山东电力技术2026，Vol.53Issue(6)：87-101,15.DOI:10.20097/j.cnki.issn1007-9904.250230
基于电动汽车虚拟储能模型的微电网日前优化调度
Day-ahead Optimal Scheduling of Microgrid Based on Virtual Energy Storage Model for Electric Vehicles
韩信 1丁贵立 2康兵 2钟炜力 1郭洋1
作者信息
- 1. 江西水利电力大学,江西 南昌 330099
- 2. 江西水利电力大学,江西 南昌 330099||江西省高压大功率电力电子与电网智能量测工程研究中心,江西 南昌 330099
- 折叠
摘要
Abstract
Benefiting from advancements in battery technology and policy incentives,the market penetration and ownership of electric vehicles(EV)have been steadily increasing.To fully leverage the dynamic response capability of EV clusters and optimize the supply/demand balance in microgrids with a high proportion of new energy sources,this paper proposes a day-ahead optimal scheduling strategy based on the virtual energy storage model of clustered EVs and the optimized echoes of the state network(ESN)prediction model utilizing the Black-winged Kite Algorithm(BKA)for microgrids strategy.First,a single EV virtual energy storage model is constructed.The charging and discharging power and energy boundaries of clustered EVs are aggregated by combining the Minkowski sum operation with the Monte Carlo random sampling method.This approach ensures the accuracy of the scheduling plan by retaining the correlation of individual constraints among EVs while downscaling the original data.Second,for the cluster EV power boundary prediction problem,a parameter prediction method based on BKA-ESN is proposed,which significantly reduces data dimensionality by taking the cluster EV power boundary as an input feature.It optimizes the core parameters of the reserve pool through the BKA algorithm,enhancing the accuracy and stability of the prediction model.Further,a day-ahead optimal scheduling model is established with the objectives of minimizing the integrated operation cost of the microgrid and the charging costs of cluster EVs.A linear weighting method is used to transform the multi-objective into a single-objective for solving.The simulation results show that the proposed strategy effectively reduces the demand for gas turbine operation and grid power purchases.It reduces the total microgrid operation cost by 22.0%and lowers the cluster EV charging costs by 8.9%.Additionally,the strategy significantly increase the renewable energy consumption rate while improving the stability of microgrid operation and power supply reliability.This is achieved by coordinating the charging and discharging of energy storage systems with gas turbine power to facilitate peak shaving and valley filling.The simulation results verify the effectiveness of the proposed model in terms of scheduling feasibility,economy and renewable energy consumption capacity,and provide theoretical support for the integration of cluster EVs in microgrid co-optimization.关键词
电动汽车/虚拟储能模型/黑翅鸢算法/回声状态网络/日前优化调度Key words
electric vehicle/virtual energy storage model/black-winged kite algorithm/echo state network/day-ahead optimal scheduling分类
信息技术与安全科学引用本文复制引用
韩信,丁贵立,康兵,钟炜力,郭洋..基于电动汽车虚拟储能模型的微电网日前优化调度[J].山东电力技术,2026,53(6):87-101,15.
