It can be predicted that the future power system, especially microgrids (MGs) will consist of many different sources and storages. In this regard, many researches have been conducted on different energy management strategies to improve power system adequacy indices. This paper presents a hybrid stochastic/robust coordinated power management strategy (CPMS) to simultaneously improve the flexibility, reliability and security indices of MG in the presence of electric vehicles (EVs), energy storage system (ESS), distributed generation (DG) and demand response programming (DRP). At first, this paper models the original problem which minimizes the difference between MG operation and reliability costs and its flexibility and security benefits considering MG optimal power flow constraints. This problem is non-linear programming (NLP) that is generally resulted in the local optimal, hence, this formulation is converted into linear programming (LP) using the first-order expansion of Taylor’s series for linearization of power flow equations and a polygon for linearization of circular inequalities. In addition, the active and reactive load demand, energy price, active power of renewable energy source (RES), different parameters of EVs, and availability/unavailability of MG equipment are considered as uncertain parameters. Hence, the hybrid stochastic/robust optimization with coupling the bounded uncertainty-based robust optimization (BURO) and scenario-based stochastic programming (SBSP) is used for the presented problem for modeling of uncertain parameters. Finally, the proposed method is applied to 32-bus MG using GAMS software. The numerical results show the capabilities of the proposed strategy to improve power system adequacy indices determining the optimal power scheduling for MG devices.

可以预见，未来的电力系统，特别是微电网（MGs）将由许多不同的来源和存储组成。在这方面，已经对不同的能量管理策略进行了许多研究以提高电力系统的充足性指标。本文提出了一种混合/随机/鲁棒协调电源管理策略（CPMS），以同时提高电动汽车（EV），储能系统（ESS），分布式发电（DG）和电动汽车存在时MG的灵活性，可靠性和安全性指标。需求响应编程（DRP）。首先，本文对原始问题进行了建模，考虑到MG最佳功率约束，该问题最小化了MG运行和可靠性成本之间的差异，以及其灵活性和安全性收益。这个问题是非线性规划（NLP），通常会导致局部最优，因此，使用泰勒级数的一阶展开式对潮流方程进行线性化，并用一个多边形将其转化为线性规划（LP）。圆不等式的线性化。此外，有功和无功负载需求，能源价格，可再生能源的有功功率（RES），电动汽车的不同参数以及MG设备的可用性/不可用性被视为不确定参数。因此，结合了基于有限不确定性的鲁棒优化（BURO）和基于情景的随机规划（SBSP）的混合随机/鲁棒优化用于不确定参数建模的问题。最后，利用GAMS软件将该方法应用于32总线MG。