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Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

机译:考虑住宅中的最佳电池储能时间表的粒子群算法需求响应优化

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摘要

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.
机译:随着分布式资源的需求响应已经证明了其功率系统的显着潜力。它能够提供灵活性,在某些情况下,可以是抑制分布式发电的不可预测性的优点。参与小型或中等设施需求响应计划的能力有限;随着新的政策规定,这种限制可能会被夸大。制度是一种新的实体,被认为是电力的生产者和消费者,这可以为电网提供超额产量。此外,根据所考虑的变量的数量,具有不同一代资源,能量存储系统和需求灵活性的设施中的决策变得更加复杂。本文提出了一种在通用住宅中应用的需求响应优化方法。在该模型中,用户能够在其设施中执行需求响应的动作,而无需任何与需求响应服务提供商的合同。该模型考虑了具有所需设备来执行需求响应行动的设施。光伏发电,可用的存储容量和负载的灵活性用作找到最佳运行成本的最佳调度的资源。使用粒子群优化获得并与确定性分辨率进行比较,以证明模型的性能。结果表明,需求响应的使用可以降低运营日常成本。

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