基于改进粒子群算法的分布式光伏及储能系统优化配置

doi:10.3969/j.issn.2097-0706.2023.01.006

摘要/Abstract

摘要：

“碳达峰、碳中和”目标的提出要求进一步推进分布式电源接入配电网，针对分布式光伏并网引起的不确定性问题，研究了分布式光伏及储能的优化配置，采用系统聚类筛选光伏的典型出力场景用于储能规划，综合考虑场景的经济效益、负荷的波动及削峰填谷率等，建立光伏及储能优化配置的混合整型非线性规划模型。采用自适应粒子群算法进行求解，分析不同负荷、不同电价时段对储能容量配置及系统运行的影响。算例结果表明，引入储能系统后可有效平抑光伏出力的不确定性，优化负荷曲线，同时提高系统的整体运行性能，验证了模型的可行性与求解方法的有效性。

关键词: 碳中和, 分布式光伏, 储能系统, 系统聚类, 改进粒子群算法, 容量配置, 分布式电源, 配电网

Abstract:

The proposal of the goals of carbon peaking and carbon neutrality makes a further request on the grid connection of distributed energy systems. To address the uncertainty triggered by the grid-connected distributed photovoltaic （PV） systems， the optimal configuration of distributed PV and energy storage systems is studied. Based on the typical PV output scenarios selected by clustering process， a hybrid integral non-linear programming model for the optimal configuration of a photovoltaic and energy storage system was established while taking economic benefits， load fluctuation and peak-shaving rates into consideration. The model was solved by adaptive particle swarm optimization algorithm， and the influence of different loads and electricity prices on energy storage capacity and on the operation of the system was analyzed. The results show that the energy storage system can effectively stabilize the photovoltaic output， optimize the load curve and improve the overall operating performance of the system， which verifies the feasibility of the model and the effectiveness of the solution.

Key words: carbon neutrality, distributed photovoltaic, energy storage system, clustering, improved particle swarm algorithm, capacity allocation, distributed power source, distribution network

中图分类号:

TK51：TP301.6

胡祖源, 靳现林, 谭雅之, 樊静宜. 基于改进粒子群算法的分布式光伏及储能系统优化配置[J]. 综合智慧能源, 2023, 45(1): 49-57.

HU Zuyuan, JIN Xianlin, TAN Yazhi, FAN Jingyi. Optimized configuration of distributed photovoltaic and energy storage system based on improved particle swarm algorithm[J]. Integrated Intelligent Energy, 2023, 45(1): 49-57.

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测试函数	搜索成功率/%		平均搜索时间/s
测试函数	PSO	AWP-PSO	PSO	AWP-PSO
Griewangk	65	91	3.174	4.172
Rosenbrock	69	93	2.316	3.917

参数	数值
单位储能成本/[元·(kW·h)^-1]	2 830
单位发电功率运维成本/[元·(kW·h)^-1]	0.087
规划年限/a	20
光伏上网电价/[元·(MW·h)^-1]	900
光伏单位弃光成本/[元·(kW·h)^-1]	2 400
所允许节点的电压最大标幺值	1.05
所允许节点的电压最小标幺值	0.95
储能电池最小荷电状态/%	10
储能电池最大荷电状态/%	90

时段		电价
高峰段	10：00—15：00，18：00—21：00	0.86
平时段	07：00—10：00，15：00—18：00，21：00—23：00	0.51
低谷段	00：00—07：00，23：00—24：00	0.20

节点编号	a_j /[元·(MW·h)^-2]	b_j /[元·(MW·h)^-1]
1	0.003 75	2.00
2	0.001 75	1.75
5	0.062 50	1.00
8	0.008 34	3.25
11	0.002 50	3.00
13	0.002 50	3.00

典型日	概率	典型日	概率
01-03	0.125	05-31	0.045
02-18	0.144	08-07	0.151
03-21	0.121	08-14	0.086
05-16	0.066	09-13	0.148