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

• Published in: Journal of energy storage Vol. 21; pp. 58 - 71
• Main Authors: Sedighizadeh, Mostafa, Shaghaghi-shahr, Gholamreza, Esmaili, Masoud, Aghamohammadi, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2019
• Subjects: Demand response (DR); Distributed generation (DG); Distribution feeder reconfiguration (DFR); Electric vehicles (EV); Microgrid; Optimal generation scheduling (OGS); ANN; RES; DG; PV; BM; PHEV; DR; DFR; SCIP; Optimal generation scheduling (OGS); SH; Distribution feeder reconfiguration (DFR); MG; WT; Electric vehicles (EV); DSM; Demand response (DR); GAMS; Microgrid; SOC; MT; EMS; G2V; V2G; GT; MICP; EV; EES; OGS; FC; Distributed generation (DG)
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2018.11.009

•Proposing a convex optimization model for MGs with integrated DFR and OGS for day-ahead operation.•Modeling the uncertainty of WT and PV renewable generations.•Modeling EVs as EES in the suggested framework to reduce the total cost.•Considering the DR program and the participation of customers in the operation of MG.•Modeling the charging pattern of EVs in controlled and uncontrolled patterns. 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.