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Abstract— The application of a STATCOM and Battery

Energy Storage System (BESS) can help alleviate some of the problems encountered with wind farm integration to the existing power system. The intent of this paper is to demonstrate the benefits obtained with STATCOM and BESS for this purpose. First, the paper identifies the power quality and some other issues in the Southern California Edison system where there is abundant wind generation. Computer simulations (e.g. load flow and transient stability) show that the application of a STATCOM with BESS can help the system survive under the most severe contingencies occurring in the area. The analysis also demonstrates that the BESS can help dispatch an individual wind farm in the area during steady state. The BESS can help reduce local wind generation curtailment, necessary during high generation and low local system load that causes transmission overloading, by absorbing the excess energy generated by the wind farms.

Index Terms—STATCOM, Battery Energy Storage System, Wind Farm Integration, Wind Power Dispatch.

I. INTRODUCTION OUTHERN CALIFORNIA EDISON (SCE) is working on a research project with the support of the California

Energy Commission (CEC) / Public Interest Research and Development (PIER). The project has multiple objectives and this paper addresses grid power quality and other issues, and assesses potential improvements on the grid at selected interconnection locations by providing energy storage devices with bidirectional control of real and reactive power.

This paper is based on measurements from a wind power generation rich area in the Southern California Edison electric power system and describes the system models and software tools used during the studies. Section II of this paper includes a general description of the wind power generation rich area and a characterization of the key problems in the area. Section III describes the proposed STATCOM-BESS system and the benefits obtained from the application of such a system. Section IV presents the BESS application at one individual wind farm. Conclusions and next steps in the project are presented in Section V.

A. General information of the SCE wind generation area The studies presented in this paper are based on measurements

Juan Castaneda is with Southern California Edison. Sercan Teleke is a

PhD candidate in Dept. of ECE at NCSU. All other authors are with Quanta Technology, 4020 Westchase Blvd. Suite 300, Raleigh, NC, 27607.

from a wind power generation rich area in the Southern California Edison electric power system. The total wind farm and one specific wind farm generation profiles during seven days are presented in Figure 1. The wind power generation profiles are based on actual measurements with one sample per minute from the SCE Energy Management System. As can be seen from Figure 1 the wind power output has steep rises and sudden drops during the entire week and during any given day. During peak generation the system can produce approximately 270 MW and absorbs about 100 MVAr from the bulk power system for reactive power support.

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Figure 1: Total wind farm (a) and one specific wind farm (b) generation profiles during seven days.

B. System Models and Software Tools The load flow and stability studies performed are based on two software tools: PSLF and PSCAD. The PSLF model is a SCE system wide network database used for bulk power system analysis. The 2009 year SCE base case is used for the steady-state contingency analysis and dynamic simulations. To the base case, the wind generation static and dynamic characteristics were added. Wind generation was modeled as Type 1 wind farms, i.e., without any reactive power support [1]. The results obtained from this base case are representative

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Application of STATCOM with Energy Storage for Wind Farm Integration

Juan Castaneda* Johan Enslin David Elizondo Nagy Abed Sercan Teleke **

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978-1-4244-6547-7/10/$26.00 © 2010 IEEE

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of the operating conditions in the system. The PSCAD model is essentially a detailed representation of the wind power generation rich area in the SCE system, but the rest of the system is represented by an equivalent. The results obtained from this base case are used to re-confirm the results obtained in PSLF in which the full SCE system is represented.

II. CHARACTERIZATION OF PROBLEMS AT SYSTEM UNDER STUDY AREA

This section includes a general description of the wind power generation rich area and a characterization of the key problems.

A. Wind power generation related problems A summary of the system characteristics and problems in

the area of study include: • 380 MW installed wind capacity (310 MW operation) • Minimum reactive power support capability • Absorb around 100 MVAr reactive power from system • No Low-Voltage-Ride-Through (LVRT) capability • Non-compliant with FERC – Large Generator

Interconnection Procedure (LGIP) • Common wind farm curtailments • N-1 contingency require > 60 MW wind curtailment • Voltage collapse concern during line trip • Operational problems with installed SVC • Limited reactive power support on system

B. Contingency results without energy storage devices A contingency analysis on the wind power generation rich

area in the SCE system was performed and identified two critical contingencies as unsolved cases/non-converging during steady state load flow simulations. SCE has issued operational instructions that outline the mitigation techniques for this contingency by wind energy generation curtailment.

Figure 2 shows the voltage profile and the power output for

two individual wind farms in the area of study and its performance during one of the critical contingencies. These results are based on simulations performed in PSCAD software tool. As can be seen in Figure 2 the voltage collapses after the contingency and the power output of the two individual wind farms goes to zero in less than 2 seconds after the contingency.

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Figure 2: Voltage profile (a) and the power output for two individual wind farms (b) and (c) during one of the critical contingencies (P and Q shown with

dark and light color, respectively).

III. APPLICATION OF STATCOM WITH ENERGY STORAGE

A. Proposed system and STATCOM - BESS modeling description The authors of this paper reviewed the existing battery technologies [2,3,4] and selected the energy storage device, i.e., the battery and STATCOM size and the specific substation location within the wind power generation rich area in the SCE system based on the solution to contingency problems listed before in Section II. Figure 3 shows the STATCOM – BESS system proposed.

Figure 3: Basic Schematic of the STATCOM – BESS

From Figure 3, the main components technical characteristics are described next. 1. 8 MW/4hr Battery. Charge/discharge profile for battery operation is weekday-

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only. The battery shall have an operational life of at least 5 years without any major maintenance and charging /discharging its full nominal capacity. It is also required that the battery shall last about 20 years at reduced capacity. No battery technology is preferred and any reliable technology with good efficiency is satisfactory. 2. 20 MVAr STATCOM. STATCOM ratings in MVAr are dynamic, or short term overload ratings, which should last at least 4 seconds. A hybrid STATCOM with Mechanical Switched Capacitors (MSC) can also be considered in the final design. 3. Control and HMI (Human Machine Interface) of STATCOM-BESS system At the time of the writing, it is acceptable to have the BESS (with its AC/DC interface) operating independently of the STATCOM. Efforts to integrate this as one system as well as addressing potential of conflicting controls among the systems will be conducted at a later stage in this study. 4. Inverters One inverter or more inverters can be used to build up the complete BESS and STATCOM system as long as the ratings are within the MVA requirements. 5. Substation Selection The substation selection was based on the following criteria: a) close proximity to the high capacity wind farms in the area; b) existence of at least 10 MW of local loads near the selected substation bus which can be supplied partially by the storage; c) mitigation of the voltage and angular stability effects of the critical contingencies; d) available physical space inside the substation.

B. Contingency results with STATCOM and STATCOM – BESS This section presents the benefits of the application of STATCOM-BESS to address the problems at the wind power generation rich area. Three main benefits of the system application are: 1) Contingency support in terms of MW and MVAr. The STATCOM-BESS system prevents the system from collapsing for the critical contingencies. 2) Voltage profile support. With the STATCOM-BESS system the voltage recovery is improved in about 10-15%. 3) Improved fault ride-through support on Type 1 wind farms. The STATCOM-BESS system can support the close-by wind farms to ride through low voltage excursions following distant line faults. 4) Some portion of the connected wind farms can be dispatched an hour ahead. Dynamic simulations were performed to observe the behavior of the system during the contingency with and without the energy storage system. Dynamic simulations in PSLF and PSCAD software tools were performed.

Dynamic simulation results in PSCAD software Figure 4 shows the voltage profile and the power output for two wind farms after the critical contingency. As it can be seen in the figure, the STATCOM -BESS prevents the voltage collapse as after 5 seconds from the contingency initiation, the voltage profile is fairly maintained and the power output of wind farms remain without significant change. In this case, a 20 MVAr STATCOM and 5 MW battery was simulated. The graphs are shown only for 5 seconds in order to shown the transitory response. Other controls like governors act after 10 seconds time frame but are not included in the PSCAD model.

Figure 4: System Collapse Prevention after the application of 20 MVAr STATCOM and 5 MW battery during the Contingency (P and Q shown with

dark and light color, respectively).

Dynamic simulation results in PSLF software Two cases are investigated using PSLF: 1) system behavior without energy storage; and 2) system behavior with energy storage with reactive capability of 20MVar. A three phase fault is simulated at time equal to 1 second and normally cleared after 4 cycles by disconnecting one of the critical lines. Figure 5 shows the output power and the terminal voltage behavior of different wind farms in the system before and after the critical contingency without the energy storage. It’s clear that the system is unstable and within an undamped oscillatory state.

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Figure 5: Power output (a) and voltage profile (b) at different wind generation buses before and after the contingency without the energy storage

Figure 6 shows the system frequency before and after the critical contingency without the energy storage. The abnormal frequency excursions are the result of system instability.

Figure 6: The system frequency without the energy storage before and after

the contingency.

Figure 7 shows the system frequency with the energy storage installed in the system. The figure shows that the system is stable after the contingency and the oscillations are damped.

Figure 7: The system frequency with the energy storage during the

contingency.

Figure 8 shows the voltage profile and the power output of a number of wind farms before and after the critical contingency with energy storage and reactive power support. As we can see in the figure, the wind farms maintain their pre contingency power output without any oscillatory behavior.

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Figure 8: Voltage profile (a) and the wind farms output power (b) before and after the contingency with energy storage.

IV. BESS APPLICATIONS/OPERATING MODES TO AN INDIVIDUAL WIND FARM

This section presents a summary of the BESS control algorithm and the results of its possible applications to an individual wind farm of about 50 MW peak power. Two applications are presented: 1) hourly dispatch of real power output; and 2) contingency support. For the hourly dispatch at the individual wind farm, the battery contributes to minimize the wind power variations and controls wind farm power output within a pre-set value range. For the contingency support, the battery contributes to absorb energy (8 MW during four hours maximum) in order to minimize wind farm curtailments during the time the contingency is on the system.

A. Proposed BESS and control algorithm The proposed system is a 32 MW-hr battery. The BESS controls are set so the battery modes of operation are as follows: 1. Battery regulates, based on hourly dispatch profile, when the wind farm maximum power output (Pmax) is less than 80%. 2. When wind power output is more than 80%, the battery discharges at State of Charge (SOC) of 30% in preparation for a potential contingency. No hourly dispatch is done during the time Pmax is at 80% or more. 3. If a contingency happens, the battery absorbs energy to minimize wind farm curtailments and avoid transient instability during the time following the contingency on the system. 4. If the contingency is fixed before the battery SOC is at 100% and Pmax is less than 80%, battery goes back to hourly dispatch. 5. If the contingency is not fixed and the battery gets 100% SOC, wind curtailments take place.

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The authors of this paper used proprietary models for the BESS and the control algorithms for the applications previously described. These simulations have been performed in PSCAD software and the simulation results are presented as a sample of the possible battery operating modes. The wind profile presented in Figure 1 is used for these results.

B. Hourly dispatch results The BESS is applied to minimize the wind’s variability at an individual wind farm through an hourly dispatch. We assume that we can forecast the average wind power output for the next hour with 10% mean absolute error of the individual wind farm (50-52 MW peak power) [5,6]. For this application, the BESS will compensate the differences between the hourly dispatch level, Pset, which comes from the forecast, and the wind farm power output, Pwind. The power at the battery, Pbess, then can be expressed as Pbess=Pset-Pwind. The basic assumptions regarding the battery include AC/DC converter losses of 3%, the State of Charge (SOC) of the battery is allowed to change between 30% and 100% and each battery contributes the same amount of current (uniform SOC among battery cells). The results of dispatching the wind farm are shown in Figure 9 (a) for low wind generation and Figure 9 (b) for high wind generation.

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Figure 9: Dispatching of wind farm power with BESS; Pset: desired set point, Pwind: wind power, Ptotal: net injected power (Pwind + Pbess, in MW) (a)

During low wind generation (b) During high wind generation

It is seen from Figure 9 (a) that we can dispatch the wind power with the help of the BESS during low wind generation. From Figure 9 (b) the BESS can also help to absorb the excess generation when wind power is high as long as the SOC of the BESS is within its limits.

C. Contingency Support results The BESS can also be used to absorb the wind generation during a contingency so wind curtailment could be avoided. The BESS performance under a contingency occurring at 24th hr is seen in Figure 10. From the figure, it can be seen that before the contingency the battery SOC is kept low so that it can be ready to absorb the power if the contingency occurs. When the contingency occurs at 24th hr, the BESS begins to charge and limit the wind production from 53 MW (before the contingency) to 45 MW for two and half hours, i.e., until the BESS is fully charged.

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(b) Figure 10: Desired Power Output (MW) for BESS application during contingency (a) Pset: desired set point, Pwind: wind power, Ptotal: net injected power (in megawatts) (b) State of charge of one battery (normalized to unity)

The operational modes of the battery to support a 50 MW hourly dispatch as well as for contingency support have been presented. The 32 MWh battery is capable of dispatching about 50 MW of the total capacity of the surrounding wind farms (270 MW peak power).

V. CONCLUSIONS This paper has presented the application of STATCOM - BESS to help alleviate some of the problems encountered with wind farm integration at SCE power system. The paper has characterized a number of issues in the Southern California Edison system where there is abundant wind generation.

The benefits of the application of 8 MW/4 hours Battery Energy Storage System (BESS) / 20 MVAr STATCOM to address the problems at a wind power generation rich area were identified. First, for contingency support in terms of MW and MVAr; applying the STATCOM-BESS system prevents

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the power system from collapsing for the two critical contingencies. Second, for voltage profile support; applying the STATCOM-BESS system results in improved voltage recovery, about 10-15%. The fault ride-through for close-by wind farms is improved.

This paper showed that applying an hourly dispatch at an individual wind farm, the battery contributes to minimizing the wind power variations and controls wind farm power output within a pre-set value range. For the contingency support, the battery contributes to absorb energy (8 MW during four hours maximum) in order to reduce wind farm curtailments during a contingency and system conditions of high generation and low local load that causes transmission overloading. The operational modes of a 8 MW/4 hours battery to support hourly dispatch for a 50-52 MW peak power output wind farm and contingency support has resulted to be effective.

At the time of this writing, the study team is currently performing other studies within the SCE system and investigating potential solutions for the problems at these locations.

VI. ACKNOWLEDGMENTS This paper has been supported in part by funds from the

California Energy Commission.

VII. REFERENCES [1] CIGRE TF38.01.10, “ Modeling New Forms of Generation and storage”

2001 [2] J. McDowall, “Conventional battery technologies—Present and future,”

in Proc. 2000 IEEE Power Engineering Society Summer Meeting, vol. 3, July 2000, pp. 1538–1540.

[3] M. A. Casacca, M. R. Capobianco, and Z. M. Salameh, “Lead-acid battery storage configurations for improved available capacity,” IEEE Trans. Energy Conversion, vol. 11, pp. 139–145, Mar. 1996.

[4] N.W. Miller, R. S. Zrebiec, R.W. Delmerico, and G. Hunt, “Design and commissioning of a 5 MVA, 2.5 MWh battery energy storage,” in Proc. 1996 IEEE Power Engineering Society Transmission and Distribution Conf., 1996, pp. 339–345

[5] M. Ahlstrom, L. Jones, R. Zavadil, W.S Grant, “The future of wind forecasting and utility operations,” IEEE Power and Energy Magazine, pp. 57-64, no.6, Nov.-Dec. 2005.

[6] B. Ernst, “Wind Power Forecast for the German and Danish Networks,” Wind Power in Power Systems, T. Ackermann, Ed. John Wiley & Sons, 2005, pp. 365-381.

VIII. BIOGRAPHIES

Juan Castaneda (M ‘00) earned the Electrical Engineer degree is a Senior Electrical Engineer in the Grid Advance Department of the T&D Business Unit at Southern California Edison. He has being at SCE for six years. As a studies engineer he has conducted electromagnetic transients studies (EMTP-type computer simulations) for several transmission projects as well as failure analysis of high voltage equipment. He has also worked in the area of dynamic protection relay testing using time domain computer simulations. Juan received his Electrical Engineering Degree from California State University Long Beach (CSULB) in 1998 and a Master in Electrical Engineering from the University of Southern California (USC) in 2008. He is a registered professional engineer in the State of California and a member of the IEEE and PES societies.

Johan Enslin (M’85, SM’00) is Vice President of Sustainable department of Quanta Technology. He is an expert on the interconnection issues of large-scale, on-shore and offshore wind parks and solar farms to the high and medium voltage electrical grids. These include feasibility, system impacts, interconnection options, dynamic modelling, stability, transient, network grid upgrades, power balancing and harmonic system studies for different on- and offshore grid topologies using HVDC, HVAC and energy storage technologies. Previously he also designed, developed and commercialized back-to-back converters for wind and solar generators.

David Elizondo (S’99, M’03) is a Senior Advisor for Quanta Technology, Raleigh, NC . Dr. Elizondo has a broad range of experience in electric power transmission and distribution which includes: extensive experience in power system analysis and simulations such as load flow, short circuit, transient angular stability, contingency analysis, transient voltage stability, and reactive power compensation. Dr. Elizondo has recent experience with the integration of renewable energy into the electric power system and has extensive utility-based project experience with key analysis tools such as PSS/E, PSLF, Power Factory, Power World and ETMSP.

Nagy Abed (M’00) is with Quanta Technology. He received his B.Sc. (The first Rank on the class) and M.Sc. from Mansoura University, Egypt, and his PhD from Florida International university, Miami. His research interests include power system modeling, fault diagnosis, power quality, FACTS devices, Application of Finite Element in power system and real time control with HIL. He published more than 43 articles and technical papers in refereed Journals and Conference Proceedings. He is a member of IEEE and reviewer for IEEE transactions and conferences. Nagy Abed can be contacted at: nyabed@ieee.org; nabed@quanta-technology.com

Sercan Teleke (S’08) was born in Ankara, Turkey, in 1983. He received the B.S. degree in electrical and electronics engineering from Middle East Technical University, Ankara, in 2005 and the M.S. degree in electric power engineering from Chalmers University of Technology, Goteborg, Sweden, in 2006. He is currently pursuing Ph.D. degree in electrical engineering at North Carolina State University, Raleigh. His research interests are in the areas of power electronics applications to power systems, and integration of renewable energy sources using energy storage.