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A Risk-Based Gaming Framework for VPP Bidding Strategy in a Joint Energy and Regulation Market

机译:基于风险的游戏框架,用于联合能源和监管市场中的VPP竞标策略

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摘要

This paper presents a risk-based game theoretic model for virtual power plant (VPP) bidding strategy in both energy and balancing markets, in the presence of conventional generation companies (GenCos) as rivals. The objective is to provide a method for finding strategic bidding of VPP comprising traditional units, wind turbine, interruptible and shiftable loads along with other strategic rivals. In this regard, a novel shifting load scheme is introduced into the VPP portfolio in which VPP is penalized based on shifting load amount and shifting load time as well. A bi-level mathematical program with equilibrium constraint (MPEC) is represented for modeling behavior of each producer in which the upper level deals with profit maximization of each strategic unit and the lower level encompasses social welfare maximization considering transmission constraints. Power transfer distribution factors are employed to model transmission constraints. The proposed bi-level problem is converted to a traceable mixed-integer linear programming problem using duality theory and Karush-Kuhn-Tucker optimization conditions. Simultaneous solution of all MPECs forms an equilibrium problem with equilibrium constraint that results in market Nash equilibrium point. Finally, information gap decision theory is employed for modeling load price uncertainty and evaluating risk of VPP decision making. The proposed model is tested on a standard IEEE-24 bus system, and the accuracy of the results is indicated.
机译:本文提出了一种基于风险的游戏理论模型,用于在常规一代公司(Gencos)的存在中,在能量和平衡市场中的虚拟电厂(VPP)竞标策略,作为竞争对手。目的是提供一种寻找包括传统单位,风力涡轮机,可中断和可移动负载的VPP的战略招标的方法以及其他战略竞争对手。在这方面,引入了一种新的转换载荷方案,其中VPP组合中,VPP基于换档量和换档时间而受到惩罚。具有均衡约束(MPEC)的双级数学程序,用于每个生产者的建模行为,其中每个战略单元的利润最大化的上层统计化和较低级别包括考虑传输约束的社会福利最大化。电力传输分布因子用于模拟传输约束。使用二元理论和Karush-Kuhn-tucker优化条件,所提出的双级问题转换为可追踪的混合整数线性编程问题。所有MPEC的同时解决方案形成了均衡限制的平衡问题,从而导致市场纳什均衡点。最后,信息差距决策理论用于建模负荷不确定性和评估VPP决策风险。在标准IEEE-24总线系统上测试了所提出的模型,并指示结果的准确性。

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