DG DemoNet Validation: Voltage Control from Simulation to Field Test

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DG DemoNet Validation: Voltage Control from Simulation to Field Test M. Stifter, Member, IEEE, B. Bletterie, Member, IEEE, H. Brunner, D. Burnier, H. Sawsan, F. Andrén, R. Schwalbe, A. Abart, R. Nenning, F. Herb, and R. Pointner  Abstract—Refinements of the voltage control algorithm for the DG DemoNet concept have been developed extensively over the last years. Consequently the next step will be field tests prior to the deployment. This paper describes the (re-)design of the existing prototype algorithm, offline and online simulation environments and testing of the implementation to prepare the central voltage control unit for the field test. The communication links – PLC and radio link – and the MV grids – ‘Großes Walsertal’ and ‘Lungau’ – impose different challenges for the validation of the voltage controller. During the porting of the prototype to the production implementation, the algorithm has run through a major code revision and re-design, to ensure a more general and modular approach for the voltage control algorithm. Index Terms— Coordinated Voltage Control, Distributed Generation, Grid Integration of Renewable Energy Sources, Simulation, Voltage Control

I. NOMENCLATURE DG: distributed generation DER: distributed energy resource OLTC: on load tap changer CVCU: central voltage control unit DMS: distribution management system COM: component object model II. INTRODUCTION

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oltage Control in medium voltage networks is considered as an effective solution to integrate high number of distributed and renewable generation into the grid. Different concepts have been developed and extensively elaborated and simulated within the DG DemoNet project series. Few field tests have been conducted to verify the proposed effects on voltage control and only long term studies will validate the simulation results for different seasonal and operational situations.

This work was supported by the Austrian Climate and Energyfonds. M. Stifter, B. Bletterie, H. Brunner, D. Burnier, H. Sawsan, F. Andrén are with the Energy Department, Business Unit Electrical Energy Systems, AIT Austrian Institute of Technology, 1210 Vienna, Austria e-mail: {given name.last name}@ait.ac.at. R. Schwalbe is with Vienna University of Technology, Austria A. Abart is with Energie AG Oberösterreich Netz GmbH, Austria R. Nenning and F. Herb are with VKW Netz AG, Bregenz, Austria R. Pointner is with Salzburg Netz GmbH, Austria.

III. RELATED WORK A. Voltage Control Algorithms Different approaches for controlling voltage with a central OLTC transformer and contributions of reactive power from generators exist. They can be divided basically between central and decentralized control and between coordinated, distributed and local control. In general it can be noted that many of the related projects and concepts include a specialized information and communication technology (ICT) concept for the voltage control approach. This can optimize the performance, whereas an advantage of the solution in DG DemoNet is its independency of the communication solution. 1) DG DemoNet: Several years of development and refinement of the controller algorithms have passed since the start of the first development of the DG DemoNet voltage control concept [1]. It evolved from several state machine approaches [2], refined with interval arithmetic [3] to the current state. 2) Crisp Within this project a concept has been developed for controlling the tap changer based on abnormal voltage variations on the high voltage side of the power transformer, to satisfy constraints on voltage magnitudes [4]. The developed voltage control only uses the tap changer of the transformer. Therefore only shifts of the whole voltage band are possible, whereas the concept of DG DemoNet also allows for control of the voltage band range using DG. 3) Integral A coordinated control concept by modulating the reactive power injection for the distributed generation units to maintain the voltage profile, with additional constraint for the losses, is described. The main focus in the Integral project is the development of ICT for the Smart Grid using a Multi Agent System (MAS). This introduces the possibility to optimize the performance of the grid not only in regard to the voltage profile but also for network losses, power demand management, optimal power flow, etc [5]. This also means that the coordinated control concept shown is only a small part in the project. The Integral project and the DG DemoNet project also differ in the use of the OLTC transformer, which is used in DG DemoNet but not in the Integral project. When only distributed generation is used for voltage control only over-voltages can be handled, whereas with an OLTC transformer also under-voltage can be handled.

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4) Aura-NMS The two main tasks for distributed voltage control concept in this project are to ‘Identify Voltage Excursion (IVE)’ and ‘Voltage Excursion Relief (VER)’ which rely on the available measurements and the state estimator. Static – updated only if network status changes – and dynamic network data – update every 5 minutes are used [6]. Compared to DG DemoNet the results of a control step are similar, i.e. changes in set points for OLTC tap position and for generation of active and reactive power for each distributed generation. However the implemented methods to reach these results are completely different, where Aura-NMS uses a case based reasoning technique and DG DemoNet uses an optimization approach with regard to the voltage band and the reactive power flow. 5) Adine A coordinated voltage control was developed using two control measures: Changing tap changer position and reactive power production of distributed generation. Either tap changer or distributed generation can be used as first control measure. If the first control measure is not possible or has reached its limits the second measure is used. The new set points for reactive power are distributed to the DGs using the existing SCADA system of DNO [7]. The method used in Adine is very similar to DG DemoNet, however the active power is not used as a control measure in Adine, which can be seen as a disadvantage since active power has a relative high influence on the voltage in medium voltage networks. B. Real time Simulation and Field Tests A verification of a coordinated voltage control implementation based on state estimation in conjunction with real time simulation has been demonstrated in [8]. Also field test has been conducted and the controller has been interfaced to a DMS/SCADA system.

1) Development of coordinated voltage control During the first phase of the project an algorithm for the control by a CVCU by means of an OLTC based on monitoring values of critical nodes together in coordination with reactive and active power control of DG units has been developed (Fig. 2). To indicate the deviation of the voltages of the critical nodes with respect of the active and reactive power of the controllable DGs, the contribution matrix approach was introduced. Based on the contribution matrix a constraint linear optimization is used to calculate the set points and schedule the contributions. The set points of the reactive power output of the generation units can be controlled via communication lines and telecontrol protocols. On the generation site, the local controller or telecontrol head-end has to transfer the set points to the DG’s internal generator controller. 110 kV

110/30 kV

CVCU

30 kV

M2

DG4

DG1 DG5

DG1

DG6 DG2 M1

IV. CONTROLLER EVOLUTION A. Evolution Starting from 2006 the DG DemoNet voltage control algorithm has been constantly improved. The mayor steps in this process are briefly described. Fig. 1 depicts the voltage band and the definition of the voltage range. Voltage range umax(t)-umin(t) highest network voltage UUL

ULL lowest network voltage Fig. 1. Voltage band and range defined by the lowest and highest critical node voltage values

M3

DG3

Fig. 2. Coordinated voltage control: OLTC set point is based on critical nodes in coordination with an optimized reactive power management of DG [10]

Additionally the local controller can operate the DG unit in several modes: fixed reactive power, reactive power according to a given profile, reactive power according to the actual voltage level, or active power according to an emergency maximum voltage. In case of a controller fault or communication loss, the local controller is configured to operate after one of this strategy. Details about this concept and the distributed, local and the coordinated voltage control can be found in [9]. 2) Advancing the voltage control strategies The prototype for offline simulation has been developed on base of a state machine concept to implement the hierarchical controller step model. First the tap changer is used to control the voltage from the critical nodes. If the optimization with the

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reactive power has a solution, a new set point for the reactive power demand is transmitted to the controllable generators (“Q regulation”). If this measurement alone is not sufficient, the controller runs an optimization with reactive and active power reduction to bring the voltages of the critical nodes back within the acceptable voltage band (“PQ regulation”). Constrained optimization (nonlinear, convex) is used to in order to optimize the control actions during “Q regulation” and “PQ regulation”. A detailed description of the control strategy can be found in [2]. 3) Interval arithmetic approach The contribution matrix is a linearized model of the distribution network and valid in a broad range of operation. Uncertainties due to seasonal time variants have been accounted by the constitution of interval arithmetic methods. By these more general models using interval matrices, safer results at the expenses of more complex implementation and more time consuming solution algorithms can be achieved. B. The New Level and Range Controller Concept The control strategy is based on two separate objectives: range control – keep the spreading of the maximum and minimum voltage within the accepted voltage band, regardless of the absolute voltage values – and the level control, which shifts the voltages into the upper and lower voltage limits once the spreading is small enough. The tasks are separated. One keeps the range (spreading width) within the allowed voltage band range and the other task is to bring this range to the right voltage level, so that neither upper limit nor lower limit is violated. Fig. 3 shows the new design of the two control strategies. Configuration - critical nodes - DGs - contribution matrix

Parameter - Ulimits - ...

Usetpoint

Level Control Usetpoint = ...

Tap changer

DQ, DP

Range Control Q (PQ) Optimization

Communication (PLC, radio link)

DG DemoNet-Controller (Central voltage control unit – CVCU)

Fig. 3. Level and Range controller concept.

1) Level Controller The level controller evaluates if a shift of the level of all voltages is necessary. A shift is done by providing a set point to the controller of the tap changer. A voltage spreading width smaller than the allowed (effective) voltage band is required before the level controller sets an action. The voltage set point for the tap change controller is determined by (1): VB  Rng U set UUL   (u u ) (1) max curr 2 Rng  u

max

u

min

( 2)

Where UUL is the voltage upper limit, VB the voltage band (room available between the upper and the lower limits), Rng the range between umax and umin as the highest and lowest voltage measurement respectively and ucurr the current voltage at the lower voltage side of the transformer. In Fig. 4 five different scenarios (represented by a column) of the level control algorithms are demonstrated with the voltage range (in blue) before (left side of the column) and after correcting the tap changer voltage set point (right side of the column). The set-point for the OLTC as well as the upper and lower controller limits are represented in red. It can be seen that the OLTC actions bring the current voltage into the controller range.

Fig. 4. Voltage band before and after an action of the level controller.

2) Range Controller The optimization task for the range controller is given with the objective function to minimize the total amount of reactive power which is necessary to bring the voltages within the available voltage band according to (3) with the nonlinear constraint functions given in (4)-(6). Equation (3) represents the objective function allowing a minimization of the amount of reactive power which must be provided or consumed by the generators in order to limit the contribution of generators to those which are absolutely necessary. The formulation with the sum of the squares has been chosen for performance reasons of the optimization tool. Inequalities (4) and (5) reflect generator constraints: the minimum power factor and the maximal apparent power respectively. Equation (4) should basically reflect the generator P-Q diagram, but a simple minimal power factor has been assumed here since it is required by the DNO. Equation (6) is the inequality ensuring that the voltage stays within the limits. The equation ensures that the spreading of the voltages is always smaller than the effective voltage band (EVB) which is the available voltage band (room between the upper voltage limit and the lower voltage limit) minus a deadband which is needed for the OLTC controller and a reserve which has been introduced for uncertainties.

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n 2 min  (Qi  DQi ) DQ i1

(3) Range < Voltage band?

Qi  DQi  Pi tan(arccos ( PFmin )) Qi  DQi 

( 4) yes

2 2 S n  Pi

(5)

max(U i  AQDQi )  min(U i  AQDQi )  EVB EVB  VB  DU

no Overvoltage? Undervoltage?

( 6)

yes

 DU

(7)

RANGE - EVB (p.u.)

RANGE - EVB (p.u.)

DB reserve In these equations, Qi is the current reactive power value, ∆Qi the reactive power variation, Pi the current active power value, Sn the nominal apparent power, Ui the current voltage value, AQ the contribution matrix, ∆UDB the voltage dead band and ∆Ureserve the voltage reserve, according to the network configuration. Fig. 5 shows an example simulation for a whole year with and without range controller. On this figure, the range (spreading between the voltages) is compared to the effective voltage band. If the difference between both is positive, the level controller (based on OLTC operation) does not allow maintaining the voltages within the limits and the range controller must compute reactive power set-points for the generators. The lower part of the figure shows that the range controller succeeded in relieving the voltage violations.

Over / Under voltage Range within Voltage Band

no Optimize Q(P) Umax – (Rng – VB)

Voltage violation

no solution

Calculate Set Point

Optimize with Violating limits

solution

possible not possible

Set new Q(P)

Alarm

Alarm

(Range)

(nTap max/min)

Set Uset

Wait for new tap position

Wait for new Q(P) setting

Fig. 6. Simplified flow diagram of the controller algorithm.

V. DESIGN AND IMPLEMENTATION

0.02 0 -0.02 -0.04 -0.06 0.02 0 -0.02 -0.04 -0.06 0

Q (kvar)

Range > Voltage Band Q(P) active

-500

-1000 JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

Fig. 5. Simulation with and without range controller for the MV in “Lungau”.

3) Algorithm The simplified algorithm of the level and range controller is illustrated in Fig. 6. If the voltage range is smaller than the allowed voltage band the reactive power optimization is performed continuously until the spreading width of the voltage range fits into the voltage band. If a voltage violation occurs, the voltage range can be shifted into the voltage band by the tap changer, setting the appropriate voltage set point for the tap changer controller. Part of the range control algorithm is the optimization step which finds the minimum of the reactive power contribution and active power reduction. This problem is convex and in case of there is a resulting optimum it is always a global optimum. An algorithm for this optimization problem has been implemented. Basically it determines the direction for finding the minimum by calculating the negative gradient of the minimum function until it bumps into a constraint, stated by a hyperplane.

A. Design Modular design principles have been chosen for the architecture of the controller. Based on software design patterns, like the interface and factory pattern, different implementations of the modules can be selected during the build and runtime. Fig. 7 shows the different possibilities for interfacing the process. This is realized by connecting via OPC to the simulated network of a power simulation application (e.g. DIgSILENT/PowerFactory) or by interfacing the process via a DMS application (Siemens SICAM 230) using COM interface. In the latter case the process itself can be a simulation application (running in real time) or the real network process itself. CVCU Parameters

MV Network

Process Data Manager

IEC 101 SICAM

Power Factory

DCOM

OPC Interface

Config

Controller

User Interface Monitoring

U, P, Q, UTap

Range Controller

Uset, Pset, Qset, UTapset

Level Controller

OPC

API

Control

Process Interface

Power Factory

SICAM Interface

Internal State

OPC RPC

System: Execution Monitor, Logging

Fig. 7. Modular architecture of the controller design and process interface.

B. Implementation Implementation of safety critical system applications is standardized under IEC 61508. Additionally a test driven approach has been chosen, based on prior designed test cases.

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Unit and integration test are continuously performed during the build based on the Boost unit test framework [11]. Another example of the modular concept is the optimization interface of the range controller in Fig. 8. The implementation of the optimization algorithm can be chosen at build time. The range controller relies on solving the boundary constraints of the convex optimization problem stated in (3). To rely on open source and free software as much as possible, the optimization module can be exchanged by various solvers. In order to increase transparency of the code and also the performance, a linear solver has been developed and implemented, beside the more complex interval arithmetic and along commercial and freely available solutions. Matlab runtime Optimization Toolbox (fmincon)

Range Controller Controller Logic Opimization Interface

Opimization Opt++

OptLinearConstraint Solver

Intervall arithmetic

Fig. 8. Modular optimization implementations of range controller.

C. Topology information for controller operation Changes in the network topology influence the operation of the controller, since the contribution matrix is the linearized model of the current network topology and represents the DG’s influence on a critical node. The change of the DG’s influence can result in incorrect function of the controller. The requirements for the reliable operation can be divided into four steps, whereas the first two steps are necessary information for operation. The SCADA / telecontrol system of the distribution network has to provide the information of the  online status of the critical node and controllable DG  association of the critical node and controllable DG with the correct transformer  change of the associated feeder of a critical node or controllable DG  dynamic determination of the contribution matrix after change in the topology While intra-feeder changes, when the critical node and/or the controllable DG stay in their original feeder, have not much influence on the contribution matrix, the change of the associated feeder of a DG’s associated critical node can be severe. The situation at the critical node could become opposite to the situation of the voltage at the controlled DG. In case of known switching situations, the matching contribution matrix is selected by the controller. In case of unknown topology changes, the controller operates in a reduced mode, increasing the operational reserve or distance to the voltage limits. If single DGs are out of operation or a measurement of a critical node is missing, due to communication problems or failures, the according column or row in the contribution matrix is omitted for the optimization step.

VI. SIMULATION ENVIRONMENT Modular test setups are necessary to simulate an environment close to the real setup. This has been achieved by coupling of the simulation, the process interface and the interface itself via standardized communication ports. Fig. 9 shows the setup for the offline simulation and the setup for the real time process interface, which can be a simulated environment or the real process behind. A. Offline Simulation In this setup all network operation conditions can be simulated offline. The simulation time is only constraint by the performance of the simulation platform. Different network operation scenarios can be simulated without interference of the real world process. B. Real Time Simulation To simulate the real world setup and the real time behavior the controller can be coupled to the DIgSILENT/PowerFactory network simulation application – running in real time mode – via the Siemens/Sicam 230 SCADA interface. For the operation of the controller only the interface to the process is visible and the nature of the process – simulated or the real network – is completely hidden. Fig. 10 shows the result of a real time simulation for one day with only the level controller in action. More details can be found in [12]. REAL PROCESS MV Network

IEC 104 Telecontrol interface

Real Time Simulation

Power Factory SIMULATION

OPC

SICAM 230 Offline Simulation

COM OPC

Process Interface via SCADA

CVCU

VOLTAGE CONTROLLER

Fig. 9. Simulation and process interface concept for offline, real time simulation and real world process control.

Fig. 10. Realtime simulation results of the CVCU (in level control mode).

C. Communication simulation Communication latency due to PLC can exceed 60 seconds until the next reading or set point can be transmitted. This time delay imposes a dead time to the control loop and can lead to instabilities. System analysis in advance together with

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simulation depending on field test experiences must be carried out to guarantee the correct voltage control operation. In simulation mode this is done by using a time delay buffer for the communication transmissions. VII. FIELD TEST PREPARATION For of the field test the following steps have to be achieved:  Unit, integration and system tests of the controller software components  Testing of the simulation and process interfaces  Simulation of predefined test cases of network operation conditions: changes in topologies, fault conditions of network, communication and distributed generators  Simulation of the communication delay times  Real time simulation via SCADA process interface  Open loop field tests with supervision and manual acknowledgement of set point changes  Closed loop field tests, with extensively logging A. Network operating Conditions for the Controller Several test conditions according to real world scenarios must be prepared for simulation run prior to the deployment in the field. Following operations are bound to happen as normal and abnormal conditions:  Switching parts of branches from the analyzed network to other grid sections. In this case critical nodes could be removed from the analyzed network and would, therefore, not require voltage control anymore. On the other hand, controllable plants could be removed from the analyzed grid, leading to a reduction to the controller’s capacity;  Switching parts of branches from other networks to the analyzed grid. In this case additional critical nodes could be included into the analyzed grid. The voltage on these nodes would be ignored by the Controller;  Internal switching of parts of the analyzed grid between two branches. In this case, controllable plants could lose their influence on some critical nodes, since they are now electrically separated. On the other hand, this could cause voltage problems at the branch they are now connected to. This internal switching could also lead to a ring operation of a part of a network, which would have a great impact on the voltage levels of the network nodes; Additionally the grid section specifics and operation strategies of the network operator must be considered: 1) “Großes Walsertal”(VKWNetz AG)  interconnection to a neighboring MV grid  switching from one transformer to the other with intermediate parallel operation 2) “Lungau” (Salzburg Netz GmbH)  Reverse a pumped hydro from storage to generation, which affects the high voltage network by high voltage gradients.

B. Communication medium 1) PLC Field test for the PLC communication have been carried out. Fig. 11 shows photos from the installation of the PLC coupling system and Fig. 12 shows the installed PLC repeaters in the network area. During measurement campaigns the following tasks and objectives have been investigated:  Analysis of cross talk of signal transmission  Analysis of topology recognition based on PLC  Determination of maximum communication distances  Bidirectional communication  Training of technical staff members  Different PLC coupling systems (inductive and capacitive)

Fig. 11. Installation of the PLC communication link

Fig. 12. Overview of the installed PLC repeaters in the test region “Großes Walsertal”.

2) Radio link communication The communication link based on radio links is currently under development. The first result and update of the status is expected for autumn 2011. In the field test in “Lungau” the communication will completely rely on radio link and in the field test of “Großes Walsertal” a combination between radio link and PLC is necessary, because the small bandwith protocol does not support multiple masters which would happen in same cases of topology switching.

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VIII. CONCLUSION AND OUTLOOK For porting the prototype to a C++ implementation required for the field test, the code has run through a major re-design, which now allows a more modular approach for the voltage control task. The essential steps toward a successful field test are listed and discussed. In detail these are the appropriate simulation environment for offline and real time simulation to emulate the real world process in the network and well prepared study test cases for the different network operation situations. The communication latency must be taken into account and simulated for stability analysis. The communication links are currently set up at the field test sites parallel to the controller system integration tests. The first open loop tests are scheduled for September 2011 and will bring important insights for validating the voltage control concept of DG DemoNet. IX. ACKNOWLEDGMENT The authors notice and gratefully acknowledge the contributions of Alexander Viehweider, currently with the University of Tokio, Japan. X. REFERENCES [1]

F. Kupzog, H. Brunner, W. Pruggler, T. Pfajfar, und A. Lugmaier, „DG DemoNet-Concept - A new Algorithm for active Distribution Grid Operation facilitating high DG penetration“, in Industrial Informatics, 2007 5th IEEE International Conference on, 2007, Bd. 2, S. 11971202. [2] A. Viehweider, B. Bletterie, D. B. de Castro, und H. Brunner, „Advanced coordinated voltage control strategies for active distribution network operation“, in Electricity Distribution - Part 2, 2009. CIRED 2009. The 20th International Conference and Exhibition on, 2009, S. 1. [3] A. Viehweider, H. Schichl, D. B. de Castro, S. Henein, und D. Schwabeneder, „Smart robust voltage control for distribution networks using interval arithmetic and state machine concepts“, in Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES, 2010, S. 1-8. [4] B. Enacheanu, A. Martin, C. Andrieu, B. Raison, N. Hadjsaid, und D. Penkov, „Future distribution network architectures: approach with a CRISP experimentation“, in Future Power Systems, 2005 International Conference on, 2005, S. 6 pp.-6. [5] „High-level specification of the functionalities for novel electricity distribution grid control“, in Technical Report from European Project INTEGRAL, Project Nr: FP6- 038576. [6] P. C. Taylor u. a., „Distributed voltage control in AuRA-NMS“, in Power and Energy Society General Meeting, 2010 IEEE, 2010, S. 1-7. [7] ADINE - Active Distribution Network, Specification of coordinated voltage control application. 2008. [8] A. Kulmala, A. Mutanen, A. Koto, S. Repo, und P. rventausta, „RTDS verification of a coordinated voltage control implementation for distribution networks with distributed generation“, in Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES, 2010, S. 1-8. [9] W. Pruggler, F. Kupzog, B. Bletterie, und B. Helfried, „Active grid integration of distributed generation utilizing existing infrastructure more efficiently - an Austrian case study“, in Electricity Market, 2008. EEM 2008. 5th International Conference on European, 2008, S. 1-6. [10] H. Brunner, B. Bletterie, A. Lugmaier, und T. Pfajfar, „Strategien für die Spannungsregelung in Verteilnetzen mit einem hohenAnteil an dezentralen Stromeinspeisern“, in IEWT 2007, Vienna, 2007. [11] „Boost Test Library“. [Online]. Available: www.boost.org. [12] F. Anrén, S. Henein, und M. Stifter, „Development and Validation of a Coordinated Voltage Controller using Real-time Simulation“, presented at the IEEE Industrial Electronics Society, IECON, Melbourne, 2011.

XI. BIOGRAPHIES Matthias Stifter (M’2009) M.Sc. in Technical Cybernetics in 2005, from Vienna University of Technology. Since 2007 he is with AIT, Energy Department - Electrical Energy Systems. His research interests include distributed generation, active distribution network planning, energy management and standards for communication and control in smart electricity networks. Since 2008 he is national expert for the IEA DSM Task XVII. He is a member of IEC TC 57 WG 14 and OVE TSK MR 57. Benoît Bletterie, M. Sc. in Electrical Engineering from Supélec (France) and Universidad Politécnica de Madrid (2001); Since 2009 researcher at business unit Electric Energy Systems of Department Energy at the Austrian Institute of Technology (AIT). 2003-2008 researcher and project manager at the Business Unit for Renewable Energy Technologies at arsenal research. His field of expertise covers the integration of distributed generation into power networks, with a particular focus on power quality and the performance of Photovoltaic inverters. Benoît Bletterie is member of CENELEC TC8X WG3 and of IEC SC77A. Helfried Brunner, M.Sc. in Electrical Engineering from the University of Technology in Graz, Austria (2004); Since 2009 he is vice-head of business unit Electric Energy Systems of Department Energy at the Austrian Institute of Technology (AIT). 2004-2008 researcher and project manager at business unit for Renewable Energy Technologies at arsenal research, responsible for the topics integration of distributed generation into electricity networks, and smart grids. Since 2008 operating agent of Annex II – DG Integration in Distribution Networks – within the IEA ENARD. Filip Andrén, studied Applied Physics and Electrical Engineering at Linkping University from 2004 to 2009 with a thematic focus on Control and Information Systems where he received a masters degree. Since 2009 he is working as a scientist at the AIT Austrian Institute of Technology, Energy Department. He is specialised on smart grid and is working with power hardware in the loop as well as control and communication standards. Daniel Burnier de Castro Studies in computer science at Universidade do Estado do Rio de Janeiro (Brazil). Graduation in electronics with a focus on renewable energies at Universidade do Porto (Portugal). From 2004 to 2006 software developer at EDS (Electronic Data Systems) in Rio de Janeiro. Since 2008 he is working as a scientist at the AIT Austrian Institute of Technology, Energy Department. He is specialised on smart grids and on the integration of distributed energy production plants into electric grids and on storage technologies. Sawsan Henein is Researcher at the Austrian Institute of Technology, Vienna, Austria. She holds a bachelor degree (1997) from the University of El-Mansoura Egypt, Faculty of Engineering, in electrical engineering and a master degree (2008) from the University of Technology Vienna, Austria in electrical engineering. She has experience in reliability estimation of electrical networks, network simulations and optimization. Her main research interests are reliability estimation, simulation, and control methods for electrical distribution networks with high penetration of distributed generation.

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Roman Schwalbe, BSc, is student of Physical Energy and Measurement Engineering at the Faculty of Physics of the Vienna University of Technology since 2007. He gained work experience at Siemens IT Solutions and Services (former Siemens PSE). His main interests are in Smartgrids and the simulation and modeling of physical and electrical processes.

Andreas Abart, MSc in Electrical Engineering and Technical Biomedicine from Technical University Graz. 2003 assistant at the Institute of Electrical Power Systems. PhD in Electrical Power Systems 2006. Since 2003 with Energie AG Oberösterreich in the area of EMV, electromagnetic fields, power quality, impacts of distributed generation and smart grids. He is a member of OVE TSK EMF and CLC TC106x. Reinhard Nenning, MSc in Mathematics, electrical power systems and energy transmission systems from university of Hagen. Since 1980 with Vorarlberger Kraftwerke Netz AG (VKW Netz) in the area of high voltage systems. Since 1994 responsible for network planning and optimization. Special interests in reliability and impact of distributed generation on power quality at MV and LV networks. He is a member of expert committees at “Österreichs Energien” and Smart Grids Technology Platform Austria. Frank Herb, MSc. in electrical engineering / electric power systems from HTWG Konstanz. Since 2007 with Vorarlberger Kraftwerke Netz AG (VKW Netz) in the area of network planning. Since 2008 project leader of the project DG Demonet at VKW Netz. Responsible of availability and fault statistics, reliability estimations, network planning and impact of distributed generation on the electrical grid.

Rudolf Pointner, MSc in Electrical Engineering / Control Engineering at the Vienna University of Technology. ELIN in the area of voltage regulation of synchronous generators. Since 1989 with Salzburg Netz GesmH in the field of generation, metering, protection, power quality and smart grids.