基于孔雀优化算法的配电网储能系统双层多目标优化配置

doi:10.16183/j.cnki.jsjtu.2021.371

摘要/Abstract

摘要：

考虑电池储能系统(BESSs)规划与运行之间的联系,建立兼顾经济性和技术性要求的BESSs多目标优化配置模型并进行双层架构,保证BESSs规划的有效性和运行的高效性.内层以BESSs运营收益最大为目标,提出孔雀优化算法求解BESSs充放电运行策略的最优解;外层以BESSs投资运维成本、配电网电压波动和负荷波动最小为目标,设计多目标孔雀优化算法求解选址定容规划方案的Pareto非支配解集.考虑配电网运行条件的不确定性,采用聚类算法获得典型场景集,并基于IEEE-33节点系统进行仿真.结果表明:所提算法实现了局部探索和全局搜索的平衡,有效获得高质量解;与传统多目标优化算法相比,其能够获得分布更广泛且均匀的Pareto前沿,实现BESSs投资效益最优,显著提升配电网电压质量和功率稳定性.

关键词: 电池储能系统, 双层优化配置模型, 孔雀优化算法, Pareto多目标优化

Abstract:

Based on the relation between battery energy storage systems (BESSs) planning and operation, a multi-objective optimal allocation model that takes into account both economic and technical requirements is established, and a bi-level optimization structure is constructed to ensure effective planning and high-efficient operation of BESSs. In the inner layer, a peafowl optimization algorithm (POA) is employed to solve the BESSs charge-discharge operation strategy with the purpose of BESSs operation benefit maximization. In the outer layer, a multi-objective peafowl optimization algorithm (MOPOA) is devised to solve the Pareto solution set of BESSs siting and sizing scheme, which aims at minimizing BESSs cost, as well as voltage fluctuation and load fluctuation in distribution network. Furthermore, a typical scenario set is obtained via the clustering algorithm considering uncertain operating conditions. The simulation is performed based on the extended IEEE-33 bus system. The results show that the proposed algorithm achieves a trade-off between local search and global search, thus obtains a high-quality solution. It can obtain a more widely distributed and uniform Pareto front, which not only achieves the best investment benefit, but also improves voltage quality and power stability.

Key words: battery energy storage systems (BESSs), bi-level optimal allocation model, peafowl optimization algorithm (POA), Pareto based multi-objective optimization

中图分类号:

杨博, 王俊婷, 俞磊, 曹璞璘, 束洪春, 余涛. 基于孔雀优化算法的配电网储能系统双层多目标优化配置[J]. 上海交通大学学报, 2022, 56(10): 1294-1307.

YANG Bo, WANG Junting, YU Lei, CAO Pulin, SHU Hongchun, YU Tao. Peafowl Optimization Algorithm Based Bi-Level Multi-Objective Optimal Allocation of Energy Storage Systems in Distribution Network[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1294-1307.

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场景序号	组合方式	t/d
1	负荷2/风电4/光伏1	120
2	负荷2/风电4/光伏3	84
3	负荷4/风电4/光伏2	83
4	负荷2/风电3/光伏2	78

成本参数	数值
C_inv/(美元·台^-1)	1×10⁶
a/(美元·MW^-1)	137×10⁶
b/(美元·MW^-1·h^-1)	98×10⁵, s.t. C_rate=0.5C
	190×10⁶, s.t. C_rate =C
	373×10⁶, s.t. C_rate =2C
	734×10⁶, s.t. C_rate =4C
λ/(美元·kW^-1·h^-1)	0.1
ρ_om/%	5
r/%	6.33
η_cha/%	95
η_dis/%	95
δ/%	1

算法	标准	F₁/元	F₂(p.u.)	F₃/MW
MOPSO-PSO	最差值	9.6331×10⁵	15.7440	335.49
	最好值	4.3513×10⁵	0.3392	325.22
	平均值	4.7894×10⁵	0.6860	335.08
	折中解	4.5364×10⁵	0.6549	334.83
NSGAII-GA	最差值	6.9207×10⁵	3.3892	338.61
	最好值	2.5263×10⁵	0.3431	321.21
	平均值	4.1647×10⁵	0.7902	330.98
	折中解	3.4864×10⁵	0.5931	328.84
MOGWO-GWO	最差值	3.6801×10⁵	33.6600	508.55
	最好值	-3.7789×10⁵	0.2488	298.90
	平均值	-8.3822×10⁴	8.2495	379.74
	折中解	-2.1783×10³	0.4436	298.90
MOPOA-POA	最差值	5.8130×10⁵	35.5390	538.81
	最好值	-1.7104×10⁶	0.3138	282.56
	平均值	-2.9754×10⁵	9.3221	387.86
	折中解	-3.5192×10⁵	0.3736	328.64

算法	GD	IGD	SP	广泛性	PD	DM	HV
MOPSO-PSO	6314	2.798×10⁵	2.798×10⁵	1.11	7.918×10⁷	0.267	0.1878
NSGAII-GA	2994	2.266×10⁵	2.266×10⁴	1.012	7.691×10⁷	0.344	0.1015
MOGWO-GWO	9469	1.038×10⁵	1.038×10⁵	1.358	5.468×10⁸	0.698	0.2961
MOPOA-POA	872.9	1.568×10⁴	1.988×10⁴	0.969	7.47×10⁸	0.889	0.2855

算法	BESSs优化配置方案			内层优化结果 f/元	ω₁	ω₂	ω₃	外层优化结果
算法	位置节点	容量/(MW·h)	功率/MW	内层优化结果 f/元	ω₁	ω₂	ω₃	F₁/元	F₂(p.u.)	F₃/MW
1	(27, 33)	(0.375, 0.375)	(0.093, 0.09)	3.814×10⁴	0.554	0.429	0.017	4.536×10⁵	0.655	334.83
2	(25, 21)	(0.646, 0.612)	(0.162, 0.15)	3.319×10⁵	0.296	0.553	0.151	3.486×10⁵	0.593	328.84
3	(23, 2)	(1.426, 3.375)	(0.476, 1.222)	1.343×10⁶	0.167	0.499	0.334	-2.178×10³	0.444	298.80
4	(23, 7)	(1.827, 2.445)	(0.457, 0.611)	1.582×10⁶	0.244	0.476	0.28	-3.519×10⁵	0.374	328.64
5	(8, 3)	(0.701, 0.484)	(0.175, 0.242)	4.067×10⁵	1/3	1/3	1/3	-2.825×10⁵	5.87	328.88