基于改进DE算法的园区微电网风光储优化配置

doi:10.3969/j.issn.2097-0706.2025.09.008

摘要/Abstract

摘要：

针对传统差分进化（DE）算法在多园区微电网风光储系统优化配置中易陷入局部最优、物理可解释性弱的局限，提出了改进DE算法与物理机理融合的优化框架。构建以日供电成本最小化为目标的风光储配置模型，嵌入储能充放电效率及荷电状态约束；设计三重自适应改进DE算法：采用双阶段线性衰减机制调节缩放因子和交叉概率，融合精英历史经验复用策略提升收敛速度，引入双模振荡扰动增强多样性；从源荷适配的物理本质出发，剖析储能配置与风光负荷曲线的内在规律。算例表明：改进DE算法较传统DE、粒子群和遗传算法效果更好，联合运行成本降至15 424.06元；联合运行的供电成本较独立运行总和的供电成本降低6.11%，储能总功率与容量分别节省30.77%和50.00%，弃风弃光量归零；储能配置具有最大单时段弃电量决定功率、最大连续弃电总量决定容量的普适规律，基于此的B园区物理估算方案成本为5 065.43元，较优化算法结果（5 066.22元）更低。通过算法改进与物理规律挖掘，为风光储系统优化配置提供了高精度、强可解释性的解决方案。

关键词: 风光储微电网, 协同调度, 储能配置, 改进差分进化算法

Abstract:

To address the limitations of traditional differential evolution （DE） algorithms—susceptibility to local optima and weak physical interpretability—in the optimal configuration of wind-solar-storage systems in multi-park microgrids， an optimization framework integrating an improved DE algorithm with physical mechanisms was proposed. A wind-solar-storage configuration model was established with the objective of minimizing daily power supply costs， incorporating constraints on energy storage charging/discharging efficiency and state of charge. A triple-adaptive improved DE algorithm was designed： a dual-phase linear decay mechanism was used to adjust the scaling factor and crossover probability， an elite historical experience reuse strategy was integrated to enhance convergence speed， and a dual-mode oscillatory disturbance was introduced to increase population diversity. From the physical essence of source-load matching， the intrinsic patterns between energy storage configuration and wind-solar load curves were analyzed. Case studies showed that： the improved DE algorithm outperformed traditional DE， particle swarm optimization， and genetic algorithms， reducing the joint operating costs to 15 424.06 yuan； the joint operation reduced the power supply cost by 6.11% compared to the sum of independent operations and saved 30.77% of total energy storage power and 50.00% of capacity， with wind and solar curtailment reduced to zero； energy storage configuration followed a universal pattern that the power rating was determined by the maximum single-interval curtailment， and the capacity was determined by the maximum consecutive curtailment. Based on this， the physical estimation scheme for Park B reduced the coet to 5 065.43 yuan， which was lower than the result obtained by the optimization algorithm （5 066.22 yuan）. By combining algorithm improvement with the exploration of physical patterns， a high-precision and strongly interpretable solution for the optimal configuration of wind-solar-storage systems is provided.

Key words: wind-solar-storage microgrid, coordinated scheduling, energy storage configuration, improved differential evolution algorithm

中图分类号:

TK018：TM73

张元曦, 杨国华, 马龙腾, 马鑫, 刘耀泽. 基于改进DE算法的园区微电网风光储优化配置[J]. 综合智慧能源, 2025, 47(9): 71-79.

ZHANG Yuanxi, YANG Guohua, MA Longteng, MA Xin, LIU Yaoze. Optimized wind-solar-storage configuration of industrial park microgrids based on improved differential evolution algorithm[J]. Integrated Intelligent Energy, 2025, 47(9): 71-79.

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性能	改进DE	DE	PSO	GA
时间/s	14.470	14.294	14.402	14.727
功率/kW	447.781	428.983	440.464	422.171
容量/(kW·h)	1 468.519	1 443.895	1 459.769	1 428.079
成本/元	15 424.06	15 426.51	15 424.79	15 429.24

指标	园区			A, B, C总和	A, B, C联合
指标	A	B	C	A, B, C总和	A, B, C联合
总购电量/kW	4 874.125	2 432.300	2 699.390	10 005.815	8 266.270
弃风弃光量/kW	951.200	897.500	1 128.020	2 976.720	1 237.175
总供电成本/元	6 465.36	5 519.90	5 472.08	17 457.34	15 717.79
单位电量平均供电成本/元	0.82	0.72	0.70	0.75	0.67

指标	园区			A, B, C总和	A, B, C联合
指标	A	B	C	A, B, C总和	A, B, C联合
最优储能功率/kW	194.415	209.319	260.556	664.290	448.103
最优容量/(kW·h)	1 129.278	620.099	1 269.610	3 018.987	1 469.140
总供电成本/元	6 206.62	5 066.22	5 137.38	16 410.22	15 423.97
取整功率/kW	200	200	250	650	450
取整容量/(kW·h)	1 100	600	1 300	3 000	1 500
取整后的总供电成本/元	6 215.66	5 069.54	5 159.58	16 444.78	15 439.59
总供电成本偏差/元	9.04	3.32	22.20	34.56	15.59
总负荷/kW	7 901	7 710	7 776	23 387	23 387
总发电量/kW	3 978.075	6 175.200	6 204.630	16 357.905	16 357.905
总购电量/kW	4 038.125	1 642.214	1 691.000	7 371.339	7 149.720
总弃风弃光量/kW	24.884	22.058	10.690	57.632	0.000
单位电量平均供电成本/元	0.787	0.658	0.664	0.703	0.660
弃风弃光率/%	62.55	35.72	17.23	35.23	0.00
新能源消纳率/%	37.45	64.28	82.77	64.77	100.00