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Abstract – Especially influenced by the rising number of renewable and distributed power generation plants and so-called unbundling, the controllability and protection of power supply systems are becoming ever more complex as these systems are developed further. To securely meet these challenges a reliable observability of the power system is needed. Novel digital measurement equipment such as phasor measurement units (PMU) in transmission and distribution networks and smart meters in low voltage grids deliver the system parameters needed to perform a system state estimation analysis. This paper discusses the use of PMUs to support the operation of virtual power plants (VPP). It additionally presents methods, which have already been tested in different simulations, to optimally select the locations of measurement units to ensure system observability.

Index Terms – power system observability, phasor measurement unit, renewable energy sources, virtual power plant.

I. INTRODUCTION n the near future, distribution networks will be obliged not only to handle the tasks of load supply but also will be

instrumental in providing system services such as active contribution to the support of frequency and voltage stability through the adjustment of active and reactive power, respectively, as well as improving the quality and security of supply. This requires an advanced monitoring and controlling of the distribution grids as well as of the locally installed power plants, selected loads and storage systems. Thus, the future power system should be able to operate under the strong bidirectional power flow conditions in order to allow an efficient integration of local power generation (especially based on renewables). For this purpose, additional functionalities in the distribution grid are necessary that include on the one hand, an active participation of different consumer types in the system control and, on the other hand, an improved application of storage systems for balancing the intermittent renewable generation. Furthermore, the operation of all aforementioned units, which actively participate in the

Z. A. Styczynski is head of Chair Electric Power Networks and

Renewable Energy Sources at the Otto-von-Guericke University Magdeburg, Germany (sty@ovgu.de).

P. Komarnicki is group manager in the Process and Plant Engineering Business Unit of the Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany (komarn@iff.fraunhofer.de).

M. Powalko, K. Rudion are research fellows at the Chair Electric Power Networks and Renewable Energy Sources at Otto-von-Guericke University Magdeburg, Germany (powalko@ovgu.de, rudion@ieee.org).

grid control, has to be coordinated by a super-ordinate management system in order to realize the pre-defined goal functions. Such possibilities are provided by the virtual power plants, the concepts of which have been developed in the last decade in different research projects such as [1]. Advanced information and communication technologies (ICT) are being employed to combine renewable and distributed energy sources integrated in the distribution network with distributed storage systems and controllable loads within the virtual power plants.

However, these different challenges, which are partially already now in the implementation and test phase in Germany in the scope of several research projects covered by the E-Energy initiative [2], [3], should not have any negative influence on the existing power system security, reliability and quality. To ensure these requirements an advanced observability of distribution power system is crucial [7]. System observability analysis delivers information on the feasibility of a system state with the aid of the measurement units installed in the network. The number as well as the spatial distribution of the measurement units is evaluated in order to minimize the costs and to deliver the widest possible information spectrum. Novel digital measurement equipment such as phasor measurement units in the high and medium voltage grids as well as smart meters in low voltage grids can deliver the necessary information for obtaining the state of the distribution power system.

Since cost effectiveness and technical feasibility preclude their installation at every network point, expedient and beneficial use of such measurement units in virtual power plants, for instance, will necessitate the development and testing of appropriate methods for their efficient placement.

This paper discusses the use of phasor measurement units to support the operation of virtual power plants. It additionally presents methods to optimally select the locations of measurement units to ensure system observability, which have already been tested in different simulations. Then, it describes real phasor measurement units installed on the basis of the aforementioned selection of locations. In conclusion, the results are explained and evaluated.

Enhancing Virtual Power Plant Observability with PMUs

M. Powalko, Student Member, IEEE, P. Komarnicki, K. Rudion, Members, IEEE, Z. A. Styczynski, Senior Member, IEEE.

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978-1-4244-8081-4/10/$26.00 ©2010 IEEE

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II. PMU INSTALLATION AND OPERATION The observability is defined by providing the information of

the system and its state, which are required for stable operation of the power system. This information can be delivered by phasor measurement units. PMUs allow very fast measurement, for 50Hz systems in Europe a measurement rate of 25 times/s [12], of different power system parameters like current, voltage and frequency.

The PMU measurements properties allow them to be used not only in normal or sub-normal conditions of the power system but also in critical conditions, where dynamic measurement of different parameters is required.

Furthermore, the high accuracy measurement of power system data via PMUs makes it possible to use them for different applications of power system operation, beginning with monitoring (e.g. online voltage monitoring), through control (e.g. state estimation, optimal usage of transmission capacities) and up to protection (e.g. adaptive protection schemes and wide-area-concepts).

Moreover, the PMUs have communication features (protocol and data model) that facilitate the rapid exchange of large quantities of data with a phasor data concentrator (PDC) [12], or directly with the power system control centre applications (Fig. 1). Synchronous measurements of system parameters produce a forward state when providing global information, which may be used to evaluate the system state and thus safeguard system stability.

An example of the PMU installation at the low voltage level is shown in Fig. 2. The phasor measurement unit is located inside the board (at the top) together with the GPS receiver (at bottom). The measurement signals are provided directly from the plug to the corresponding inputs of the PMU. The measurement data in this case is sent via the LAN network to the PDC, where is processed and can be viewed both directly by the monitoring software as well as via the internet server site. As can be seen the installation can be realized in a compact form and can be adapted to the requirements of the surrounding, e.g. cubicles of substations or interconnection points of generation units.

III. POWER SYSTEM OBSERVABILITY Depending on the voltage level in the power system,

different measurements used to be performed in the grid. Not only the type of the measurements can differ but also the amount of installed measurement devices can vary significantly with a general tendency of providing no measurements in distribution grids with rated voltage lower than 110 kV [5]. However, it is necessary in both good and poorly equipped power grids to be able to monitor the actual operation state of the system in case a virtual power plant is realized. In order to test if a full or only partial observability of the system is provided using the available measurement equipment, the observability analyses need to be performed. The observability analysis is the first step that precedes the state estimation process.

Fig. 3 gives the general process structure for gaining the system state. The inputs are, in addition to measurements, also the information about network topology as well as parameters of the network elements.

Fig. 1 Principle of PMU monitoring of virtual power plant

As output from the observability analysis the information about the critical and redundant measurements as well as information about the observable grid parts (islands) is provided. For the grid parts that are not directly observable using the PMU-measurements the second step has to be carried out that estimates the state of the rest of the system.

Performing the power system observability analyses will give the indirect answer for the three common questions:

• Are there enough measurements? • Are measurements properly located? • Which are the observable islands?

The necessary information for analysis is the topology of the network and the available measurements in the grid. The information about the measurements should contain the type of the measurements – voltage, current, active and reactive power flows, and the placement of the corresponding measures. Using that information the statement can be made whether or not the system state can be obtained – by using the available measurements in the network.

As an additional result of the observability analysis, the critical measurements can be pointed out. These measurements play a key role in the system, since the leakage of only one from the whole set of the critical measurements will lead to the situation, where the system cannot be fully observed. The information corresponding to the redundant measurement gives an overview about the measurements that are not critical in the system.

Fig. 2 Phasor measurement unit installation in the distribution system

PMU

GPS receiver 47,8 cm

60 cm

3

Network topology

Network measurements

Parameters of the network

elements

Observability Analysis

State Estimation

Calculated State of the System

(Snapshot)

Critical measurements

Redundant measurements

Observable islands

Setting of power system modell according to

snapshot and scenario analysis

Fig. 3 Interrelation between the observability analysis and state estimation

They can be removed from the grid without a negative impact on the observability status. The amount of redundant measurements in the grid should be as high as possible, but installing the low accurate measurement devices can have a negative impact on the accuracy of the state estimation.

In the event that the power system cannot be fully observable, it is possible to carry out the observable islands. These are the areas in the grid, where the available measurements allow for performing the estimation of the system state. Observable islands are connected with each other through the unobservable branches or unobservable areas or islands.

Due to the possibility that either the network topology can change or the measurement device can fail, the algorithms used for the observability analysis can take both of these possibilities into account and provide the corresponding optimal placement for the measurement devices. Since in networks some of the conventional (not time synchronized) measurement devices are already installed, the placement dedicated for the new devices in the grid can be carried out as well.

As shown in Fig. 3 the observability analysis is directly correlated with the state estimation. The input data from observability analysis is taken with one additional type of information for the analysis. This is the description of the grid elements parameters (e.g. lines, transformers), which are used in order to create the π equivalent circuit. In the state estimation the information about the network parameters and the network topology are necessary to create the mathematical representation of the power system using the corresponding equations. The result of the state estimation is the information about the current state of the power system. This is defined as a voltage phasor at each node in the grid that includes voltage amplitude and the phase angle.

IV. PHASOR MEASUREMENT UNIT FOR VIRTUAL POWER PLANT MONITORING

A. Virtual point of common coupling The coordination of a local virtual power plant, which in

general is connected by several points of common coupling (PCC) with the rest of the network, may be improved by introducing the concept of a virtual connection point – the so called virtual point of common coupling (VPCC). Such a

solution may enable representation of a system with several PCCs to be considered as a conventional power plant with one point of connection to the grid – this idea is shown in Fig. 4. The concept of the VPCC is based on the usage of PMU technology. The PMUs allow for very fast time-stamped measurements of different network parameters such as current, voltage and frequency, which are necessary for network monitoring since other parameters of the virtual point of common coupling, such as the active and reactive power flows, can be computed:

where:

kttVPCCP = - resulting real power in VPCC for the time tk

ki ttPCCP = - measured active power in the i-th point of connection for the time tk

i - number of real connection point tk - time point

The fast, continuous and secure provision of the measurement data from the separate PCCs to the central control room of VPP can be ensured due to the communication properties of the phasor measurement units [12].

B. Contribution to the observability and state estimation PMU measurement devices can contribute to improve the

observability and the accuracy of the state estimation. The main benefit of using the PMU, besides their high accuracy, is the time synchronization used for performing the measurements.

Conventional measurement devices are able to provide the measurements of the voltage and current amplitude, but because of the lack of global synchronization, the measurement of the phase angle is not possible in this case. Since the PMU uses the widely and freely available GPS signal, which contains the time information, the devices can perform the measurements of the phase angle of voltage and current in consideration of the GPS accuracy at the level of 1μs [12]. This accuracy allows estimating in 50Hz power systems the phase angle with ±0.018 degree phase shift accuracy [8].

ControlRoom

Virtual power plant

PCC1

PCC2

PCC3

PCC4

PCC5 Virtual power plant

VPCC

PMU1

PMU2

PMU3

PMU4

PMU5

Equivalent representation

Original system Equivalent system

Fig. 4 PMU based virtual point of common coupling [3]

∑=

== =n

ittPCCttVPCC kik

PP1

(1)

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The measurement data coming from the PMU is saved with the UTC time and date [12]. This feature can be helpful in situations where the communication infrastructure is not fast enough to transfer the data and, in addition, delays in the communication route are present. The time information can be used by the network operator to filter the data coming from different measurement stations in the grid, where due to the limitation in communication infrastructure the measurement results were retrieved at a different time. The PMU data from exactly the same time can be used further in state estimation or, if some grid areas are completely covered by the PMU measurement, this fulfills the observability requirements that the state estimation is not necessary and the state of the system results directly from the measurement.

When the correct data is chosen and put into the state estimation procedure, it is possible to prioritize the measurements corresponding to their accuracy. This can be

done by defining the values of variance 2nσ for each

measurement device in the R matrix in estimation algorithm [9]:

where the standard deviation nσ for each device can be calculated using the equation:

Since the phasor measurement units come with higher accuracy as compared to conventional devices, their measurement data will have higher values in the matrix R in the estimation objective function:

))(())(()( 1 xhzRxhzxJ −−= −T (4) where:

J(x) - state estimation objective function, z - measurement vector,

h(x) - measurement functions vector, R - measurement error covariance matrix.

State estimation takes into account more than just the high accuracy of the phasor measurement units. By installing a corresponding amount of PMU devices in the network, the full system observability can be obtained by using only the PMU data. The adjustment of the state estimation equations according to time synchronous measurements of both the voltage and current phasors can be done. In such case, the non lineal coherence between the measurement data (input) and the state variables (output) from the standard state estimation method can, with related assumptions (linear model in a network structure is used and kept constant), be represented as lineal dependencies [8]. This allows contributing to the reduction of computation power and efficiency of the calculations.

If the grid observability can be provided by combining the conventional (not time synchronized) and time synchronized measurements, different approaches can be taken into account. The PMU data can be used together with the conventional data in standard estimation approach – the benefit of PMU high accuracy. Otherwise, if part of the system is observable by PMUs and part by the conventional measurement devices, the lineal state estimation using PMU data can be performed at first and the results can be used in the second step as input for the standard state estimation together with the not time synchronized measurements.

C. Optimal placement of the PMU In order to provide the observability of the system the

proper placement of the PMU in the grid needs to be determined. There are plenty of algorithms for estimating optimal sites in the grid to install the measurement devices. They take into account the topology of the network and are based on the linear programming, like [10], or try identifying the nodes with the softest characteristics, like in [11] – nodes in which phase and voltage are very sensitive to changes in the active and reactive load.

The algorithms point out the nodes where the measurement devices need to be installed. In most cases the minimum number of devices needed for providing the system observability is sought. This is followed by the economical aspects – high cost of the measurement devices and corresponding measurement equipment, like for e.g. the measurement transformers. In such cases, a high number of critical measurements in the network will be present. This situation is disadvantageous, since if a critical measurement is missing, the whole system will no longer be observable.

An important aspect connected with the state estimation is the bad data detection. After the voltage phasors in the network are obtained, the plausibility check can be performed. The mathematical procedure, e.g. largest normalized residuals [6], can be implemented in order to find out bad data in the measurement set. Such measurement can be excluded from the state estimation input to improve the accuracy. It is not possible for the critical measurement to detect the bad data, since the residuals for those measurements are always equal to zero [6]. Fig 5 presents the benchmark power system, which was used for the analysis of optimal placement of the PMU in order to obtain full observability of the system. The network was assumed to have no measurement devices. There were two cases taken into account, which were solved using the placement algorithm based on the linear programming presented in [10]:

• Case I – the optimal placement of the minimum number of PMU devices for achieving system observability,

• Case II – the optimal placement of the minimum number of PMU devices with regard to the N-1 security provision.

In the N-1 provision the disconnection of one line was defined, which is equal to possible loss of one power flow measurement (active and reactive). The results of the placement analysis for the first case are shown in Tab. I.

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=−

2

21

1

10000

001

mσ

σR (2)

( )∑=

−−

=N

ii zz

N 1

2

11σ (3)

5

33

32

10

26

9

27

12

11

25

4

653

24

13

38

15

21

39

8

7

2223

14

1630

31

43 41

20

34

35

21

37

36

1828

19

17

29

4042

Fig. 5 Medium voltage benchmark network – 43 nodes

For the first case, only 15 devices are necessary to obtain

the complete observability of the grid. This number needs to be increased by 6 additional PMUs, if case II is taken into account – see results shown in Tab. II. It can be observed, that not only the number of the devices, but also the location of the phasor measurement units differs from case I to II. It is not possible to enhance the configuration from case I by adding more PMUs to fulfill the requirements for case II. Therefore, the concept of installing measurement devices in the network needs to be carried out separately for the corresponding aims.

D. Improvement of the power system monitoring Application of PMU devices can significantly contribute to

the monitoring solutions in a distribution system taking into account high accuracy of the measurements. Therefore, apart from gaining the general information about the system state (snapshot) the additional tasks can be carried out with the PMU based monitoring system that especially covers the online monitoring of power quality indices of the voltages and currents defined in DIN EN 50160 [13] – see Fig. 6.

Such information can be then used either by the VPP operator or grid operator, according to the structural and organizational architecture of the virtual power plant, to improve the voltage quality, e.g. by activation of reactive compensation in the affected grid parts. The measured PMU data are continuously sent from local measurement points spread over the grid to the global monitoring and controlling center via the communication medium, where it is analyzed focusing on the issues of interest and visualized. In order to practically test the benefits of the PMU in the distribution grid a test installation is being planned in the scope of German light-tower project RegModHarz [3].

TABLE I PMU PLACEMENT IN THE BENCHMARK NETWORK – CASE I

Number of PMU PMU location 15 1,13,16,17,19,20,21,22, 23,24,25,26,27,32,41

TABLE II

PMU PLACEMENT IN THE BENCHMARK NETWORK – CASE II Number of PMU PMU location

21 2,6,12,13,16,17,18,19,20,21,22, 23,24,25,26,27,30,32,35,40,42

Fig. 6 Voltage quality indices [13]

An additional aspect, which is in the planning stages, is the direct monitoring of the power flow exchange of the region. Due to the time synchronized and highly accurate measurements the actual energy demand/production of the region can be estimated and used for improved coordination via the virtual power plant, e.g. for scheduling aspects. Using the measurements the power flow exchange visualizations can be realized as shown in Fig. 7 on the example of the whole Danish power system.

V. CONCLUSION This paper deals with the issues of system observability and

virtual power plants. The phasor measurement units were introduced as modern devices, which can in comparison to the conventional measurement units provide the time synchronized measurement of the system parameters. The benefits of PMU usage for improving system observability were discussed as well.

The aspects connected with high accuracy and time stamped measurements were pointed out. The virtual point of common coupling was described as a PMU based monitoring solution, which makes it possible to represent the virtual power plant or part of the VPP as a conventional generation unit connected to the grid at one point – VPCC.

The necessity of the optimal PMU placement in the grid in order to provide system observability was taken into account and examined within the 43 node benchmark network. Two cases were investigated in order to provide system observability under different conditions – the minimum number of the PMU during the normal operation (case I) and under consideration of N-1 for the line disconnection (case II). The results show that for the first scenario only 15 PMU are necessary, whereas in the second case 21 phasor measurement units need to be installed in the grid.

The installation of a PMU monitoring system in the distribution grid is planned in order to test the concept of the VPCC as well as to improve the already existing monitoring solutions by incorporating new, time synchronized measurement data.

VI. ACKNOWLEDGEMENT The authors thank Prof. I. Golub from the Technical

University of Irkutsk for his support in the scope of optimal PMU placement in the distribution grids.

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Fig. 7 Power exchange visualization for Danish power system [4]

VII. REFERENCES Internet Sources:

[1] Flexible Electricity Network to integrate the expected Energy Evolution; http://www.fenix-project.org/

[2] E-Energy - ICT-based energy system of the future; http://www.e-energy.de/en/

[3] Project Regenerative Modellregion Harz (RegModHarz); https://www.regmodharz.de/

[4] Energinet.dk - The electricity grid just now; http://www.energinet.dk/Integrationer/ElOest/ElsystemetLigeNu/energinet1.swf

[5] VDE (ETG-Task Force): Smart Distribution 2020 - Virtuelle Kraftwerke in Verteilngsnetze. Technische, regulatorische und kommerzielle Rahmenbedingungen. Studie der Energietechnischen Gesellschaft im VDE (ETG), Jui 2008. http://www.vde.com/de/fg/ETG/Arbeitsgebiete/V2/Aktuelles/Oeffenlich/Seiten/SmartDistribution2020Ergebnisse.aspx

Books: [6] A. Abur, A. G. Exposito; „Power System State Estimation – Theory and

Implementation”, CRC Press, 2004. Conference Papers:

[7] P. Lombardi, M. Powalko, K. Rudion; „Optimal Operation of a Virtual Power Plant”; IEEE Power & Energy Society General Meeting, 26-30 July 2009, Calgary, Canada.

[8] M. Powalko, K. Rudion, Z. A. Styczynski; „Erweiterung des State Estimation Algorithmus durch den Einsatz von PMU Messungen”, Internationaler ETG-Kongress, 27-28 Oktober 2009, Düsseldorf, Germany.

[9] M. Powalko, K. Rudion, P. Komarnicki, Z. A. Styczynski; „Observability of the distribution system”. In Proceedings of 20th International Conference and Exhibition on Electricity Distribution (CIRED), Juni 2009, Prag, Czech Republic.

[10] B. Gou; „Optimal placement of PMUs by Integer Linear Programming“. IEEE Trans. Power Syst. Vol.23. No. 3.P.1525-1526, 2008.

[11] A. Naumann, P. Komarnicki, M. Powalko, Z. A. Styczynski, J. Blumschein, M. Kereit; „Experience with PMUs in Industrial Distribution Networks”, IEEE Power & Energy Society General Meeting, 25-29 July 2010, Minneapolis, Minnesota, USA.

Standards:

[12] IEEE Standard for Synchronphasors for Power Systems (IEEE C37.118TM-2005, revision of IEEE Std 1344TM -1995), Power System Relaying Committee of the IEEE Power Engineering Society, Approved 1 February 2006 American National Standards Institute Approved 21 October 2005.

[13] Deutsche Kommission Elektrotechnik: DIN EN 50160 Voltage characteristics of electricity supplied by public distribution networks; German version EN 50160:2007.

VIII. BIOGRAPHIES Michal Powalko earned a dual Master’s of Science from Wroclaw University of Technology in Poland and Otto-von-Guericke University Magdeburg in Germany in 2007. Since then, he has been working as a scientific assistant at the Chair of Electric Power Networks and Renewable Energy Sources at Otto-von-Guericke University, Magdeburg, Germany. His special fields of interest include State Estimation and PMU applications in power systems.

Przemyslaw Komarnicki earned a dual Master’s of Science from Wroclaw University of Technology in Poland and Otto-von-Guericke University Magdeburg in Germany in 2004.

Since 2004, he has been a research manager and, since 2008, the Electric Power Systems Group Manager in the Process and Plant Engineering Business Unit of the Fraunhofer Institute for Factory Operation and Automation in Magdeburg, Germany. Parallel to his work, he earned his doctorate from the

Department of Electric Power Networks and Renewable Energy Sources at Otto von Guericke University in 2008. His special fields of interest include synchronized measurements, testing methods and PMU applications for power systems. He is a member of the Association for Electrical, Electronic and Information Technologies (VDE), the International Institute for Critical Infrastructures (CRIS) and LPQIVES Certification Board.

Krzysztof Rudion studied electrical engineering at the Wroclaw University of Technology, Poland and the Rostock University of Technology. He graduated in 2003 at the Wroclaw University of Technology with a Dip.-Ing. Degree. He then joined the Chair of Electric Power Networks and Renewable Energy Sources at the Otto-von-Guericke University Magdeburg, Germany as a research engineer and he earned his PhD degree there. His primary field of interest is wind energy.

Zbigniew A. Styczynski (M’94, SM’01) earned his doctoral and habilitation degrees from Wroclaw University of Technology in Poland in 1977 and 1985, respectively. He taught at the Technical University of Stuttgart in Germany from 1991 until 1999 when he was appointed to Chair of Electric Power Networks and Renewable Energy Sources in the School of Electrical Engineering and Information Technology at Otto-von-Guericke University Magdeburg in Germany. He is President of the

Saxony-Anhalt Center for Renewable Energy (Z.E.R.E. Sachsen-Anhalt) His special fields of interest include electric power networks and systems, expert systems and optimization problems.