考虑储能动态运行特性的充电站光储容量优化配置模型

doi:10.12204/j.issn.1000-7229.2021.05.010

摘要/Abstract

摘要：

光伏和储能技术的快速发展,为“新基建”背景下大规模充电桩接入电网提供了可靠支撑。考虑到城市充电站配置光伏以及储能系统,文章提出了光储容量优化配置方法。首先,基于储能系统的充放电特性,融合储能容量衰减、寿命损耗、动态效率特性,建立了动态的精细化储能模型。然后,在精细化储能模型基础上,以充电站成本最小化为目标建立混合整数非线性模型(mixed integer non-linear programming, MINLP),并通过分离变量转化为双层规划模型,外层利用遗传算法全局寻优,内层转化为线性规划模型求解,最终确定充电站的最优光储配置容量。最后,通过算例对模型进行仿真,仿真结果证明了模型及其求解方法的可行性,能够为电动汽车充电站的光储容量配置提供参考。

关键词: 精细化储能模型, 双层规划, 光储充电站, 光储容量配置

Abstract:

Under the policy of "New Infrastructure", the development of photovoltaic (PV) and battery energy storage (BES) technologies supplies reliable support for large scale charging piles integration into grid. This paper proposes an optimal configuration method for BES and PV in charging station. Firstly, capacity degradation, lifetime loss, and dynamic efficiency are integrated to the discharging and charging characteristics of BES to establish a refined BES model. According to this model, a mixed integer non-linear programming (MINLP) model is developed to minimize the cost of charging station. By separating variables, the MINLP is transferred to a double-layer model. The optimal solution of external layer is determined through genetic algorithm (GA), and the inner layer optimization can be solved by commercial solvers. Lastly, the double-layer model is simulated in a case study, and the simulation results validate the feasibility of the proposed model and solution methods, which could be a reference for PV and BES configuration in electric vehicle charging station.

Key words: refined battery energy storage model, double-layer programming, charging station with PV and battery energy storage, PV and battery energy storage capacity configuration

中图分类号:

TM73

桂强, 史一炜, 周云, 冯冬涵. 考虑储能动态运行特性的充电站光储容量优化配置模型[J]. 电力建设, 2021, 42(5): 90-99.

GUI Qiang, SHI Yiwei, ZHOU Yun, FENG Donghan. Optimal Configuration Model of Photovoltaic and Energy Storage Capacity of Charging Station Considering Battery Dynamic Characteristics[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 90-99.

图/表 17

图1 储能SOH与寿命损耗关系曲线

Fig.1 BES SOH versus lifetime loss

图2 放电时间与放电电流关系

Fig.2 Discharging time versus charging current

图3 基于GA的双层模型求解流程

Fig.3 Flowchart of GA-based double-layer model

表1 储能电池参数

Table 1 BES parameters

表2 储能电池SOH分段参数

Table 2 Piecewise parameters of BES SOH

图4 储能电池全寿命周期多阶段SOH

Fig.4 Multistage SOH of BES whole lifetime cycle

图5 4 MW光伏电站出力

Fig.5 Output of PV station with 4 MW installed capacity

图6 电动汽车充电负荷

Fig.6 EV charging load

表3 其他参数

Table 3 Other simulation parameters

图7 遗传算法的迭代过程

Fig.7 Iteration process of GA

表4 仿真结果

Table 4 Simulation results

图8 仿真结果

Fig.8 Simulation results

图9 储能寿命损耗成本

Fig.9 Lifetime loss cost of BESS

图10 储能容量衰减

Fig.10 BESS capacity fading

图11 未考虑精细化模型储能系统运行结果

Fig.11 Operation results of BESS ignoring refined model

表5 未考虑精细化储能模型算例结果

Table 5 Simulation results without considering refined BESS model

表6 对比算例仿真结果

Table 6 Simulation results of cases for comparison

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