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Optimal Strategies for Scheduling the Hourly Demand Response Considering Uncertainties of Renewable Energy in Day-ahead Market

机译:考虑日前市场中可再生能源不确定性的小时需求响应的优化策略

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Traditionally, the distribution system operator (DSO) is responsible for the reliable operation of power distribution systems. However, the advent of micro-grids in electric power distribution systems promotes a new role of DSO where it is responsible for aggregating the widely dispersed distributed energy resources (DERs), small thermal generation units, and flexible loads into the electricity markets. This paper presents an optimal demand response (DR) bidding framework for aggregators in the distribution network, that help integrate the uncertainty of the power output of the wind turbine. In the proposed framework, the load aggregators collect and submit DR offers to the DSO to make their contribution to the market operation. The load reduction offers include load curtailment, load shifting, and the generation from DERs. The DSO solves market clearing problem using the proposed DR model for the day-ahead market using a mixed-integer linear programming (MILP) model. The proposed approach for DR participation and market clearing is implemented using a 6-bus test system, and the merits of the proposed DR model are demonstrated using two cases for hourly unit commitment and ten scenarios for wind variability.
机译:传统上,配电系统运营商(DSO)负责配电系统的可靠运行。但是,配电系统中微电网的出现促进了DSO的新作用,DSO负责将广泛分散的分布式能源(DER),小型火力发电机组和灵活的负载聚集到电力市场中。本文为配电网中的聚合器提供了一个最佳需求响应(DR)投标框架,该框架有助于整合风力涡轮机功率输出的不确定性。在提议的框架中,负载聚合器收集灾难恢复报价并将其提交给DSO,以为市场运营做出贡献。减少负荷的措施包括减少负荷,转移负荷以及由DER产生负荷。 DSO使用混合整数线性规划(MILP)模型针对日间市场使用拟议的DR模型解决了市场清算问题。所提出的灾难恢复参与和市场清算方法是使用6总线测试系统实现的,并使用两种情况下的每小时单位承诺量和十种风变率场景来证明所提出的灾难恢复模型的优点。

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