Charging and discharging strategy of battery energy storage in the charging station with the presence of photovoltaic

doi:10.19799/j.cnki.2095-4239.2021.0265

Abstract

Abstract:

In view of the uncertainty of the load caused by the charging demand and the possibility that it may result in the overload of the charging station transformer during the peak period if not controlled, this study proposes a photovoltaic and energy storage configuration to improve the effective charging power or service capacity of the charging station, achieving the effect of load tracking by control algorithm optimization. This method takes the daily photovoltaic power generation, user load power, and daily time-of-use electricity price as the input. The profits brought by the cooperative control of the photovoltaic and the energy storage can be quantificationally computed by comparing three application scenarios. This study puts forward and compares two different algorithms, namely the particle swarm optimization (PSO) and the mixed integer linear programming algorithm, to effectively solve the model. The two algorithms can be applied to determine the energy storage control strategy and optimize the output of the optical energy storage system; however, both algorithms have advantages and shortcomings. The calculation results indicate that the simple charging and discharging modes of low-cost charging and high-cost discharging cannot quickly respond to the changing load power. The energy storage control strategy based on PSO can solve problems, such as load tracking, and obtain a local optimal solution, but cannot reach the maximum utilization rate of the energy storage. On the contrary, an algorithm based on mixed integer linear programming can achieve the overall optimal solution and reach nearly 100% energy storage utilization rate while reducing the users' daily operating costs. Moreover, by dynamically adjusting the charging and discharging power of the energy storage, the load power can be tracked; the peak load can be reduced to avoid transformer overload; and the purpose of dynamic changing scenarios can be achieved. This shows a flexible response to the complex and changeable power demand and supply sides.

Key words: energy storage utilization, particle swarm algorithm, mixed integer linear programming algorithm

CLC Number:

TM 912.9

Heng LUO, Xiao YAN, Qin WANG, Bo HU. Charging and discharging strategy of battery energy storage in the charging station with the presence of photovoltaic[J]. Energy Storage Science and Technology, 2022, 11(1): 275-282.

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时段	电价/[CNY/(kW·h)]
00:00—07:00	0.316
07:00—10:00	0.678
10:00—15:00	1.106
15:00—18:00	0.678
18:00—21:00	1.106
21:00—24:00	0.678

微系统设备	总成本 /元	电网峰值 /kW	平均供电 /kW
充电站不加光伏	2601.0	237.5	137.8
充电站配置光伏	2246.9	234.1	111.6
充电站加光伏和储能(一天一储充)	2082.6	234.1	111.6

微系统设备	算法	总成本 /元	电网峰值 /kW	平均供电 /kW
充电站加光伏和储能 (一天一储充)	混合整数线性规划算法	2071.7	200.0	111.6
充电站加光伏和储能 (一天两储充)	混合整数线性规划算法	2038.4	200.0	111.6

微系统设备	算法	总成本 /元	电网峰值 /kW	储能利用率 /%
充电站加光伏和储能 (一天一储充)	粒子群算法	2082.6	234.1	92.4
充电站加光伏和储能 (一天一储充)	混合整数线性规划算法	2071.7	200.0	98.6
充电站加光伏和储能 (一天两储充)	混合整数线性规划算法	2038.4	200.0	58.6

微系统设备	算法	总成本 /元	电网峰值 /kW	储能利用率 /%
充电站加光伏和储能(一天一储充)	混合整数线性规划算法	2160.6	200.0	97.1
充电站加光伏和储能(一天两储充)	混合整数线性规划算法	2141.2	214.0	59.4