Hybrid stochastic/robust scheduling of the grid-connected microgrid based on the linear coordinated power management strategy

Abstract

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.