网格
调度(生产过程)
电网连接
可再生能源
计算机科学
电力系统
分布式计算
解耦(概率)
智能电网
可控性
储能
负荷管理
稳健性(进化)
控制工程
系统集成
能源管理
电动汽车
需求响应
负载平衡(电力)
汽车工程
控制系统
降低成本
弹性(材料科学)
能源管理系统
鲁棒控制
电力
营业成本
经济调度
频率调节
波动性(金融)
工程类
作者
Zhifeng Liu,Zeqi Li,Xiaolong Jin,Hongjie Jia
标识
DOI:10.1109/tsg.2025.3617542
摘要
Amid global decarbonization efforts, Integrated Energy Systems (IES) face critical challenges from the inherent uncertainties of high-penetration renewable energy sources and the large-scale, potentially disorderly integration of Electric Vehicles (EVs), leading to significant supply-demand imbalances and system volatility. Existing approaches struggle to effectively coordinate diverse device response times and capture complex EV user behavior. To address these, this paper proposes a multi-time domain control model-driven bi-level optimization framework for Integrated Energy Systems. The upper level employs a multi-objective intelligent algorithm to generate Pareto-optimal solutions balancing economic costs and environmental impacts. The lower level implements a singular perturbation theory-based multi-time domain control model, dynamically partitioning devices into fast/slow subsystems for differentiated scheduling, significantly enhancing resilience against uncertainties. Additionally, a prospect theory-driven EV dynamic charging response model accurately incorporates user psychology (e.g., loss aversion) to guide orderly charging via price incentives. Furthermore, a Fourier-based power decoupling strategy for hybrid storage (LIB-SC) reduces battery degradation by 28.7%. Case studies demonstrate superior performance: a 12.2% reduction in economic costs, an 11.5% reduction in emissions, and robustly maintained system volatility below 5% under extreme uncertainties, showcasing significant advancements in balancing economic-environmental objectives while accommodating high renewable and EV penetration.
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