSO Stakeholders Involved: EVSE, EVSE Operator, DSO Description This interface allows the exchange of information between EVSE Operator back-end and DSO systems. The purpose of the interface is to deliver the DSO constraints to the EVSE Operator, by means of the business object called DSO Target Load Curve, which is a vector consisting in maximum power values to be globally absorbed over the Load Area, a selection of EVSEs. The EVSE Operator will perform its optimization trade-off between the DSO Target Load Curve and EV user preferences, in order to allocate the proper Power Modulation level to each EVSEs belonging to the Load Area on which the DSO has elaborated the Target Load Curve. Business Objects involved DSO Target Load Curve, Load Area


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Specifications This communication is usually performed by means of a web service implemented on EVSE Operator and DSO side. For the purpose of “Planned Demand Response: Enhanced RES integration” service demonstration, the DMS system will behave on behalf of DSO, implementing the client interface of “Load Management Target” web service already available on Italy’s EVSE Operator back-end. Load Management Target web service specs are available in Green eMotion FP7 Project, Grant Agreement n. 265499, Deliverable 3.6 “Core services and transactions design specification 2”. The Load Area for the PlanGridEV demonstration will be fixed. In principle, this business object is a result of dynamic calculation run at DSO level in compliance with the proposals of ADDRESS FP7 project (www.addressfp7.org).

5.2.4. Interface 4: EVSE Operator ‐ EVSP Stakeholders Involved: EVSE Operator, EV User, EVSP Description The general purpose of such interface is to enable customer authentication and customer billing. The specific purpose of this interface, with regards to a smart charging service implementation, is to propagate the EV User preferences from the EVSP to the EVSE Operator which runs the optimization trade-off. For PlanGridEV demonstration, this interface is embedded within the same IT system of Enel, which performs simultaneously EVSE Operator and EVSP roles. This means that the EV user mobile application by which he will input Initial SOC, Final SOC and Time of Departure business objects will be pointing a web service end point on board of EVSE Operator back-end system. Business Objects involved Initial SOC, Final SOC, Time of Departure, Customer ID Specifications There are no harmonized specifications available yet. For the purpose of PlanGridEV demonstration of “Planned Demand Response: Enhanced RES integration” service, this interface is formally declared at system architecture (Figure 12) but not implemented, as it is embedded within the inner operation of EVSE Operator back-end, which is directly receiving the EV user preferences.


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5.2.5. Interface 5: EV User ‐ EVSP Stakeholders Involved: EV User, EVSP Description This is the interface that allows EV User to input charging process preferences into the EVSE Operator optimization algorithm. The implementation could be via web service having end-point on the EVSE Operator system and entry-point on EV User mobile application. Business Objects Initial SOC, Final SOC, Time of Departure Specifications There are no harmonized specifications available yet. In principle, each EVSP holds its own EV User mobile application and describe the specifications for the delivery of core and value added services to the EV User. Specifications of the IF 5 according to PlanGridEV test bed needs are going to be detailed and made available before demonstration execution, according to the test bed Timeline (see paragraph below).

5.2.6. Interface 6: EVSP – Energy Vendor Stakeholders Involved: EV User, Energy Vendor, EVSP Description This is the interface that allows EVSP to contract energy supply for the delivery of basic and smart charging services to the EV User. This interface is typically at contract and IT level simultaneously. The tariff schema could be a driver for EV user preferences when the EV User sets them in IF 5. Business Objects Tariff schema Specifications This is a contractual and IT interface between EVSP and Energy Vendor. There are no harmonized specifications available yet.


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5.2.7. Interface 7: DSO – RES Operator Stakeholders involved: DSO, RES Operators Description The purpose of this interface is to exchange the availability forecast of DER between DER Operator and DSO in order, for the DSO, to elaborate a DSO target Load Curve to optimize renewable integrations. Business Objects DER production curve Specifications Specifications will be suggested to DER Operators willing to participate in “Planned Demand Response: Enhanced DER integration”. For demonstration in Plan Grid EV the DER production curve will be simulated.

5.2.8. Interface 8: DSO – LV/MV Grid Stakeholders involved: DSO Description The purpose of this interface is to harvest information regarding the LV/MV Grid state and validate the DSO target Load Curve over the selected LV/MV Grid. These information are available thought DMS. Business Objects GIS Specifications DMS is a custom system fulfilling the requirements of D2.2 and allowing this interface to be fully functional for the delivery of “Planned Demand Response: Enhanced DER integration”.

5.3. Test bed location The pilot site for PlanGridEV test bed in Italy will be L’Aquila city, which is part of grid operation area coded DM7N, L’Aquila capital city of Abruzzo Region (Figure 14).


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Figure 14 ‐ L’Aquila and Abruzzo Region The pilot site will be hosting a set of Enel AC charging stations (Figure 15) deployed in the time frame January 2015-June 2015 for public and private charging usage as part of nationally funded “Smart City” project based in L’Aquila.

Figure 15 ‐ Enel AC Charging Station for public charging These charging stations can be equipped with different type of sockets each of them enabling charging processes compliant to Mode 3 according to IEC 61851.

All the stations are connected through GPRS/UMTS communication with the EMM Platform (EVSE Operation back-end of Enel) over a dedicated APN. All charging processes can be monitored and controlled remotely, changing for example the Power Modulation level during the execution of “Planned Demand Response: Enhanced DER integration” service.


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Figure 16 ‐ Execution of “Planned Demand Response: Enhanced DER integration” service

Three charging stations amongst the ones deployed in L’Aquila will be selected for the demonstration and will be forming the test Load Area, according to the prescription set forth in the previous paragraph. The Power Modulation level given to each EVSE of the Load Area is decided by the EVSE Operator finding the trade-off between the availability of renewable energy (through the desired DSO Target Load Curve) and the EV User preferences (Initial SOC, Final SOC, Time of departure).

Before assigning the DSO Target Load Curve to the EVSE Operator, the DMS implementing interface 8 of the Framework architecture checks its feasibility with the electricity grid state and its physical description.

The grid operation area of L’Aquila has the following characteristics:



DM7N as AUI zone code (Archivio Unico di Impianti: database of components and network elements)



5035 km2 of extension;


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

247917 LV customers;



574 MV customers;



24 primary substations;



2902 MV/LV substations;



342 MV safety switches;



2863 km of MV network;



5636 km of LV network.

The following table contains the main characteristics of the DM7N LV network nodes: Table 26 Main characteristics of the DM7N LV network nodes ID AUI Zone

Name Zone

Node number

Node description

DM7N

L'AQUILA

11281

Generic node

DM7N

L'AQUILA

17025

Node with LV switch

DM7N

L'AQUILA

106108

LV node

DM7N

L'AQUILA

11402

Delivery node to LV customer

DM7N

L'AQUILA

7863

Starting node of the LV feeder

DM7N

L'AQUILA

4

LV/LV transformers

DM7N

L'AQUILA

153683

5.4. Test bed timeline According to the timeline of the activities (Figure 17) for the Italy’s test bed, the selection of relevant use cases will be completed in month 17, deriving the use cases related to the “Planned Demand Response: Enhanced DER integration” service chosen in the phase “test beds Requirements” (Task 2.2) for Italy’s test bed. This service is demonstrating a set of features belonging to “Smart Grid” scenario within “Scenario Definition” activity (Task 2.1).


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CATEGORY

ACTIVITY

Scenario Definition

Task 2.1 “EV‐Grid Integration Business Scenarios”

Test beds Requirements

Task 2.2 “Technical Requirements for tools/methods for smart grid integration of Evs”

Use cases selection

Task 5.1

6

7

8

9 10 11 12 13 14 15 16 17 18 19 (…) 23 (…) 26 (…) 32 33

Deployment of services in EVSE Op. Technical Updates of (IT) Infrastructure

Technical Update of (field) infrastructure

Test beds: implementation phase

Deployment of DMS – EVSE Op. interfaces According to use cases selection, preparation of field infrastructure: check against EVSEs installed and DER Operators locally available. EVSE Op. update are ready to use DMS is properly interfaced

Test beds: system integration

Systems integration validation

Test beds: on field Demonstrations

On field Demonstrations Test Cases and Demo Plan



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The features of “Smart Grid” scenario demonstrated in Italy’s test bed are the following (Figure 18): Smart grid

Charge management

Massive, local


Charge modulation

Italy’s test bed of “Smart Grid” scenario


No

EV-related

No

Energy flow in EVs that are used to provide services

Grid  EV EV  Grid


EVSP

Remuneration scheme

Competitive market


Centralised


Decentralised


Decentralised


Decentralised

Energy trade

Decentralised

DER integration

Decentralised

Figure 18 ‐ Matching of Italy’s test bed against “Smart Grid” scenario defined in D2.1. Once the use cases have been selected, the necessary IT-infrastructure updates will be deployed. These updates include: 

The services for smart charging in the EVSE Operator backend in order to satisfy interface 3.



The services for smart charging in DSO systems (DMS) in order to satisfy interface 3.



The installation of a DMS-license to perform state-analysis in the operation area of the demonstration grid.


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From month 19 to month 23 the EVSE Operation backend and DMS system gets integrated for the execution of the “Planned Demand Response: Enhanced DER integration” service. During the same timeframe the test cases proposed in the following paragraph will be further detailed with realistic parameters related to local renewable plants in the demonstration grid operation area. With the test cases refined and the system integration validated, the demonstration will start on month 25 and finish on month 32, producing the data for the final report of test bed activity.

5.5. Test bed exploitation and dissemination This test bed fits in a general innovation pursued by Enel within FP7 initiatives, leading to the field demonstration and proof of concept of two products within the PlanGridEV D2.2 Catalogue of services: “Planned Demand Response: Load Management for minimization of electricity grid investments” and “Planned Demand Response: Enhancement of DER integration”. The IT service infrastructure discussed above has been built within the activities of EU funded projects such as Green eMotion, MOBINcity and PlanGridEV, with the goal of equipping the Enel infrastructure with the capability of providing enabling technology for Demand Response products by 2017. The validation of PlanGridEV’s test bed is therefore crucial for proof of concept and builds up as a first product prototype of “Planned Demand Response: Enhancement of DER integration”.


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5.6. Test cases overview The following experiments are relevant for the PlanGridEV project within the demonstration of a “Planned Demand Response: Enhancement of DER integration“ product. The expectations for the results of the field test are then defined for each test.

5.6.1. Test Case 1: Customer preferences for smart charging Goal:

Setting of customer preferences for smart charging

Description:

The customer will set Initial SOC, Final SOC, and Time of Departure through HMI Smartphone application.

Results: The routine test will prove that the constraints established by the EV customer arrive correctly to the EVSE Operator. EV customer preferences will be used by the optimization algorithm as boundary conditions. The optimum charging load curve established will respect the EV user preferences.

Field test: In the field test the user sets Initial SOC, Final SOC and Time of departure through HMI Smartphone application. These values will be correctly read in the EVSE operator interface.

5.6.2. Test Case 2: Power modulation Goal:

Setting PWM of the recharging process according to optimum charging schedule for EVs.

Description:

Through communication between EVSE Operator and EVSE (PWM signal though pilot wire) the power (current) during charging process is set.

Results: The power (or current) limit is defined for each charging period. The load curve for each charging process is given as a PWM value every fifteen minutes.

Field test: The routine test will verify through an oscilloscope that the PWM settled by the EVSE is according the one established by the optimization algorithm.


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Figure 19 ‐ Example of PWM Duty cycle variation during charging process

5.6.3. Test Case 3: DMS‐EMM connection for load management Goal:

Test DSO target communication to EVSE Operator

Description:

Periodically or when updates occur the DSO DMS shares a demand target considering grid assets and DER generation.

Results: The DSO sends through a Web Service the optimum demand for a load area obtained from network conditions and DER availability.

Field test: The routine test will verify that the target profile assigned by the DMS for a load area will be correctly communicate to the EVSE Operator in order to use it for the optimization algorithm.


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Figure 20 ‐ Example of DMS Test case


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6. Overview of the Irish (ESB) test bed The ESB test bed will consist of one specific site where 3 electric vehicles will be deployed on a 33kVA transformer fed on a single phase MV network. The specific section of network consists of a 33kVA transformer which currently feed 4 residential customers. The test bed will be implemented to examine the impact of high penetration levels of electric vehicles on a typical rural network. Intelligent charging will be utilised to examine its capabilities in resolving network congestion.

Table 27 ‐ Summary of main characteristics of the different scenarios adapted to ESB Test Bed

Conventional

Safe

Proactive

Smart grid

No


Massive

Massive, local

None

On/off

On/off

Minimal Minimal

Yes Minimal

Minimal No


Grid  EV

Grid  EV

Grid  EV

Grid  EV


EVSE Operator


Remuneration scheme

None

ToU








The concept of meshing of LV networks will also be investigated from a simulations perspective. The implementation of meshing LV networks faces significant technical challenges from a protection and operational perspective. ESB Networks are currently working with Manufacturers to develop a device to perform LV network meshing that meets these requirements. Monitoring of the test bed will consist of MV/LV transformer monitoring, as well as end of line monitoring and smart metering. The trial will identify the challenges that are faced in the rural electricity network in the Republic of Ireland through the roll out of electric vehicles.

10



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The trail will consist of 4 key stages; 1. No Electric Vehicles Connected – This will provide the benchmark for the current standard of the test bed network 2. Electric Vehicles deployed but charging restricted between the hours of 11pm and 7am – this will provide the worst case scenario as vehicles began to charge at the same time 3. Electric vehicles deployed with unrestricted charge times – this will identify the likely impact if no control measures were developed 4. Intelligent charging – vehicle charge times will be monitored and staggered so as to reduce charging usage peaks

6.1. Description of the process and of the different actors involved The following section identifies the business process involved in the test bed.

6.1.1. DSO ‐ ESB Networks ESB Networks (ESB) is the sole Distribution System Operator, Meter Operator and Network Asset owner for the Republic of Ireland serving a population of over 2.2 million customers with over 175,000 km of transmission and distribution network. The system has a peak demand of 5000MW and already over 1700MW DER has been connected – over 50% being connected at Distribution level.

6.1.2. EVSO – ESB eCars ESB established ESB eCars in 2010 to roll out the charging infrastructure for electric cars and vehicles across Ireland and to support the introduction and demand for electric cars nationally.

6.1.3. Electricity Service Provider This is the supplier of the residential electricity and is dependent on the Electricity Supplier chosen by the home owner.

6.1.4. EV User This is the user of the electric vehicles whose choices will dictate the impact the EV charging will have on the distribution network. In the case of the test bed they are the home owner where the EV will be located.


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6.2. Business objects and system interfaces The following two sections will outline in detail the business objects and the system interfaces. As with most smart grid concepts these system interfaces are the current concept and are subject to change dependent on future market architecture. From the perspective of a DSO the business objective would be to examine the impact that the wide spread roll out of EVs would have on the rural distribution network in Ireland. The investigation into the impact of multiple EVs on a low rated transformer will identify the challenges ESB Networks will face going forward and tools that are developed as part of PlanGridEV are envisaged to aid in solving these challenges. Ireland is unique in the fact that it has 3 times the average network length per customer in Europe, this is largely down to the geographic dispersion of the population and hence the widespread use of 15kVA and 33kVA transformers.


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The System Interfaces can be classed by being both commercial and Technical. The following discusses these interfaces.

6.2.1. Technical Interface

1. Electricity, which includes distribution connected renewable energy sources, flows to the distribution system. 2. ESB Networks complete testing to determine suitable locations and standards for charge points. MV/LV substation monitoring is currently being developed so as to manage the increased demand associated with EV growth. 3. ESB ecars will monitor the home charge point consumption. If there is a large uptake of EVs, it may use intelligent communication infrastructure to complete demand response and balancing activities. 4. ESB ecars manage the public charge points in consultation with ESB Networks who facilitate the installation of Public charge points through network design and analysis. 5. The EV will charge in line with the selected operation of the charging unit. 6.

As per 5.

Figure 21 – Technical Interface


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6.2.2. Commercial Interface

The energy supplier purchases the electricity from the Single Electricity Market (SEM). 1. The EV user chooses an energy supplier. The electricity consumed by the home charging units is included in the domestic utility bill. Due to the low uptake of EVs there is no market in place for the public charge points and electricity consumption is included in network losses. A Pay-As-You-Go market system is being developed which will allow EV users to choose an energy supplier for their public charge point usage. 2. The EV user will register its EV with ESB ecars which will install the home charge point. The relationship between the EV user and the home charge point will ultimately dictate the impact of EVs on rural electricity networks. 3. After registering the EV the user will get an access card that is required to use the public charge points. 4. The EV will charge based on the user’s specifications and will be charged at the domestic rate. 5. until

The EV is currently charged for free the Pay-As-You-Go scheme is

implemented. Figure 22 – Commercial Interface


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6.3. Test bed location The ESB PlanGridEV EV test bed will be located in County Limerick on the west coast of Ireland. Limerick is the eighth most populous county in the Republic of Ireland and the third most populous local council area. It is the fifth largest of Munster's six counties in size and the second largest by population. The River Shannon flows through the city of Limerick into the Atlantic Ocean at the north of the county. Below the city, the waterway is known as the Shannon Estuary. Because the estuary is shallow, the county's most important port is several kilometres west of the city, at Foynes. Limerick City is the county town and is also Ireland's third largest city. County Limerick covers an area of approximately 2,756 km2 and has a population of 191,809.

Figure 23 – Ireland test bed loaction


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Figure 24 ‐ PV Potential ‐ European Union, 2012 In terms of PV potential we are not currently experiencing significant PV installations as can be seen in the above Figure 24, Ireland does not have the same PV potential as other countries such as Italy and Portugal.

Figure 25 ‐ ESB eCars Home Charger

Home charge points will be installed at the 3 residential locations who will receive an electric vehicle for the duration of the trial. A home charge point is usually installed on an external wall of the house and electric car charging is facilitated through the domestic electricity supply. An electric car can draw single phase 16A (3.6kW) when connected to your home charge point.

Figure 26 ‐ ESB eCars 16A Public Charger and 50kW DC Fast Charger In the vicinity of the test bed public ESB eCars electric vehicle charging infrastructure is also available.


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Figure 27 ‐ Network Topology Test Bed Location The test bed is located on a single phase spur off of a three phase 20kV medium voltage back bone line. The four customers are fed from a 33kVA pole mounted transformer; three of these customers are fed via underground cable and one via overhead conductor. The exact overhead and underground cable type will be outlined in the network analysis stage.


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6.4. Test bed timeline CATEGORY

ACTIVITY

Scenario Definition




Use cases selection

Task 5.1

6

7

8

9 10 11 12 13 14 15 16 17 18 19 (…) 23 (…) 26 (…) 32 33









Figure 28 ‐ Timelines for PlanGridEV Test beds

6.5. Tested exploitation and dissemination The results of the PlanGridEV test bed in Ireland will pave the way in which DSOs will manage the impact of EVs on rural single phase networks. The goal from an ESB point of view is to analyse the impact and to verify if the proposed mitigating measures are suitable. The implementation of intelligent EV charging will enable the maximum penetration through the maximum utilisation of network assets. Efficient asset utilisation is crucial to enabe EV deployment but is also crucial to ensure best practice continuity and quality of supply, while also ensuring the most cost efficient management of the distribution network. The communication and monitoring that will be deployed has been researched and developed through other FP7 funded projects such as Green eMotion, evolvDSO and FINESCE. Through the combination of knowledge accrued through these working groups and research the PlanGridEV test bed will be seen as industry best practice for management of EVs on a rural single phase network.


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6.6. Test cases overview Intelligent charging will be implemented and its applicability examined from the perspective of ensuring maximum EV network capacity and quality of supply.

Table 28 ‐ Matching of Ireland test bed against “Conventional Scenario” defined in D2.1 Conventional Save

Charge management

Ireland Test Bed of “Conventional” “ Safe” scenario

Yes


On / Off


Yes

EV-related

Yes

Grid  EV Energy flow in EVs that are used to provide services EV  Grid


EVSE Operator

Remuneration scheme

None


Centralised


None


None


None

Energy trade

None

DER integration

None

The test bed will be investigated over four scenarios, as follows:


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6.6.1. Test Case 1 – No Electric Vehicles Connected This will provide the benchmark for the current state of the test bed network in terms of network capacity and will identify likely situations to be experienced in the following scenarios.

6.6.2. Test Case 2 – Electric Vehicles deployed but charging restricted Electric Vehicles deployed but charging restricted between the hours of 11pm and 7am – this will provide the worst case scenario as vehicles begin to charge at the same time, it is envisaged that if overloading is likely to take place it will occur at this time so it is crucial the network is closely monitored during this trial period.

6.6.3. Test Case 3 – Electric vehicles deployed with unrestricted charge times Electric vehicles deployed with unrestricted charge times – this will identify the likely impact if no control measures were developed. The likely scenario to be experienced here is dependent on the lifestyle of the EV drivers. There is the possibility that all EVs will be connected when residents return from work in the evening and begin to charge immediately. This uncertainty poses risk as identified in the above scenario so the network parameters will need to be monitored closely.

6.6.4. Test Case 4 – Intelligent charging Intelligent charging – vehicle charge times will be monitored and staggered so as to reduce charging usage peaks. This scenario is expected to manage the issues that will be experienced in the above scenarios. The performance of this specific scenario will be a useful identifier in terms of the applicability of intelligent charging as a possible network congestion solution.


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7. Overview of the German (RWE) test bed For the field test RWE makes use of the test bed, which was established for the RWE Project Smart Operator. In this test bed many smart grid components including stationary battery storage, renewables from photovoltaic, measurement in the sub stations and electric vehicles have been deployed. This makes it an ideal base for the proactive and partly even the smart grid use case of PlanGridEV. PlanGridEV can carry out the proposed tests from section 2 in this test bed. Since the developed components from “Smart Operator” like the home energy controller (HEC) are also used by PlanGridEV to show local customer and grid motivated control they are also described below. Also some insights about possible tests and laboratory results are given.

7.1. Description of the process and the different actors involved The test series in the project PlanGridEV make use of tests in connection with the project “Smart Operator”. The test scenarios were divided into  

a laboratory test in Aachen and field tests in Schwabmünchen and Kisselbach

this way the functionality of the control algorithms could first be observed in the laboratory test in Aachen before being used in the field. The laboratory test was done by the IFHT at the RWTH Aachen. All of the pictures in this report were created by colleagues from the IFHT. Many data also was provided by other partners of RWE Deutschland AG in the frame of the Smart Operator project: RWTH Aachen University, PSI, Hoppecke, Stiebel Eltron, MR and Horlemann. During the field test, the control by the smart operator will have to prove its practicality. As part of the project PlanGridEV especially the tests are relevant, where the DER input and the reference current for charging EV are examined.


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Table 29 ‐ Summary of main characteristics of the different scenarios adapted to RWE Test Bed

Conventional

Safe

Proactive

Smart grid

No


Massive

Massive, local

None

On/off

On/off

Minimal Minimal

Yes Minimal

Minimal No


Grid  EV

Grid  EV

Grid  EV

Grid  EV


EVSE Operator


Remuneration scheme

None

ToU








7.2. Business objects and system interfaces The business objects and system interfaces necessary and deployed via “Smart Operator” for use within PlanGridEV are already well described in section 5.2 and are not repeated here.

7.3. Test bed location While a relatively new settlement area was selected in the grid area of the LVN (LEW Verteilnetz GmbH), in which several households are to be equipped with the Home Energy Manager, older and more diverse settlements were sought for the field test sites in the grid of the Westnetz GmbH.

7.3.1. Field test region Kisselbach The initial in the German field test area Kisselbach was characterized by the following properties: 

Availability of a fibre optic network for transmitting data from the smart meters to the

11



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attachment sites in the ONS 

Large number of PV installations (roofs, 12 units with 170 kWp)



We expected a high willingness of customers to a smart meter installation attributable agree, as several colleagues from the Westnetz GmbH offers Kisselbach and convince their neighbours on site could

The following Figure 29 shows a diagram of the field test area Kisselbach.

Figure 29 ‐ Test Bed Kisselbach

7.3.2. Field test region Wertachau The field test of the LVN in the settlement Wertachau near Augsburg comprises the only part of the project covering HECs in private households. A total of around 115 households (as of January 2014) take part. The involvement of private households the subproject has a whole lot of specifics that have been specially developed and implemented. One of the most distinctive features is its own fibre-optic network to connect the smart meters in the


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households as well as for communication between the components in the households with the Smart Operator. The project for the first time will record just in time a significant number of profiles of the electricity consumption in more than 100 private households and will evaluate and summarize data from the consumption sent from the smart meters. Focus in the field test LVN lies on the control of intelligent home appliances in the homes of the project participants through the Smart Operator.

7.3.3. Planning and project structure Planning for the project began in early 2012 to examine the technological, financial and required human resources. The project scope makes the integration of different disciplines necessary. In LVN a seven-member steering committee was formed including the Directors superior to the project manager and other representatives of the first management level. The actual project team consists of 12 employees from various departments. Steering committee and project team came together for the first time in March 2012.

7.3.4. Criteria for the selection of the test beds The place for implementation was selected by grid-related criteria. The decision was taken for the Wertachau, a small settlement from the years between 1950 and 1960. It lies west of Augsburg in the town Schwabmunchen.

The following criteria were considered, when selecting Wertachau:



Secluded settlement area



Direct connection to the substation Schwabmunchen (20 kV connection, communication technology)



LV grid: grid with a ring topology with open mesh-up connectors



125 customer connections, 23 PV installations, 10 electrical storage heaters and 8 warm water storages



LV grid with earth cables since 1998



Empty tubes were available for laying the fibre-optic network cables.


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Figure 30 ‐ Test Bed Wertachau

Figure 31 ‐ Test Bed Wertachau


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7.4. Test bed timeline The timeline for the German RWE test bed is presented in Figure 32. CATEGORY

ACTIVITY

Scenario Definition




Use cases selection

Task 5.1

6

7

8

9 10 11 12 13 14 15 16 17 18 19 (…) 23 (…) 26 (…) 32 33











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The German scenario is described in Table 30: Table 30 ‐ Matching of Germany’s test bed against “Proactive” and “Smart Grid” scenario defined in D2.1

Proactive Smart Grid

Charge management

Massive, local


Charge modulation

Germany’s test bed of “Proactive” “Smart Grid” scenario


No

EV-related

No

Energy flow in EVs that are used to provide services

Grid  EV EV  Grid


EVSP

Remuneration scheme

Regulated contract


Centralised


Decentralised


Decentralised


Decentralised

Energy trade

Decentralised

DER integration

Decentralised


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7.5. Test bed exploitation and dissemination The results of the PlanGridEV test bed in Germany will pave the way for the way in which DSOs will manage the impact of EVs on rural networks with high integration of DER and additionally controllable equipment in the grid as well as in the households. The goal from a RWE point of view is to analyse the impact and to verify if the proposed mitigating measures are suitable to integrate more DER and more EVs. The implementation of intelligent controllers in the households (HEC) and in the substations (smart operator) will enable the maximum penetration through the maximum utilisation of network assets. The communication and monitoring that will be deployed has been researched and developed through the RWE internally financed project Smart Operator and can be used for evaluation purposes within PlanGridEV. Smart Operator shares relevant information about test results with PlanGridEV (e.g. test case results in chapter 7.6). The findings from PlanGridEV will be integrated in the Smart Operator test site. Through the combination of knowledge of both research projects the PlanGridEV/Smart Operator test bed will be seen as industry best practice for decentralised network control in conjunction with DER, EVs and other controllable loads.


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7.6. Test cases overview The following experiments have been carried out by the project “Smart Operator”. The relevant results can be used for the PlanGridEV project. The results of the experiments in the lab are presented and summarized. Besides the laboratory test also the findings from the upcoming field tests can be used by Smart Operator. The expectations for the results of the field test are then defined for each test. Extensions to the below described test program can be made for PlanGridEV. The investigation of the control behaviour of the Smart Operator will be tested in three scenarios:

Stability during normal operation: In normal operation, the behaviour of the components will be tested when voltages and loads stay in the distribution grid within acceptable limits. This test is to be realized under near-realistic operating conditions and offers a high variability of network structures. Additionally, different consumption- and feed-in-technologies at various points integrated in the test set and predetermined power profiles can be traversed. Also, control technologies, such as inverters in photovoltaic systems, which are not under the direct management of the network operator but able to interact with other control technologies must be integrated in the test-structure.

Stability during operation outside of limits: There are network situations created which move outside the state defined for the algorithm to be admissible. I.e. voltage variations and dips, as well as overload of resources can be simulated. This results in the definition of frame requirements, which the laboratory environment has to be able to produce. The power of the loads and feeders must be overdimensioned compared to the current-carrying capacity of the equipment, so that voltage and resource limits may be exceeded during the test period. The energy source of the laboratory network must be highly flexible and quickly adjustable in amplitude and phase, as to simulate voltage fluctuations and dips imposed by the MV distribution networks. Faults at primary and/or auxiliary equipment: In this type of test, the failure of communication and measurement technology or errors within the components of the Smart Operator system (actuators, communication and sensors) is investigated. The test setup must be done such that even extreme conditions can be realized in the test structure. The wide range of requirements needs intensive series of tests in a flexible and modifiable operational environment.


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7.6.1. The environment for the lab test The main components of the "Centre for grid integration and storage technologies" of RWTH Aachen University form a 10 kV medium-voltage network with 200 MVA short-circuit power, different local network stations and a flexible switchable low-voltage grid. In the medium voltage network structure a 4 MW low-voltage ride-through (LVRT) Test Lane is integrated, able to generate voltage dips by an inductive bleeder. Single and double voltage dips can be generated from 0 to 85% of rated voltage. Another core component is a network simulator with 90 kVA continuous output, which allows using 4quadrant amplifiers to generate any voltage curves (0 to 270 V, 0 to 5000 Hz). The low-voltage grid consists of 9 lines (NAYY-J 4x35 mm ²) with 25 to 500 m in length, which can flexibly be interconnected using a junction box (JB) and fuse switches. In the JB are integrated voltage taps and current transformers, the measurements of which are evaluated via network analysers. In the test environment the replica of consumers is done by automated heater loads, which cover a power range from 0 to 20 kW in a gradation of 0.5 kW. In addition, uncontrolled loads are available (3.3 kW), which are integrated via switchable sockets. Real inverters (2.5 to 36 kVA) simulate feeders and are supplied by remote-controlled DC power supplies 5-15 kW. Here, a parallel arrangement of up to three power supplies for each inverter is used to feed the required power. All components can be integrated via a connection to the JB arbitrarily in the grid topology. Through a connection of these components local voltage variations of more than 10% of rated voltage are possible. The implementation of the test cases requires a time-synchronous control of the components of the test centre. For this purpose, a control mechanism is embedded, based on the Labview ® software, which allows integration of all components. A relational database acts as interface to access the stored measured values, profiles, connection setups and component. The sequence of an examination consists of three phases. In the initialization phase of the test set-up, the component list and the metering positions can be transferred to the controller and the components are connected. This phase serves for the self-testing of inverters and other components, so that after turning on, a stable system state will be reached with all devices being operable. In the second phase, the experiment is carried out. Here, the load and DER generation profiles can be loaded (with a one second resolution) from the database and simultaneously be distributed to the components (where applicable). A comparison of specified value and actual value delivers feedback on the current state of the grid in the form of a gauge. The user thus has full control during the experiment of the grid status and the utilization of resources as well as a comparison of specified data vs real data for the components, as to validate the test items (here: Smart Operator controlled components). In the third phase, all the components are cut off and transferred to a secure operation mode. Evaluation of the test sequence is done through queries of the measurement data from the database.


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VK = JB Junction Box SM Smart Meter LB Load bank LS Charging point for EV PV DER source HEC Home Energy Controller

Figure 33 ‐ Set up of the experimental network with loads and generators of the Smart Operator The Figure 33 shows the structure of the test network with two lines with a length of 350 and 525 meters. The necessary voltage band deviations above the lines can be enforced by loads and generators, which then cause defined deviations with the help of laboratory control profiles. The load banks are distributed so that loads are installed on each node of the low voltage network. In addition, a feeder (PV) is installed in the network to achieve voltage increases. This is positioned at the end of the strand, in order to achieve a maximum voltage at the maximum feed.

In order to explain the process and the different types of control and regulation of the algorithm, evaluations are given regarding the status of the control in the experiments. The following list explains the different meanings: 

Forecast: With the forecast-bit active, the algorithm follows its created prognosis and carries out the appropriate switching operation.



Actual state: When not following the forecast, the smart operator remains in its current state and does not perform any switching action.


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

Grid control: Are tolerance limits exceeded in several lines, grid control is activated. All operating equipment can be used to fix the limit violation.



Line control: If tolerance limits are exceeded in one line only, line control is activated. Only resources of the line can be used to fix the limit violation.



Backup-Control: If line or grid control is not successful, the active back-up control (expert system) is activated. This checks whether a threshold violation exists and whether it can be possibly resolved by switching operations of the voltage regulated distribution transformer (VRDT). In addition, the Simple Backup control (VRDT wide-range control) gets active when the software returns incorrect values or the state estimation does not converge.



Fault in one line: Indicator of whether a limit value violation is present in only one line.



Faults in two lines: Indicator of whether a limit value violation is present in two lines.


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7.6.2. Forecasting The smart operator creates a forecast for all connected resources for the next 24 hours in a 15-minute resolution. The forecast is renewed every 6 hours. The basis of the forecast is seasonal and weather-related information. In the laboratory tests, test durations are typically set to 1 hour or less, except for the cycle test R-1. As to have similar conditions for all experiments, the Smart Operator was trained using data for 1 hour only. This should make the results from the different experiments comparable. To cover a whole day, this hour is repeated accordingly 24 times. In the field test, this restriction does not apply.

0 Forcast generation

96

72 24

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1

16 17 18 19 20 21 22 23 24 25 26 27 28 29

2

Figure 34 ‐ Generating hourly forecasts

Weather information: The algorithm of the Smart Operator can process three different weather variables. These are temperature, wind speed and global radiation. The basic idea of using external weather variables is to identify seasonal fluctuations and thus different grid situations. Typically the grid situation is more loaddominated in winter and in summer rather DER dominated (PV feeding in). If the relevant information is made available to the algorithm of the Smart Operator, it will rely on learned information to identify the appropriate control strategy.


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HEC-Forecast: For the HEC no power profiles for the learning phase were available. Instead we formed load curves from the existing information on the equipment connected to the HEC, from which the algorithm can choose a current load value. For the laboratory validation this is of high relevance, as the steps for the load value substantially influence the learning behaviour and the formation of the prediction of the algorithm. Forecasting depending on the weather information: In order to validate, whether a different PV feed (DER) has a significant impact on the grid situation and therefore should be considered in the forecast, the algorithm is fed with different global radiation values during its learning phase and its forecasting quality is evaluated. To this end, the algorithm is supplied with a global radiation value from each cluster. We can note, that the difference in PV feed-in the context of laboratory tests influences the voltage profile and thus also the learning behaviour of the algorithm. In a realistic environment, the handling of HEC profiles may differ. This will be validated in the field tests.

7.6.3. Test R‐1: Cycle test Goal:

The goal of the experiment is to test the operation of the Smart Operator for several hours. The time for the test is set to 6 hours. So this is an endurance test.

Description:

There are different network conditions defined and tested, to create both: grid situations in normal operation and in faulty operation.

Results: The cycle test proves the functionality of the process control. The read and write functions of the application access library worked as expected. The values were read successfully for the different functions, converted (unit) and algorithmically treated properly. The interpretation was appropriate and correct. The log functions were correctly written to the analysis system (HEC - information not available). The Smart Operator responds correctly to voltage ligament injuries. The figure shows the maximum and minimum voltage during the course of 6 hours. It can be seen that the voltage ligament injuries occurring continued only for a short time, because the smart operator can find a suitable solution within this minute in order to remedy the violation. However, even here the effects can be seen when the grid model, with which the load flow calculations are done, is not 100% consistent with reality (see 90-100 minutes in the figure). The smart operator then expects a different network situation and cannot fully exploit its potential.


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Field test:

In the field test the operation of the SmOp 7/24 is tested. The switching recommendations are recorded and logged for evaluation. Limitations on DER and EV are reported separately. Min-Max-Voltage

250 U

max

[V]

U

min

[V]

U

Ref

[V]

U

Grenz

[V]

245

240

235

230

225

220

215

210 0

50

100

150

200 time [min]

250

300

350

Figure 35 ‐ Voltage during test R‐1

7.6.4. Test R‐2: Functional test Goal:

The components should generally be tested for their functionality. They should behave according to their specification. The metering by the Smart Operator components is compared with the reference measurement system. The calculated voltage profile of the load flow calculation is analysed. Thus, three different voltage profiles are compared.

Description:

In this experiment, a normal operation is being simulated. The load banks and the feeding of the laboratory system are controlled in such a way that they only generate low voltage deviations.

Results:


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A comparison of the Smart Operator measurement system, the reference laboratory measurement system and the Smart Operator load flow calculation provides a plausibility check. As an example minute 30 is selected and compared.


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Table 31 ‐ Comparison of electrical dimensions referring to test R‐2

SmOp

Reference

SmOp

meter

meter

calculation

Kn2

230.41

230.80

230.41

Kn3

230.74

230.96

230.65

Kn4

236.26

236.32

236.45

Kn5

229.80

230.37

230.22

Kn6

228.47

229.95

230.21

Kn7

228.39

229.97

230.21

The comparison of the voltage magnitudes shows a difference of less than 1%. Deviations occur due to measurement inaccuracies and inaccuracies of the network image.

Field test:

In the field test the plausibility of measurements on different locations in the LV network is regularly reviewed.

7.6.5. Test R‐3: Stable state Goals:

The smart operator should follow its forecast derived from the trained profiles. The pre-set power profiles of the load banks are similar to the profiles used during training of the load banks.

Description:

See test number R-2. A global radiation

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