Personalized demand response based on sub-CDL considering energy consumption characteristics of customers

Yunfei Shao, Shuai Fan, Yuhang Meng, Kunqi Jia and Guangyu He

Applied Energy, 2024, vol. 374, issue C, No S0306261924013473

Abstract: Demand response (DR) is a promising solution to unlock the flexibility of numerous customers with the rising proliferation of renewable energy (RE) in power system. However, the current demand response mechanisms face the challenge of low customers participation. The main reason is the significant diversity in customers' energy consumption characteristics, while existing mechanisms often provide a uniform DR signal without personalized guidance. To address these gaps, this paper proposes a novel personalized DR scheme based on sub-customer directrix load (SCDL) that considers the diversities of the customers' energy consumption characteristics. Firstly, the concept of SCDL is proposed, which decomposes the unified customer directrix load (CDL) into multiple SCDLs considering the energy consumption characteristics of massive customers. Subsequently, the formulation method of SCDL is described. The extraction of energy consumption characteristics is emphasized to ensure the reduction of regulation cost under the personalized guidance of SCDL. Furthermore, the collaborative interaction between the load aggregator layer and the customer layer is depicted by the bi-layer optimization model to achieve the optimal response effect. Finally, a multidimensional portrait method is designed to measure customers' performance in DR programs. Test systems comprising diverse DR customers are generated using the real-world data from Open Energy Information. The simulation results validate the effectiveness of the proposed personalized DR mechanism which considerably reduces the customers' regulation cost and significantly improves the DR effect.

Keywords: Demand response; Customer directrix load; Sub-customer directrix load; Energy consumption characteristics; Regulation cost; Multidimensional portrait method (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123964

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