Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

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• 作者：Linna NI ; Fushuan WEN ; Weijia LIU…
• 关键词：Distribution system ; Congestion management ; Demand response ; Load aggregators ; Uncertainty ; Robust optimization ; Bi ; level linear programming
• 刊名：Journal of Modern Power Systems and Clean Energy
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：5
• 期：1
• 页码：66-78
• 全文大小：1924KB
• 刊物主题：Energy Systems; Renewable and Green Energy; Power Electronics, Electrical Machines and Networks;
• 出版者：Springer Berlin Heidelberg
• ISSN：2196-5420
• 卷排序：5

文摘

In recent years, much attention has been devoted to the development and applications of smart grid technologies, with special emphasis on flexible resources such as distributed generations (DGs), energy storages, active loads, and electric vehicles (EVs). Demand response (DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations. Despite their potential contributions to power system secure and economic operation, uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned. In addition, the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments. In this context, this paper aims to propose a DR based congestion management strategy for smart distribution systems. The general framework and procedures for distribution congestion management is first presented. A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established. Subsequently, the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices. The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.