Modeling and Dynamic Behavior of Battery Energy Storage: A Simple ...

By Pouyan Pourbeik, Stephen E. Williams, James Weber, Juan Sanchez-Gasca, Jay Senthil, Shengli Huang, and Kent Bolton

Modeling and Dynamic Behavior of Battery Energy Storage A simple model for large-scale time-domain stability studies. ith the continued development and proliferation of renewable energy systems worldwide, particularly wind and photovoltaic (PV) generation, computer simulation models for these technologies to be used in large interconnected power-system stability analyses have been a key focus over the past several years. Such computer simulation models are used by powersystem planners and operators to simulate various scenarios of large interconnected systems—e.g., continental Europe and the western or eastern North American grid—to assess the stability and reliability of the bulk electric power system. In Europe, much of this work has been led by efforts within the International Electrotechnical Commission, while in North America, most of the major efforts in modeling has gone through the Renewable Energy Modeling Task Force (REMTF) of the Western Electricity Coordinating Council (WECC). The total nameplate capacity of wind generation installed worldwide has surpassed 360,000 MW, and PV generation has surpassed 130,000 MW, with the top three countries in

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photo courtesy ofweican

Digital Object Identifier 10.1109/MELE.2015.2447974 Date of publication: 1 September 2015

terms of cumulative total capacity being China, the United States, and Germany. More recently, energy-storage systems, such as the battery system shown in Figure 1, are being considered and deployed in many regions in North America. Energy storage comes in many forms and different technologies and offers a means of storing energy that is often produced by renewable energy systems, such as wind generation, during off-peak hours (e.g., at night) and reusing the energy during peak demand. Also, energy-storage technologies offer many other potential benefits such as helping to reduce the variability of the power output of variable energy sources (wind and PV generation) as well as providing voltage control and frequency regulation capabilities. For

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example, in October 2013, the California Public Utilities Commission established a target of 1,325 MW of energy storage for the Pacific Gas and Electric, Southern California Edison, and San Diego Gas and Electric companies by 2020, with installations to occur no later than the end of 2024 (http://docs.cpuc.ca.gov/PublishedDocs/Published/G000/ M079/K533/79533378.PDF). One of the technologies that is being deployed is battery energy storage. As such, an ad hoc group within the WECC REMTF recently took on the task to quickly develop a simple battery energy-storage system (BESS) model for power-system stability studies. The results of that effort are presented here.

Utility Installations of Battery Energy Storage BESSs deployed for power-system applications range from several hundred kilowatts to many megawatts. In the majority of the applications today, the technology used is that of lithium-ion (Li-ion) solid-state batteries. In this type of battery, lithium ions move from the negative to the positive electrode during discharging to produce an electric current to inject into the grid and then back from the positive to the negative electrode during charging when the current is coming from the grid. Li-ion batteries present some advantages over traditionally used lead–acid batteries in that they are much lighter and thus have a significantly higher power density and can typically produce higher voltages. There are many different types of Li-ion batteries, with varying battery chemistry and performance; a detailed discussion of such aspects is beyond the scope of this article. Typically, Li-ion batteries have a relatively long cycle life and minimal self-discharging rate such that most Li-ion batteries will discharge at a rate of between 1 and 2% per month if left standing and disconnected. The complete BESS installation for power-system applications usually consists of three main parts: xx The energy-storage module is usually made up of numerous battery cells connected in parallel and series to constitute a single, large energy-storage module. For example, a single battery cell might have a rating of 12 Vdc and 1 kWh of energy. By connecting, e.g., 100 cells in series and ten such units in parallel, one can create

Figure 1. A 1-MW, 2-MWh battery (foreground) and its 1.25-MVA converter system. This system was applied for smoothing and regulation at a 10-MW wind plant on an island. (Photo courtesy of WEICan.)

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an energy-storage module capable of a maximum of 1,200 Vdc and 1 MWh. Figure 2 shows the racks of energy-storage modules for an actual BESS installation in North America. xx The power converter is a power electronic device built using high-power insulated-gate bipolar transistors (IGBTs), which allows for controller switching of the power electronic circuit to convert dc produced by the batteries to ac, at the required nominal frequency, for injection into the bulk electric power grid. For North America, the ac voltage needs to be at a frequency of 60 Hz. xx Controls are associated with the power converter, typically implemented as software uploaded into a digital control system. The controls manage all aspects of the BESS, from charging to discharging, and services such as voltage regulation, frequency regulation, and controlling variability of wind or PV generation colocated with the BESS, etc. The setup for a BESS is shown in Figure 3. The power converter is similar to the power converters used in many other applications, such as the grid interface of wind turbine generation and PVs. The power converter, using IGBTs, allows for what is known as full four-quadrant control—this is shown in Figure 4. That is, within the current rating of the power electronics, the converter is able to independently control the active and reactive current being injected into (or absorbed from) the power system. This type of power converter is called a voltage source converter (VSC).

Capabilities of Battery Energy Storage The VSC interface between the energy-storage modules (the batteries) and the power system give the BESS tremendous flexibility. The VSC is used as a grid interface in many applications, including wind turbine generators, PV generation, and even advanced pumped storage hydrogeneration. As described previously and shown in Figure 4, VSC allows for full four-quadrant control, i.e., the converter is able to take the dc voltage of the battery and independently, within the current ratings of the converter,

Figure 2. Energy-/power-storage modules inside a commercially operational BESS in North America. (Photo courtesy of Electric Power Research Institute.)

As many utilities are beginning to see BESSs being proposed and introduced into their systems, particularly in the western U.S. system, there is a need to model this technology in a simple yet effective way for large-scale power-system stability studies. In recent years, one of the key challenges with renewable generation technologies, such as wind and PV generation, has been the development of standard public computer simulations models for power-system studies. These public-domain and standard model structures, which do not specifically pertain to any vendor’s equipment, are referred to as generic models. The concept is that the model structure is flexible enough that through proper model parameterization, the model can reasonably emulate the dynamic behavior of a large range of different vendors’ equipment. A large effort to this effect was embarked upon several years ago in WECC, which culminated in the development of the second generation of renewable energy system models that can be used to model both wind and PV generation. The model specifications are publicly available on the WECC website, and have been validated with measured field data from several different wind turbine types. These second-generation models were developed with a focus on modularity so that a set of modules were developed, each representing an aspect of the renewable energy system, and thus, each specific plant can be represented by connecting together the right combination of modules, e.g., the generator/converter model plus electrical controls models plus the plant-wide controller, etc. Thus, the approach taken in developing the BESS model was to augment one of

Power Converter

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dc Power

Power Transformer/ Switch Gear

Figure 3. The parts of a BESS.

the modules to allow for modeling a BESS while using all the other modules that have already been developed and equally applicable to a BESS installation, such as the converter model, the plant controller, etc. One key point needs to be made before further discussion of the BESS model. The generic stability models developed for renewable energy systems are intended for large-scale power-system simulations, which are typically performed using simplified stability models. These models have their limitations, and so, one needs to clearly appreciate that they are not suited to every type of analysis and simulation work. This is true of any model.

Reactive Current Converter Current Limit

Quadrant 1: –ve Active Current +ve Reactive Current

Quadrant 2: +ve Active Current +ve Reactive Current

Quadrant 3: Quadrant 4: –ve Active Current +ve Active Current –ve Reactive Current –ve Reactive Current

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Modeling of Battery Energy Storage for Stability Studies

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control the real and reactive power being injected into (or absorbed from) the ac side, which is the bulk electric power system. In the case of BESS, since the batteries are rechargeable, real power can be either injected (discharging) or absorbed (charging). Furthermore, because the IGBTs are power electronic switches that can be switched at kilohertz, the converter can go from discharging to charging in a fraction of a second, resulting in a tremendously flexible and functional BESS. A BESS, if controlled properly, can provide the following services: xx voltage control and regulation at the substation where it is connected in the power system xx frequency support by either providing very fast frequency response or being part of the automatic generation control (or secondary frequency response) xx power oscillation damping, if placed in a strategic place in the power system and complemented with the proper supplemental controls, or xx help to reduce the net variability of variable generation sources if combined with or colocated with, variable generation facilities such as wind or PVs. There are other potential applications that will likely be identified as the technology continues to mature.

Charging = –ve Active Current Discharging = +ve Active Current Boosting Grid Voltage = +ve Reactive Current Reducing Grid Voltage = –ve Reactive Current Figure 4. Four-quadrant operation of the VSC interface between the energy-storage modules and the grid.



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Pref and Qref Can Be Connected to the REPC_A or REPC_B Plant Controller Models

reec_c regc_a Iqcmd’

Q Control

Qref

Current Limit Logic

Ipcmd’

P Control

Pref

Iqcmd

Ipcmd

Pqflag = 1 (P priority) = 0 (Q priority)

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Figure 5. A block diagram of the BESS model, excluding the plant controller.

With the aforementioned background in mind, the BESS model concept is shown in Figure 5. The existing renewable energy generator/converter (REGC_A) model, without any modifications, is used to represent the power converter interface between the batteries and the grid. Then, the existing renewable energy electrical control (REEC) model is augmented to add to it a feature to allow for both charging and discharging with a simple representation of the battery storage. This simple representation of the charge/discharge mechanism is shown in Figure 6. The new renewable energy electrical controller type C (REEC_C) incorporates this simple charging/discharging model. This new module, when incorporated with the REGC_A model, can then represent a BESS unit. Furthermore, the plant controller modules can be used with these models to allow one to emulate various functionalities such as frequency regulation. The added new feature shown in Figure 6 has the following key features: xx A representation of the initial state of charge (SOC) of the battery—this is a user input to the model, which tells the model how much charge the battery currently has prior to the simulation being initiated.

SOCmax Power Generated

1 T.s

–

SOC +

Initial SOC

xx A representation of the maximum and minimum

allowable SOC (shown as SOCmax and SOCmin)—most battery manufacturers will recommend that the battery not be left in a state of full charge or full d ­ ischarge to preserve the battery’s longevity and performance. Thus, the model can simulate this by specifying the maximum and minimum recommended SOC during operation, which is something the actual controls would also respect and enforce. Many vendors recommend operating the batteries within a range of 20–80% SOC. xx The simple integrator block, with the time constant T, which represents the process of charging and discharging, can be understood by considering the fact that the level of charge in the battery is proportional to stored energy. Energy is the time integral of power since power is specified in units of watts = joules (energy) per second. Thus, by integrating the power coming out of (or going into when charging) the BESS, we get a representation of the SOC. xx The logic block at the end of the model represents the action of collapsing the output of the converter (i.e., forcing its active current output) to zero once the maximum or minimum SOC has been reached.

If SOC ≥ SOCmax Ipmin = 0 elseif SOC ≤ SOCmin Ipmax = 0

SOCmin

Figure 6. A block diagram of the charging and discharging mechanism of the BESS model.

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Maximum Active Current Limit (Ipmax) Minimum Active Current Limit (Ipmin)

Real Power (pu)

There are several clear assumptions inherent in this model. First and foremost, the battery chemistry is ignored, as the details are not pertinent to simple stability studies. Also, the details of the dynamics of the dc current circuitry are neglected since, again, it is assumed that this is not particularly relevant to power-system studies. Finally, the model is a positive sequence model, which is meant for stability analysis and cannot represent a detailed three-phase response of the equipment as might be needed for design studies.

Summary The impetus to develop and install renewable generation in the bulk power system continues and has accelerated in recent years. In 2014, wind generation capacity increased worldwide by more than 51 GW, which was a 45% increase compared the amount installed in 2013. With this continued increase in renewable energy deployment throughout the world, it is inevitable that energy storage will soon start to find a key function to play in the power systems of the future. One of these technologies is battery energy storage. Modeling these technologies will be equally important for transmission planners and operators as they continue to perform studies at all levels to assess system reliability. This article discussed a simple model for use in large-scale time domain stability studies. There is no doubt that more sophisticated models will be needed for other applications and analysis, and this type of work will continue.

Further Reading Lithium Ion Technology Status and Directions: 2013 Update. Palo Alto, CA: EPRI, 2013. [Online]. Available: http://www.epri.com/abstracts/Pages/ ProductAbstract.aspx?ProductId=000000003002001314 (2014, 23 Jan.). Specification of the second generation generic models for wind turbine generators. WECC Approved

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Modeling Verifications The Electric Power Research Institute, through a nondisclosure agreement under a separate project, was able to obtain actual field data from an actual BESS unit in operation in the United States. The battery is a 36-MVA unit capable of 24 MWh for 3 h. The vendor requires that it be always operated in an SOC between 20 and 80%. The measured data from one operating condition were taken, and we simulated the sequence of power reference (Pref) changes for which the actual response of the battery was recorded. The results are shown in Figure 7 and demonstrate that the fit is quite good. Also, although not shown here, tests have been done to confirm similar results can be achieved in the beta implementation of the model in commerical software platforms that have adopted this model. The model was recently approved at a meeting of the WECC Modeling and Validation Working Group and should in due course be released in the official version of several commercial software platforms that are widely used in North America.

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Figure 7. Simulation and measurement of an actual 36-MVA BESS unit’s response to a sequence of Pref changes (device in constant pf control): (a) the real power response per unit, and (b) the reactive power response per unit.

Document. [Online]. Available: http://www.wecc.biz/Reliability/WECC%20Second%20Generation%20Wind%20Turbine%20 Models%20012314.pdf (2013). Technical Update—Generic Model for Wind Turbine Generators and Photovoltaic Generation and Model Validation. Palo Alto, CA: EPRI. Product ID# 3002001002. [Online]. Available: http://www.epri.com/abstracts/Pages/ProductAbstract.aspx? ProductId=000000003002001002&Mode=download. A. Ellis, P. Pourbeik, J. J. Sanchez-Gasca, J. Senthil, and J. Weber, “Generic wind turbine generator models for WECC— A second status report,” in Proc. IEEE PES General Meeting 2015, Denver, CO, paper 15PESGM0126.

Biographies Pouyan Pourbeik ([email protected]) is a senior technical executive with the Electric Power Research Institute in Irving, Texas. Stephen E. Williams is a principal engineer with S&C Electric Company in Franklin, Wisconsin. James Weber is the director of software development at PowerWorld Corporation in Champaign, Illinois. Juan Sanchez-Gasca is technical director at GE Energy Inc., in Schenectady, New York. Jay Senthil is a senior staff engineer at Siemens PTI Inc., in Schenectady, New York. Shengli Huang is a senior engineer at Puget Sound Energy in Bellevue, Washington. Kent Bolton is a staff engineer, planning services, at the Western Electricity Coordinating Council in Salt Lake City, Utah. 

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