Open Access

ARTICLE

Supply-Demand Balancing Capability Enhancement Technique for Source-Load-Storage Coordination of Smart Microgrids

Ping Wang¹, Yu Kang², Jianze Li², Cheng Chen³, Zhenbiao Qi^4,*, Guangpei An², Honghui Liu², Haiyang Liu²
1 Fengyang Power Supply Company, State Grid Anhui Electric Power Co., Ltd., Fengyang, China
2 Bengbu Power Supply Company, State Grid Anhui Electric Power Co., Ltd., Bengbu, China
3 Anhui Laite Industrial Group Co., Ltd., Bengbu, China
4 State Grid Anhui Electric Power Co., Ltd., Hefei, China
* Corresponding Author: Zhenbiao Qi. Email:
(This article belongs to the Special Issue: Active System Support, Resilience, and Electricity Markets of Large-Scale Renewable Energy Systems)

Energy Engineering https://doi.org/10.32604/ee.2026.085208

Received 07 May 2026; Accepted 16 July 2026; Published online 13 August 2026

Abstract

To improve the supply–demand balancing capability of smart microgrids under multi-source uncertainty, this paper proposes a coordinated source–load–storage optimization method. First, considering forecast errors in wind power, photovoltaic power, and load demand, a multi-scenario source–load uncertainty model is developed based on Latin hypercube sampling. Second, from the perspective of temporal supply–demand matching, a balancing capability evaluation system is established, including the instantaneous normalized balance degree, adjustable energy proportion, adjustable load proportion, and renewable energy utilization rate. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is applied to determine the indicator weights and quantify the balancing capability of the smart microgrid. On this basis, a two-stage source–load–storage optimization model is constructed. In the first stage, an event-driven user dynamic response model is introduced to exploit load-side regulation potential. In the second stage, a multi-objective energy storage coordination model considering balance degree constraints is established to further improve supply–demand matching. The IEEE 33-node system is used for case validation. The results show that the proposed method effectively improves the temporal matching among sources, loads, and storage, thereby enhancing the supply–demand balancing capability of smart microgrids.

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Keywords

Supply-demand balancing capability; source-load-storage coordination; event-driven demand response; two-stage optimization

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