Hybrid data-driven operation method for demand response of community integrated energy systems utilizing virtual and physical energy storage

Yuntao Bu, Hao Yu, Haoran Ji, Guanyu Song, Jing Xu, Juan Li, Jinli Zhao and Peng Li

Applied Energy, 2024, vol. 366, issue C, No S0306261924006780

Abstract: Community integrated energy systems (CIES) have become flexible contributors to DR within distribution networks. Buildings’ thermal capacities can serve as virtual energy storage (VES) to augment CIES flexibility. However, uncertainties in environmental factors and building parameters hinder the accurate use of virtual energy storage without compromising user comfort. In addition, a single type of flexible resource is usually insufficient to meet the complex DR requirements of distribution system operators, underscoring the need for coordinating physical and virtual energy storage. This study proposes a hybrid data-driven operational approach to enhance the DR of a CIES. Real-time measurements were employed instead of exact system models and parameters to effectively dispatch VES during DR events. Additionally, rolling robust optimization was implemented to coordinate physical and virtual energy storage for DR, accommodating various uncertainties and multi-timescale characteristics within the CIES. Through its application to a case study, the proposed method demonstrated its efficacy in leveraging VES in a CIES for DR without compromising users’ thermal comfort. In a typical power reduction scenario, the DR revenue increased by 39.6%, while the overall operational cost decreased by 17.0%. In the power increase scenario, the DR revenue increased by 122.0%, and the overall operational cost decreased by 20.0%.

Keywords: Community integrated energy system; Virtual energy storage; Operation optimization; Demand response; Data-driven operation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2024.123295

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