Optimized wind-solar-storage configuration of industrial park microgrids based on improved differential evolution algorithm

doi:10.3969/j.issn.2097-0706.2025.09.008

Abstract

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

CLC Number:

TK018：TM73

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