储能科学与技术 ›› 2022, Vol. 11 ›› Issue (1): 275-282.doi: 10.19799/j.cnki.2095-4239.2021.0265

• 储能测试与评价 • 上一篇    下一篇

充电场站光储充控制策略

罗恒1(), 严晓2(), 王钦3, 胡波1   

  1. 1.复旦大学信息科学与工程学院，上海 200433
    2.香港大学香港量子人工智能实验室，香港 999077
    3.美国电力科学研究院，美国 加利福尼亚州 94304
  • 收稿日期:2021-06-11 修回日期:2021-07-09 出版日期:2022-01-05 发布日期:2022-01-10
  • 通讯作者: 严晓 E-mail:2642747376@qq.com;sean.x.yan@ms-battery.cn
  • 作者简介:罗恒（1995—），男，硕士研究生，研究方向为电动汽车有序充电、微电网运筹优化调度等，E-mail：2642747376@qq.com|严晓，博士生导师，研究方向为锂电池储能的衰老规律、检测诊断方法、数字化运维，以及与新能源、充电和电网的关系，E-mail：sean.x.yan@ms-battery.cn。

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

Heng LUO1(), Xiao YAN2(), Qin WANG3, Bo HU1   

  1. 1.School of Information Science and Engineering, Fudan University, Shanghai, China
    2.Hong Kong Quantum Artificial Intelligence Laboratory, The University of Hong Kong, Hong Kong 999077, China
    3.American Electric Power Research Institute, State of California 94304, USA
  • Received:2021-06-11 Revised:2021-07-09 Online:2022-01-05 Published:2022-01-10
  • Contact: Xiao YAN E-mail:2642747376@qq.com;sean.x.yan@ms-battery.cn

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摘要：

针对充电需求带来的负荷具有不确定性，以及在不控制的情况下会在高峰期造成充电场站变压器过载等问题，本文提出了一种通过配置光伏和储能来提升充电场站的有效充电功率或服务能力的方法，并通过优化控制算法达到负荷跟踪的效果。该方法以每日光伏发电功率、用户负荷功率和每日分时电价为输入量。通过对3种应用场景的对比，可以定量计算出光伏和储能协同控制所带来的收益。为了对模型进行有效求解，本文提出和对比了两种不同的算法：粒子群算法和混合整数线性规划算法。这两种算法均可以用于确定储能控制策略并优化光储能系统的出力，但各有优缺点。计算结果表明，简单的低价充电、高价放电的充放电模式，不能快速应对变化的负荷功率；基于粒子群算法的储能控制策略能解决负荷跟踪问题并求得局部最优解，但达不到储能的最大利用率；而基于混合整数线性规划算法，可求得全局的最优解，达到接近100%的储能利用率，同时降低用户的日运行成本。通过动态调整储能充放电功率，可实现跟踪负荷功率、降低负荷峰值以避免变压器过载的功能，同时达到灵活应对复杂、多变的电力需求侧和供给侧的动态变化场景的目的。

关键词: 储能利用率, 粒子群算法, 混合整数线性规划算法

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

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