# Intelligent Control Algorithm for Distributed Battery Energy Storage Systems

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2 Figure 1: Trends in utilization of Stationary storage systems and future forecast by region Figure 1 shows the trends in use of battery storage systems. It also forecasts that the utilization of battery technologies may exceed MW by Batteries have limited life cycles and calendar life. Battery capacity fades with each cycle depending on the depth of discharge [11]. For same operation DOD for smaller batteries will be higher than DOD for batteries with higher capacity. Coordinated control systems can be used to control this function which transfers the load to BESS with higher capacity and BESS with lower capacity has lower priority of operation. II. CONTROL VARIABLES A. Power Generation and Load For stable operation of system a balance between power generated by power plants and the power utilized by the load should be equal. BESS are added in the system to mitigate variable output of solar. The sum of power produced by PV, power utilized by load and power provided or absorbed by batteries should be equal to zero. Pgen+Pbat+Pload=0 (1) B. Stored Energy Stroed energy (SE) is the total energy that is available in battery. It is the product of battery state of charge (SOC), voltage (Vbat) and the maximum charge storage capacity of the battery (MC). SE =Vbat SOC MC (2) Where: SE is the stored energy in the battery at any instance. MC is the maximum capacity of the battery. III. CONTROL ALGORITHM Two sets of algorithm are running for coordinated control of BESS. A. BESS Operation Mode Control: BESS operation mode is decided in the central control.

3 Figure 2: Operation mode control algorithm The system takes the generation and load demand in real time. This algorithm has three cases given below: 1- If the real time generation is higher than load demand then a signal to turn on charging of BESS is generated. 2- If the real time generation is equal to load demand then BESS are not operational. 3- If the real time generation is less than load demand then a signal to turn on discharging mode of BESS is generated. B. Discharge Control: Discharge function in the algorithm is called when the central control detects that power deficit between generation and load. Figure 3: Discharge function flow diagram

4 Discharge function takes the battery parameters as input, calculates the energy stored in each battery and turns on BESS one by one based on priority of their stored energy. IV. SYSTEM DESIGN FOR SIMULATION A test system is designed in SIMULINK to implement the proposed algorithm. Fig. 4 shows the Simulink model of test system and Fig. 6 shows the block diagram of test system. It consists of a solar power plant, three battery energy storage systems, dynamic load and a control center to monitor and control the system. Figure 4: Simulink model of test system Figure 5: Block diagram of test system

5 A. Solar Power Plant Solar power plant modeled in the test system is 100 KW. Input temperature and irradiance can be controlled by using sample data curves. Incremental conductance MPPT technique is used with integral regulator for reliable operation. A solar inverter is connected to the output to PV panels which converts the DC voltage to three phase AC voltage. Figure 6: 100KW PV plant simulink model Fig. 6 shows the simulink model of PV plant developed in the test system. MPPT controller controls the pulses for boost controller at the output of solar panel to regulate DC voltage. B. Battery Energy Storage System Battery energy storage system as shown in Fig. 7 a battery is connected to an ideal switch which controls the SOC limits of the battery. It is then connected to a bidirectional controller which is controlled centrally by coordinated control. Figure 7: Battery energy storage system Simulink model

6 C. Dynamic Load Module Fig. 8 represents the simulink model of dynamic load in the test system. Active and reactive power of load can be controlled by input load curves. Figure 8: Load representation in Simulink V. RESULTS: Simulations are performed for different cases with initial parameters such as SOC and battery capacity in AH are given below: SOC 1 =SOC 2 = SOC 3= 80% BESS 1= 10 AH BESS 2= 20 AH BESS 3= 8 AH Inverter rating: 15 KVA Under different load and generation conditions different results are obtained. Output consists of 4 curves. Operation mode is 1 when discharge operation is required and is 0 when charging operation is required. BESS 1 to BESS 3 represent if that specific BESS is operation or not. 1 represents it is operational and 0 represents it is idle. A. Case 1: Charging In this case PV generation is 50KW and load is 20KW, thus there is excess power in the system. Fig. 9 shows the the output of the system. Here operation mode is 0 indicating charging mode of bidirectional converters. All three of BESS are at level 1 indicating that these are operational and absorbing excess power from the system. Figure 9: Simulation output for case 1 B. Case 2: No BESS operation In this case PV generation is 20KW and load is 20KW thus no battery operation is required. Fig. 10 shows the output of the system. Here operation mode is 0 indicating charging mode of bidirectional converters. All three of BESS are at level 0 indicating that these are disconnected from the system.

7 Figure 10: Simulation output for case 2 C. Case 3: Discharging operation In this case PV generation is 20KW and load is 30KW, thus power is required from BESS. Fig. 11 shows the output of the system. Here operation mode is 1 indicating discharging mode of bidirectional converters. Only BESS 1 is operational because it has more stored charge due to higher capacity and same SOC level as other BESS. In this case only one inverter is enough to supply power to the load i.e. 10 KW. Figure 12: Simulation output for discharging two BESS When load is further increased to 40KW and generation is kept at 10KW then all three inverters will turn on in discharging mode as shown in Fig 13. Figure 13: Simulation output for discharging three BESS Figure 11: Simulation output for discharging one BESS Similarly, when the generation is set to 10KW and load is 30 KW. Two inverters will be operational to supply power to load. BESS 1 and 2 have higher capacity then BESS 3 so they are turned on in discharging mode as shown in Fig. 12. CONCUSLION Battery energy storage systems provide an effective way to mitigate the variability of renewable energy. By using intelligent control algorithms battery lifetime and efficiency of the system can be achieved. In this paper the proposed algorithm for the selection of battery operation based on stored energy and maximum capacity. This algorithm can be implemented in systems with distributed battery energy storage systems. It has been shown that the proposed algorithm is effectively selecting the batteries operation mode based on system parameters and discharging batteries with higher stored energy on priority. By doing so depth of discharge for batteries can be controlled thus utilizes the limited lifecycles more efficiently.

8 REFERENCES [1] Trenberth, K., et al., Observations: surface and atmospheric climate change. Chapter 3. Climate change, 2007: p [2] Cline, W.R., The economics of global warming. 1992: Institute for International Economics. [3] Rogelj, J., et al., Paris Agreement climate proposals need a boost to keep warming well below 2 C. Nature, (7609): p [4] Pittock, A.B., Climate change: turning up the heat. 2017: Routledge. [5] Painuly, J.P., Barriers to renewable energy penetration; a framework for analysis. Renewable energy, (1): p [6] Singh, M., et al., Grid interconnection of renewable energy sources at the distribution level with power-quality improvement features. IEEE transactions on power delivery, (1): p [7] Divya, K. and J. Østergaard, Battery energy storage technology for power systems An overview. Electric power systems research, (4): p [8] Oudalov, A., R. Cherkaoui, and A. Beguin. Sizing and optimal operation of battery energy storage system for peak shaving application. in Power Tech, 2007 IEEE Lausanne IEEE. [9] Nykvist, B. and M. Nilsson, Rapidly falling costs of battery packs for electric vehicles. nature climate change, (4): p [10] Vrettos, E.I. and S.A. Papathanassiou, Operating policy and optimal sizing of a high penetration RES-BESS system for small isolated grids. IEEE Transactions on Energy Conversion, (3): p [11] Ning, G., B. Haran, and B.N. Popov, Capacity fade study of lithiumion batteries cycled at high discharge rates. Journal of Power Sources, (1-2): p

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