Identification and operational optimization of virtual energy storage characteristics for customer-side resources
DOI:
https://doi.org/10.4108/ew.11942Keywords:
Virtual energy storage, Optimal scheduling, Renewable energy integration, Distributed energy resourcesAbstract
INTRODUCTION: This paper addresses the challenges of integrating diverse customer-side resources into virtual energy storage (VES) systems to enhance power system flexibility and renewable energy utilization.
OBJECTIVES: The study aims to develop an integrated framework for the identification and optimal scheduling of VES systems, aggregating multi-type resources such as thermostatically controlled loads, electric vehicles, and distributed energy storage systems.
METHODS: Quantitative models were established to characterize the operational features of each resource. A data-driven clustering algorithm was employed to aggregate heterogeneous resources into unified VES units. A bi-level optimization model was formulated to minimize the aggregator's operational cost while incorporating comfort penalty functions to maintain user satisfaction.
RESULTS: Simulations across four scenarios (sunny, cloudy, high load, high EV penetration) demonstrated significant improvements: the peak-to-valley difference was reduced by 21.6%–28.4%, photovoltaic utilization exceeded 98%, and electricity purchase costs decreased by 4.8%–7.3%.
CONCLUSION: The proposed framework provides an effective and scalable approach for VES scheduling, significantly enhancing renewable energy integration and operational flexibility in modern distributed power systems.
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Copyright (c) 2026 Shiwang Yang, Jian Huang, Junshi Chen, Penghao Sun, Ziyi Zhan, Xinyi Zhao
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