A Virtual Microgrid Platform for the Efficient Orchestration of Multiple Energy Prosumers Ioannis Mamounakis
Dimitrios J. Vergados
Prodromos Makris
Computer Technology Institute 26504, Rio Patras, Greece
Computer Technology Institute 26504,Rio Patras, Greece
Computer Technology Institute 26504, Rio Patras, Greece
[email protected]
[email protected]
[email protected]
Emmanouel Varvarigos
Tasos Mavridis
Computer Technology Institute 26504, Rio Patras, Greece
Intelen LLC 15124, Marousi Athens, Greece
[email protected]
[email protected]
ABSTRACT The Smart Energy Grid concept aims to exploit Information and Communication Technologies (ICT) towards making the energy sector more secure, reliable and efficient, while the electricity markets are rapidly becoming more liberalized with new business actors/models being introduced. In particular, passive energy consumers are being transformed into active energy prosumers (i.e. both producers and consumers), while energy aggregation/services companies are emerging as intermediaries in the so called “Internet of Energy” arena. Prosumers need to have their energy assets efficiently managed and participate in the market independently of their size and negotiating power, while aggregators aim at maximizing prosumers’ benefits by representing them as a single big power entity in the wholesale energy market. This paper introduces the Virtual MicroGrid (VMG) concept, in which multiple energy prosumers are orchestrated into bigger associations towards optimizing the association’s benefits. An innovative decision support system platform is presented showcasing that the management of aggregated energy resources can outperform state-of-the-art solutions that manage resources at the individual prosumer’s level. The platform’s implementation is based on virtualization techniques and a wide range of functionalities are described, tested and validated. Datasets from 37 real-life prosumers are used and results of various decision-making algorithms show that under different system operation contexts, dynamic formation of prosumers’ groups (clusterings) can provide remarkable energy savings and monetary profits to the end users.
Categories and Subject Descriptors H.3.4 [Systems and Software]: Distributed systems, Information networks, Performance evaluation, User profiles H.4.2 [Types of Systems]: Decision Support Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
[email protected]. PCI 2015, October 01-03, 2015, Athens, Greece (c) 2015 ACM. ISBN 978-1-4503-3551-5/15/10…$15.00 DOI: http://dx.doi.org/10.1145/2801948.2802012
General Terms Design, Management, Measurement, Performance, Algorithms.
Keywords Smart Energy Networks, Microgrids, Virtualization, Decision Support System, Energy Prosumer, Aggregator, Genetic Algorithms.
1. INTRODUCTION In the context of a rapidly growing electricity market, renewable energy is produced and distributed through different medium and small producers, who sell their renewable energy via appropriate electronic platforms operated by market operators (MOs). However, such an approach prevents some of the small or very small producers, from participating in the electricity market, and requires them to be organized in bigger energy associations. In this case, the main difficulties are the administration of the large number of heterogeneous Renewable Energy Source (RES) prosumers (i.e. participants that both produce and consume energy) and the ability to succeed interoperation as each prosumer contributes, an infinitesimally small amount of energy to the electricity grid trying to maximize its own profit. As a result, the effective administration and orchestration of large clusters comprised of small energy producers necessitates the exploitation of new ICT technologies [1]. In the energy and telecommunication sectors, new decentralized operational models must be defined. The main research breakthrough being proposed in our paper is the idea to exploit ICT research and infrastructure in order for the creation of Virtual-MicroGrids (VMGs) to be realized. VMG creation under a highly dynamic and distributed framework will override the traditional centralized day-ahead electricity market [2]. A large variety of energy management platforms combine bill and meter data analysis, to achieve efficient operation of converged energy/ICT business processes. These microgrids related frameworks enable services that integrate and control distributed energy generation assets to form a highly responsive intelligent microgrid. In order to realize the challenges and corresponding solutions regarding the aggregation of distributed energy producers, many frameworks appear in the literature. As outlined in [3], a service-oriented architecture is proposed in order to integrate MG modeling, monitoring, and control. In [4], authors implement a microgrid management system to evaluate
the management and control under system performance and scheduling.
2. OVERALL VIMSEN ARCHITECTURE AND SYSTEM OPERATION
However, in a microgrid association it is important to achieve the flexibility to re-distribute energy resources among each other to compensate for energy production-distribution and to participate in the electricity market through the respective association, which acts similarly to a big power generator unit.
The DSS platform is responsible for orchestrating and efficiently managing the RES of multiple energy prosumers called as VIMSEN Prosumers (VPs). It is also responsible for enabling the decentralized electricity market supported by VIMSEN project. Furthermore, it provides the necessary information tools and service engineering methodologies (based on the Web service technology) to choose the microgrids and the distributed energy sources within the VMG framework, to handle the respective pricing policies and finally to activate the dynamic clustering framework in order to update and evolve VMG associations according to the current energy information, forecasting and demands. Finally, novel algorithms regarding the VMG groups’ formation/dynamic adaptation and VMG profiles’ management are designed developed and are integrated in the DSS platform. These algorithms take advantage of an innovative hybrid cloud computing processing infrastructure (HCCI), which allows heavyprocessing tasks/jobs to be executed in less time and thus realtime decision making procedures to be realized, which is very important for the successful operation of the whole VIMSEN system.
In the specialized literature, there are some works related to energy management platforms for microgrids. In [5], Tsikalakis and Hatziargyriou describe a centralized control system for a microgrid. The controller is used to optimize the operation of the microgrid during interconnected operation. Two market policies are assumed to offer options for the demand for controllable loads, and this demand-side bidding is incorporated into the centralized control system. Dagdougui et al. [6] describe a realtime operational management tool for a hybrid renewable system, composed of an electrolyzer, a hydroelectric plant, pumping stations, a wind turbine and a fuel cell. The goal is to satisfy the variable electric, hydrogen, and water demands hourly. Korpas andHolen [7] present an energy management system for a hybrid plant with wind power and hydrogen storage in order to maximize the expected profit from power trading in a day-ahead market. As opposed to [5] we propose a real time market pricing model which is based on real market prices. Author’s research in [6] is based on the renewable energy production values, in contrast to our proposed platform which takes into account both production and consumption measurements. Finally in contrast to [7] our platform maximizes the expect profit in two different energy markets (i.e. day-ahead, intra-day). In this paper we propose a VMGA-DSS Smart Energy Management Platform (proposed within the VIMSEN project [8]), which monitors and controls renewable energy prosumers, via a web interface. The main objectives of the VMGA-DSS platform are to: a) acquire real and forecast data from individual prosumers, b) interface with market operators and agree on specific contracts/service level agreements (SLAs) to be executed in a pre-defined timeframe, c) design, develop and validate information management and decision making technologies for the dynamic VMG infrastructure creation, d) communicate the results of the SLA back to each prosumer in order for specific demand response (DR) actions to be enforced. e) Participate in liberalized electricity markets following the EU regulatory framework defined in [9]. In order to validate the VMGA-DSS platform functionality we propose and present the experimental results of a genetic algorithm that tries to orchestrate the prosumers in the system into Virtual MicroGrids (i.e. clustering), in a way that limits their lost revenue due to inaccurate energy forecasts. The paper is structured as follows: in section 2, we present an overview of the proposed VIMSEN architecture and the system operations. In section 3, we describe the web platform functionalities. The energy cost model that is considered is presented in section 4. In section 5, the proposed approach for prosumer clustering is presented accompanied to the experimental analysis results. Finally, section 6 concludes this paper and presents some future research insights regarding our platform’s exploitation opportunities.
Figure 1. The VIMSEN Architecture The VIMSEN operation is illustrated in seven (7) basic steps in figure 1: 1.
Real-time data measurements are sent by All VPs (through their local VIMSEN Gateway - VGW) to the Energy Data Management System (EDMS), which acts as a VIMSEN data repository.
2.
The EDMS component acts as a single meter data repository which sends VMG measurements in various timeframes to the Forecasting and Modeling System (FMS) and the Decision Support System (DSS) upon request.
3.
The FMS exploits VIMSEN external data sources and provides predictions of future states of the VMG associations. Prediction results about weather forecasts are received from online Weather Operators (WO) and
sends VIMSEN-related forecasts to DSS for a given VMG association upon request. 4.
The DSS (see outlined area in figure 1) decides about the optimal VMG group formations to satisfy given objectives and constraints being set by market/grid operators such as the Market Operator (MO), the Distribution System Operator (DSO), the Transmission System Operator (TSO), the Balance Responsible Party (BRP), etc.
5.
Service Level Agreements (SLAs) at a VMG level are sent from the DSS to Global Demand Response Management System (GDRMS) to be “broken down” to individual SLAs per VP.
6.
The GRDMS performs automatic control policies at a VMG level and sends DR commands and energy management recommendations to each VP.
7.
The VGW receives the DR action messages via its “local DR manager” module and is responsible for implementing the actions at a VP level.
It should be noted that the Virtual MicroGrid Aggregator (VMGA) acts as an intermediary market entity between a vast number of VPs (lower level) and the various market/energy actors (higher level). Therefore, VMGA is able to interact with the: a) Market Operator (MO) in order to participate in day-ahead and intra-day electricity markets, b) Balancing Responsible Parties (BRPs) in order to participate in balancing market and c) DSO/TSO for participating in energy efficiency certificates’ (or else demand response) market.
Upon a DSS user’s request, historical data is acquired from the database via a RESTful API. In case real-time data is requested, the DSS component uses a web API to retrieve the appropriate data from EDMS. Then, it stores the data to DB and figures are visualized in a Configuration Panel (CP) via a RESTful API. The data visualization functionalities of the DSS consist of presenting different types of data to the DSS users. Different functionalities have been implemented, to allow for viewing, plotting and extracting relevant prosumption data, including historical and real-time views, individual or aggregated prosumption profiles, and different types of measurements, such as prosumption, production and consumption, as well as at different timescales (hour/day/week/month/year). As shown in Figure 2, real-time data can be streamlined for a specific time period in order for the end user to be able to continuously monitor the real-time operation of an individual VP.
3.2 Aggregated data visualization The DSS user is able to request day-ahead forecasts from the FMS regarding a single VP or a group (cluster) of VPs. These forecasts are then used as baseline prosumption profiles by DSS to make its offers in the day-ahead market based on the pricing signals generated by the MO. In the automated case, forecast prosumption profiles are used by DSS algorithms for the creation of a VMG infrastructure. The result is that aggregation of VPs can considerably reduce the deviations between forecasts and real-life measurements and thus respective SLAs agreed with the MO can be better met. The DSS user can visualize the monetary profits that the aggregation of VPs can provide to individual prosumers as a result of their participation in VMG associations.
3. WEB PLATFORM FUNCTIONALITIES This section describes some basic functionalities of the VMGA DSS platform. In particular, four main DSS operations are showcased, namely: a) DSS user can visualize aggregated historical and real-time data for multiple VPs, b) DSS user can visualize forecast vs. real data for multiple VPs, c) DSS user can visualize open energy data published by a MO, and d) DSS user can create and adapt a VMG association to accomplish a given goal. In the following subsections, a brief description is given regarding each capability of a DSS user offered by the platform accompanied by respective illustrative figures.
3.1 Real time data visualization
Figure 3. Real vs forecast aggregated data Furthermore, the DSS user is able to request short-term forecasts from the FMS. The timeframe of these forecasts is considered to be 15 minutes being thus exploitable by DSS user to manually adapt existing VMG infrastructure in order for VPs to be able to participate in the various variants of the intra-day market (e.g. Italian MO with which VMGA interacts, realizes five different types of intra-day markets [10]). In figure 3, all profiles of individual VPs (both forecast and real) are illustrated accompanied by aggregated profiles for the randomly selected group of VPs. As shown at the upper side of the figure, the selected VPs are located in Greece.
3.3 Open Energy Market data capabilities Figure 2. Real-time data interface for an individual VP
Regarding market-related data, this is retrieved from the MO (DRrelated data could be retrieved from an energy system operator, too) upon the receipt of a new electricity market event (e.g. day-
ahead, intra-day, etc). The market operator is responsible for providing the necessary data that allow the system to participate in buying and selling energy. In order to participate in the market, the DSS platform may submit bid requests to the MO’s Internet platform according to the forecast prosumption profiles of its portfolio. DSS is able to acquire historical data regarding the volume of exchanges and the bid/ask prices in the electricity market, while DSS can also be informed about the market clearing prices published by MO. Other relevant data from the market may be the total demand and supply of energy, including historical data, as well as real time information. In particular a DSS user can select via CP: a) an MO (e.g. Italian, Greek, Irish, Nordic Pool, etc), b) specific geographical region (e.g. Sardinia in Italy), c) a specific timeframe (day, week, month, year, etc), and d) an electricity market variant (day-ahead, intra-day, energy efficiency certificates market, etc). This open data is stored in a database (DB) and is mainly used as input for many DSS algorithms. Upon a market-related event published by MO, DSS platform should be able to respond accordingly by providing RES offers via the aggregation of individual VPs in the residential sector. The DSS acquires the notification from the MO and a message is also displayed at DSS user’s side (CP). In figure 4, day-ahead and intra-day prices examples are given for Sardinia in Italy by the Italian MO [10].
(CP). The prosumer organization into virtual microgrids (VMGs), consists of intelligent algorithms execution that will a) determine the optimal timing periods for buying and selling energy in the market, b) determine when is the appropriate quantity and appropriate price to participate in the market, in a way that maximizes the revenue from producing renewable energy, while c) maintain high-standard energy security. The creation of a VMG association may be subject to a regulatory constraint (e.g. the “entry barrier” constraint is used in many countries where a minimum amount of energy is required for a VMG to enter the market). The user can visualize the results of different VMG infrastructure creation scenarios and compare them to conclude to the most efficient one for each type of event. Finally, as shown in figure 5, DSS user is able to “edit” an existing VIMSEN association (e.g. add/remove a VP or adapt some VMG configuration parameters). The DSS user can visualize the results in real-time and conclude to an efficient VMG infrastructure that meets all his/her managerial/technical pre-requisites.
4. THE MARKET PARTICIPATION MODEL For prosumer i < N, where N is the number of prosumers associated with the VMGA aggregator, we have the real prosumption in each hourly block t denoted as:
ri (t) consi (t) prod i (t)
(1)
where consi (t) is the amount of energy consumed by prosumer i at hourly block t, and prod i (t) is the corresponding energy production. The forecasted prosumptions are f i (t ) at each hourly block t. The forecast is calculated 24 hours prior to the corresponding hourly block.
4.1 The energy cost of an individual prosumer Figure 4. Open data acquisition from a Market Operator
3.4 VMG association management
We assume that each prosumer participates in the day ahead energy market, buying or selling energy depending on the forecasted prosumption. The revenue/cost caused by the participation in the day-ahead market is calculated based on the amount of energy that is traded. Thus, the associated cost is:
cida (t ) fi (t ) p* (t )
(2)
where p* (t ) is the energy price per unit at hourly block t, obtained from the day-ahead market. If the forecasts always reflected accurately the true net amount of energy that is produced or consumed, eq. (2) would give the actual cost/profit for each prosumer. When there is a difference between the amount of energy traded in the day-ahead energy market and the amount that was actually produced or consumed, then the prosumer is charged with a penalty. We assume that two penalty factors are being used: a) ps , when the real prosumption exceeds the forecast, and b)
pv , when the prosumption is below the forecasted value. Figure 5. List of various “clusters” (i.e. VMG associations) that were created using our genetic algorithm. A DSS user can create a VMG infrastructure by selecting and configuring a plethora of parameters via the Configuration Panel
Thus, according to our model, the actual cost may be calculated using the following formula:
ci (t ) fi (t ) p* (t ) (ri (t ) fi (t )) (1 pv ) p(t ) if ri (t ) fi (t ) or by
(3)
ci (t ) ri (t ) p* (t ) ( fi (t ) ri (t )) ps p(t ) if ri (t ) fi (t )
the system may achieve for its prosumers. A microgrid will have the most benefit if it manages to make the term:
where p(t ) is the price of energy in the spot market. In case the real prosumption exceeds the forecasted one, the prosumer is charged for the amount of energy he forecasted to produce, at the day-ahead market price plus a penalty that is proportional to the deviation, and to the spot market price. In case the real prosumption is lower than the forecasted one, the prosumer is charged for the net energy that he consumed, at the day ahead market price, plus a penalty that is proportional to the deviation, and to the spot market price. We define the estimation error as the difference between the real and the forecasted amounts of energy:
ei (t ) ri (t ) fi (t )
(4)
f jka u (ei (t )) t
as small as possible in relation to term
f jkb u ( ei (t))
where ei (t) is the error in forecasting the prosumption of prosumer i for hourly block t, and mk defines the clustering that is applied, through the following equation:
i mk g ji k (5)
(6)
where di (t ) f i (t ) p(t ) is the cost of energy if no penalties were applied.
5. A GENETIC ALGORITHM APPROACH FOR PROSUMER CLUSTERING In this section we present an algorithm for generating optimal clusters using genetic algorithms. We will present the definition of a chromosome for the specific problem that we are examining, and we will define the fitness function that is used to determine how suitable each candidate solution is.
f (s j )
In order to use a Genetic Algorithm for solving the prosumer clustering problem we first have to define the genetic representation, i.e. the structure of the chromosome that will be used to solve the optimization problem. In the case of prosumer clustering, we represent each solution as a vector of size N, where N is the number of prosumers that are to be clustered into virtual microgrids. Thus, solution Sj may be written as:
where
f jkb f jka
1 k K mk 0
f jkb
(11)
5.3 The mutation function The mutation function is essential for maintaining genetic diversity from one generation of a population of chromosomes to the next one. The method that is applied in our genetic algorithm implementation swaps the position of two consecutive genes.
5.4 The crossover function The crossover function is used to combine two chromosomes (solutions) into a single new chromosome. There are several ways to combine two chromosomes, such as one point crossover, twopoint crossover, cut-and-splice, edge recombination, etc. We used the two-point crossover to combine our chromosomes. According to this method, two random numbers 1 x1 , x2 K are selected. The new chromosome consists of the first parent’s genes, for
5.1 The genetic representation
S j g j1 , g j 2 ,...., g jN
(10)
Finally, the fitness function is defined as:
Finally, if we assume that the energy price for each hourly block in the day ahead market is equal to the cost the spot market, then equation (3) can be simplified to:
ci (t ) di (t ) u (ei (t )) p(t ) ,
(9)
imk
t
Then we define the penalty function as:
| ei (t ) | pv if ei (t ) 0 u (ei (t )) | ei (t ) | ps if ei (t ) 0
(8)
imk
indexes between
x1 and x2 ,
and the second parent’s genes for
indexes outside the above interval.
5.5 Evaluation In order to evaluate the performance of the genetic clustering algorithm, we tested it on data from 37 prosumers located in Greece. The prosumption data were provided by Intelen [11].
(7)
g ji K is the id of the cluster where prosumer k is
assigned, and K is the maximum number of clusters.
5.2 The fitness function The fitness function in a genetic algorithm determines how good or bad one solution is compared to another one. It should be defined in a way that better solutions have a higher fitness value than worse solutions. The objective of the clustering algorithm is to orchestrate the prosumers into clusters in a way that minimizes their costs, by reducing the uncertainty in the estimation of their prosumption, and thus reducing the penalties that they will be charged with. Thus the fitness of a solution will depend on the percentile improvement in the penalty costs that each cluster in
Figure 6. The evolution of the genetic algorithm
The configuration parameters of the genetic algorithm are following: the initial population was 200 randomly created chromosomes, and the genetic system evolved for 100 generations. The number of clusters was set to 5. The penalty factors ps and pv were set to 0.2 and 0.3 respectively. The fitness of the best solution at each generation of the training is depicted in Fig. 6. We used the prosumption data of the week 24/3/3015 to 31/3/2015 in order to train the clustering algorithm. The clustering that was formed consisted of the allocation shown in Table I. Table 1. The clustering of the prosumers into clusters, using the proposed genetic algorithm Cluster name
Cluster members
Gen 0
3, 16, 18, 19, 20, 23, 25, 28, 29, 30, 31
Gen 1
1, 8, 17, 21, 24, 26, 32, 33
Gen 2
4, 7, 27, 36, 37
Gen 3
2, 5, 6, 9, 11, 12, 13, 14, 34, 35
Gen 4
10, 15, 22
For the following 5 weeks, we tested the above clustering with new forecasted and real prosumption data. For each of the following weeks, we calculated the cost according to our model considering the clustering that was calculated using data from week 0. The results are depicted in Figure 7.
forecasting errors. The testbed results show that, given the prosumption model presented, the proposed clustering method may achieve significant benefits in cost reduction for the end users. In future work we plan to integrate in our ICT platform an even larger set of real prosumer data combined with more and more datasets from MOs and BRPs. Finally to extend the platform’s capabilities in order to support a large variety of optimization algorithms, for optimizing various aspects of the microgrid operation.
7. ACKNOWLEDGMENTS The work presented in this paper has been undertaken in the context of the project VIMSEN (VIrtual Microgrids for Smart Energy Networks). VIMSEN is a Specific Targeted Research Project (STREP) supported by the European 7th Framework Programme, Contract number ICT-2013.6.1 - 619547.
8. REFERENCES [1] X. Fang, S. Misra, G. Xue, and D. Yang, “Smart Grid The New and Improved Power Grid: A Survey”, IEEE Communications Surveys & Tutorials, vol. 14(4), pp. 944980, 2012. [2] G. Lyberopoulos, E. Theodoropoulou, I. Mesogiti, P. Makris and E. Varvarigos, “A Highly-Dynamic and Distributed Operational Framework for Smart Energy Networks”, proceedings of IEEE CAMAD 2014, pp. 120-124, 1-3 December, Athens. [3] A. Vaccaro, M. Popov, D. Villacci, and V. V. Terzija, “An integrated framework for smart microgrids modeling, monitoring, control, communication, and verification,” Proc. IEEE, vol. 99, no. 1, pp. 119–132,2011. [4] W. Shi, E.-K. Lee, R. Huang, C.-C. Chu, R. Gadh, Evaluating Microgrid Management and Control with an Implementable Energy Management System, IEEE Smart Grid Communications, Nov. 2014. [5] A. Tsikalakis and N. Haatziargyriou, “Centralized control for optimizing microgrids operation,” IEEE Trans. Energy Convers., vol. 23(1), pp. 241–248, 2008.
Figure 7. The penalty reduction of each cluster for the weeks following the training g period More specifically, we can see the performance of the clusters that were created using the genetic algorithm, for the 5 weeks following the training, in terms of the percentile reduction in the penalties for each cluster. We observe that the penalty reduction is largely retained for the following weeks in most cases. The higher reduction that is observed in this experiment reaches up to 50%, Whereas even in the worst case, we observe a positive reduction of a few percentage points.
6. CONCLUTION In this paper we presented an innovative decision support platform for optimizing the participation of distributed energy prosumers into the future smart grid. A testbed platform that was developed for this purpose was presented, and a demonstration of the platform’s capabilities was showcased through the example of a genetic algorithm, that tries to reduce penalties caused by
[6] H. Dagdougui, R.Minciardi, A. Ouammi, M. Robba, and R. Sacile, “A dynamic decision model for the real-time control of hybrid renewable energy production systems,” IEEE Syst. J., vol. 4(3), pp. 323–333, Sep. 2010. [7] M. Korpas and A. Holen, “Operation planning of hydrogen storage connected to wind power operating in a power market,” IEEE Trans. Energy Convers., vol. 21 (3), pp. 742– 749, 2006. [8] EU FP7-ICT-2013-11 VIMSEN STREP Project website, http://www.ict-vimsen.eu/ [9] EU Directive 2009/28/EC of the EU Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30, 2009. [10] Italian Market Operator portal, Gestore Mercati Energetici (GME), http://www.mercatoelettrico.org/En/Default.aspx [11] Intelen Company, www.intelen.com
