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电动汽车驱动 GaN器件 ★ 5.0

一种应对极端事件的多区域电力市场风险分担双层框架

A Risk-Sharing Bi-Level Framework for Multi-Area Electricity Markets Against Extreme Events

作者 Jianing Lin · Minglei Bao · Yanqiu Hou · Yi Ding · Zhenglin Yang
期刊 IEEE Transactions on Power Systems
出版日期 2024年8月
技术分类 电动汽车驱动
技术标签 GaN器件
相关度评分 ★★★★★ 5.0 / 5.0
关键词 电价风险管理 多区域电力市场 风险分担双层框架 场景缩减技术 分布式市场清算
语言:

中文摘要

针对极端天气事件频发带来的电力价格风险管理问题,本文提出一种创新的多区域电力市场风险分担双层框架。该框架通过跨区互济电力的时间优化配置,在风险相关价格信号引导下实现高风险区域的功率平衡缓解。上层进行基于各区域报价的最优互济电力市场出清,下层则基于预期节点边际电价等价格风险指标制定风险感知竞价策略。结合负载中断值与Ford-Fulkerson方法提出新型场景约简技术,并在解析目标级联框架下分布式求解双层模型以保护区域数据隐私。算例表明,该机制可有效降低多区域市场的价格风险。

English Abstract

With the increasing frequency of extreme weather events, electricity price risk management has attracted wide attention. Considering the spatio-temporal difference of price risks, the electricity resources in different areas can be coordinatively allocated for risk management. To realize this target, the effective market mechanism is significant but seldom studied. Considering that, this paper innovatively proposes a risk-sharing bi-level framework for multi-area electricity markets (MAEMs). With the guidance of risk-related price signals, the idea of risk-sharing can be realized through the temporal allocation of interchange power to relieve the power imbalance in high-risk areas. The proposed market framework is organized at two levels, where the upper level is the market clearing of optimal interchange power based on the bids/offers submitted by different areas. The lower level conducts the risk-aware bidding strategy of each area based on price risk indices, i.e., expected locational marginal prices. Besides the novel market mechanism, several techniques are developed to provide effective support for the formulated model. Specifically, to improve the efficiency of bidding strategy formulation in the lower-level market, a new scenario reduction technique is developed by combining the Ford-Fulkerson method and the new scenario measurement index, i.e., load interruption values. Besides, the bi-level market model is cleared in a distributed manner within the analytical target cascading framework for preserving data privacy among different areas. Case studies demonstrate that our proposed market framework can effectively mitigate the price risks of MAEMs by sending more power to high-risk areas as a priority.
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SunView 深度解读

该多区域电力市场风险分担框架对阳光电源储能系统和能源管理平台具有重要应用价值。其双层优化机制可直接应用于PowerTitan大型储能系统的跨区域调度策略,通过风险感知竞价实现储能资源在多市场间的优化配置。基于预期节点边际电价的风险指标可集成到iSolarCloud平台,为ST系列储能变流器提供极端天气下的智能调度决策支持,降低电价波动风险。分布式求解框架保护数据隐私的特性,适合阳光电源构建多业主储能集群的协同优化方案。该研究为储能参与电力市场的风险管理提供理论支撑,可提升阳光电源储能产品在极端事件下的经济性和可靠性。