Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties

Abstract

Abstract Distribution Feeder Reconfiguration (DFR) and Optimal Generation Scheduling (OGS) are indispensable operation tasks that are used in Microgrids (MGs) to enhance efficiency as well as technical and economic features of MGs. The OGS problem usually minimizes the cost of energy assuming a fixed configuration of power systems. On the other hand, the DFR problem is done assuming a predefined generation of units. However, performing these two tasks separately may lose the optimal solution. In this paper, a framework is proposed to jointly perform OGS and optimal DFR on a day-ahead time frame. The total operation cost of MG is minimized subject to diverse technical and economic constraints. The Demand Response (DR) program offered by curtailable loads is considered as a Demand Side Management (DSM) tool. The Electric Vehicles (EVs) and non-dispatchable Distributed Generations (DGs) are modeled along with the uncertainty of MG components. The charging pattern of EVs is modeled in two ways including uncontrolled and controlled patterns. The objective function includes the cost of purchasing active/reactive power from the upstream grid as well as DGs, cost of switching in DFR, cost of DR, and cost of energy losses. MG profit from selling electric energy to the upstream grid is also incorporated in the objective function. The proposed model is formulated as a Mixed-Integer Second-Order Cone Programming (MISOCP) problem and solved by the GAMS software package. The efficacy of the proposed model is confirmed by examining it on the IEEE 33-bus distribution network.