Multi period MINLP framework for optimal operation of active distribution networks with integrated EVs and demand side management

Abstract This paper introduces a complete stochastic optimization framework for optimal operation of Active Distribution Networks (ADNs), taking into account the coordinated integration of Diesel Generator (DG), Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Electric Vehicles (EVs), and price-based Demand-Side Management (DSM). The suggested model includes AC power flow constraints, coordination of active and reactive power, and operating limits of all system components across a 24-hour scheduling period. The proposed approach is structured as a Mixed-Integer Nonlinear Programming (MINLP) model, applied to a modified IEEE 33-bus Radial Distribution Network (RDN), and optimized using GAMS with the DICOPT solver. The model goal is to reduce overall operational expenses. Network power losses and voltage profile are evaluated as technical performance indicators resulting from the optimal scheduling strategy. EVs are designed with bidirectional charging capabilities (V2G/G2V), RES, DG, and BESS that facilitate both active and reactive power. DSM allows for load shifting in residential and commercial sectors in response to dynamic pricing signals. The efficiency of the proposed methodology is verified through several case studies. The results indicate that coordinating DG, RES, BESS, EVs, and price-based DSM enhances the operational efficiency of the distribution network. When compared with the base case, the proposed approach lowers the operating cost from the reference value by 34.24%, while active and reactive energy losses decrease by 68.10% and 67.57%, respectively. Furthermore, all bus voltages remain within the specified operating limits throughout the scheduling horizon.