Real-Time Energy Management System for an Active Distribution Network With Multiple EV Charging Stations Considering Transformer's Aging and Reactive Power Dispatch

This study presents a real-time energy management approach for multiple electric vehicle charging stations (EVCSs), which are integrated with photovoltaic (PV) systems, wind turbines (WTs), and battery energy storages (BESs) in an active distribution network (ADN). The proposed optimization framework is developed as a mixed-integer second order cone programming problem that aims to minimize the operating cost while minimizing the accelerated aging of substation transformer and meeting the electric vehicle (EV) users driving needs. Additionally, it also fulfills power flow and voltage constraints of ADN ensuring global optimum which is achieved by scheduling both active and reactive powers of EVs and BESs, respectively. Furthermore, to address the real-time uncertainties associated with EVs, the developed algorithm is operated using dynamic prediction horizon based model predictive control technique. The proposed algorithm is applied on a modified IEEE-33 bus radial ADN, and its effectiveness is validated through a practical case study integrating both residential and workplace EVCSs. Additionally, the scalability of the proposed method is tested using a modified IEEE 118-bus distribution network. The results indicate substantial cost savings, significant reduction in the peak demand of the substation transformer along with its reduced loss-of-life, decreased energy loss while improving the voltage profile and better fulfillment of the EV driving requirements when compared to conventional EV charging methods.