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A new point estimate method for stochastic optimal operation of smart distribution systems considering demand response programs

机译:考虑需求响应程序的智能配电系统随机最优运行的新点估计方法

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Smart Distribution Systems (SDS) with different technologies in Distributed Energy Resources (DER) and different load management facilities require proper operation strategies with considering the stochastic behavior of decision variables. Accordingly, this paper proposes a new Point Estimate Method (PEM) for optimal operation of SDS considering Demand Response Programs (DRP) under uncertainty in load and generation. In this paper optimal scheduling of DER units is investigated from economic and reliability viewpoints and also, the effect of DRP on operation of SDS is studied. The network is assumed to be supplied by renewable energy resources and Energy Storage Systems (ESS). The optimization aim is to minimize the operation and maintenance costs, power loss cost and cost of energy not supplied (ENS) simultaneously. The uncertainties of load demand and renewable power generation are considered using PEM approach as remarkable tool to handle the uncertainty. Ant colony optimization (ACO) is employed to minimize the objective function as a powerful optimization algorithm. The effectiveness and efficiency of the proposed approach are validated on PG&E 69-bus distribution system. The obtained results show that the proposed framework can be considered as an efficient tool in optimal operation of future distribution networks.
机译:考虑到决策变量的随机行为,在分布式能源(DER)中具有不同技术的智能配电系统(SDS)和不同的负载管理设施需要适当的操作策略。因此,本文提出了一种在负荷和发电不确定的情况下考虑需求响应程序(DRP)的SDS最佳运行的新点估计方法(PEM)。本文从经济性和可靠性的角度研究了DER装置的最优调度,并研究了DRP对SDS运行的影响。假定该网络由可再生能源和能源存储系统(ESS)提供。优化目标是最大程度地降低运行和维护成本,功率损耗成本和同时不提供能源(ENS)的成本。负载需求和可再生能源发电的不确定性被认为是使用PEM方法作为处理不确定性的出色工具。蚁群优化(ACO)被用作使目标函数最小化的一种强大的优化算法。该方法的有效性和效率在PG&E 69总线配电系统上得到了验证。获得的结果表明,所提出的框架可以被认为是未来配电网络最佳运行的有效工具。

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