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Risk-Constrained Bidding Strategy for Demand Response, Green Energy Resources, and Plug-In Electric Vehicle in a Flexible Smart Grid

机译:柔性智能电网中需求响应,绿色能源和插入式电动车辆的风险约束竞标策略

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摘要

The flexibility of smart grids has become an important issue due to the increasing penetration of uncertain energy resources, such as renewable as well as virtual power plants in the smart grids. Flexibility sources, such as demand response (DR) programs and plug-in electric vehicles (PEVs), can help the smart grid to be more productive. Although the renewable power plants are considered as flexible tools, they are somehow uncertain by themselves. In this article, the uncertainty of power generation of renewable resources has been resolved by incorporating the DR programs and PEVs. A stochastic decision making model for the coordinated operation of renewable resources and some virtual power generation is presented to solve a risk-constrained optimal bidding strategy for a smart grid. The participation of DR and PEV aggregators in the day-ahead market is considered. The uncertainty in day-ahead prices associated with renewable power generation is discussed throughout this article. As a well-known measure, the conditional value at risk is employed in the model to cope with all aforementioned uncertainties. Numerical studies and result analysis show that the expected profit of these resources is increased and the related risk is reduced significantly.
机译:由于不确定能源资源的渗透率普及,如智能电网中的可再生和虚拟发电厂,智能电网的灵活性已成为一个重要问题。灵活性来源,如需求响应(DR)程序和插入式电动车(PEVS),可以帮助智能电网更加富有成效。虽然可再生电厂被认为是柔性工具,但它们的不确定是不确定的。在本文中,通过纳入DR程序和PEV来解决可再生资源的发电资源的不确定性。提出了一种用于可再生资源和一些虚拟发电的协调运行的随机决策模型,以解决智能电网的风险受限最佳竞标策略。考虑了DR和PEV聚合器的参与。在本文中讨论了与可再生能源发电相关的前方价格的不确定性。作为一种众所周知的措施,在模型中使用风险的条件值以应对所有上述不确定性。数值研究和结果分析表明,这些资源的预期利润增加,相关风险明显减少。

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