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A Novel Distributed Large-Scale Demand Response Scheme in High Proportion Renewable Energy Sources Integration Power Systems

机译:高比例可再生能源集成电力系统的一种新型分布式大规模需求响应方案

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摘要

Large-scale demand response (DR) is a useful regulatory method used in high proportion renewable energy sources (RES) integration power systems. Current incentive-based DR schemes are unsuitable for large-scale DR due to their centralized formulation. This paper proposes a distributed scheme to support large-scale implementation of DR. To measure DR performance, this paper proposes the customer directrix load (CDL), which is a desired load profile, to replace the customer baseline load (CBL). The uniqueness of CDL makes it more suitable for distributed schemes, while numerous CBLs have to be calculated in a centralized manner to ensure fairness. To allocate DR tasks and rebates, this paper proposes a distributed approach, where the load serving entity (LSE) only needs to publish a total rebate and corresponding CDL. As for each customer, s/he needs to claim an ideal rebate ratio that ranges from 0 to 1, which indicates the proportion of rebate s/he wants to get from LSE. The rebate value for each customer also determines his or her DR task. Then, the process of customer claims for the ideal rebate ratio is modeled as a non-cooperative game, and the Nash equilibrium is proved to exist. The Gossip algorithm is improved in this paper to be suitable for socially connected networks, and the entire game process is distributed. Finally, a large-scale DR system is formulated. The simulation results show that the proposed DR can promote the consumption of RES. Additionally, this scheme is suitable for large-scale customer systems, and the distributed game process is effective.
机译:大规模需求响应(DR)是一种用于高比例可再生能源(RES)集成电力系统的有用监管方法。由于其集中式制定,目前基于激励的DR方案不适合大型DR。本文提出了一种分布式方案,以支持DR的大规模实施。为了测量DR性能,本文提出了客户Directrix Load(CDL),即期望的负载轮廓,以替换客户基线负载(CBL)。 CDL的唯一性使其更适合分布式方案,而许多CBL必须以集中方式计算以确保公平性。要分配DR任务和折扣,本文提出了一种分布式方法,其中负载服务实体(LSE)仅需要发布总回扣和相应的CDL。至于每个客户,S /他需要索取理想的折扣比,范围为0到1,这表明折扣S /他想要从LSE获得的比例。每个客户的折扣价值也决定了他或她的DR任务。然后,客户要求理想回扣比的过程被建模为非合作游戏,并证明了纳什均衡存在。本文提高了八卦算法,适用于社会连接的网络,并且整个游戏过程分布。最后,制定了大规模的DR系统。仿真结果表明,拟议的DR可以促进RES的消耗。此外,该方案适用于大型客户系统,分布式游戏过程是有效的。

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