电网技术2023，Vol.47Issue(12)：4916-4925,10.DOI:10.13335/j.1000-3673.pst.2023.1328
计及决策依赖不确定性的广义储能可信容量评估
Capacity Credit Evaluation of Generic Energy Storage Under Decision-dependent Uncertainty
摘要
Abstract
The increasing integration of renewable energy sources may lead to significant capacity deficiencies in power systems.Aggregated and coordinated generic energy storage resources is essential for addressing this issue via capacity markets.This paper discusses the credibility enhancement of capacity credit of generic energy storage.Firstly,a generic energy storage model is proposed to achieve the unified modeling of batteries,thermostatically controlled loads,and electric vehicles.Meanwhile,the decision-independent uncertainty modeling of operation status and baseline consumption,and the decision-dependent uncertainty modeling of available energy capacity are incorporated.Then,this paper presents a capacity credit evaluation method of generic energy storage under the decision-dependent uncertainty,where a sequential coordinated dispatch is raised to achieve the trade-off between the day-ahead self-energy management of generic energy storage and the real-time adjustment to system capacity deficiency.Additionally,the response unavailability assessment effectively enhances the credibility of evaluation.Finally,the effectiveness,superiority and credibility of the proposed method are verified in the case study,which provides an effective evaluation method for the future capacity market.关键词
广义储能/可信容量/决策依赖不确定性/容量市场/运行可靠性评估Key words
generic energy storage/capacity credit/decision-dependent uncertainty/capacity market/operational reliability assessment分类
动力与电气工程引用本文复制引用
齐宁,程林,刘锋..计及决策依赖不确定性的广义储能可信容量评估[J].电网技术,2023,47(12):4916-4925,10.基金项目
国家重点研发计划项目(2021YFB2400700) （2021YFB2400700）
国家自然科学基金重点项目(52037006) （52037006）
中国博士后科学基金特别资助项目(2023TQ0169).Project Supported by National Key Research & Development Program of China(2021YFB2400700) （2023TQ0169）
National Natural Science Foundation of China(52037006) （52037006）
China Postdoctoral Science Foundation Special Funded Project(2023TQ0169). （2023TQ0169）
