A fuzzy-logic-based smart power management strategy for reliability enhancement of energy storage system in a hybrid AC-DC microgrid with EV charging station - Jagan A.; Ray P.K.; Behera B.P.; Panda G. (My University, 2025)
As Electric Vehicles (EVs) are considered as the future of the automobile industry, the demand for EV charging stations immensely rises. Though grid support can be expected for a microgrid (MG), it is highly advantageous to be self-reliant and be prepared for standalone operation for a long duration. This paper proposes a hybrid ac/dc microgrid (HMG) with an EV charging station in standalone mode with a smart power management strategy that increases the reliability of operation. Different modes of operation are designed according to power consumption levels and the operation of MG is switched between these modes considering the solar irradiance and battery bank state of charge. The decision-making system uses a combination of (a) fuzzy logic approach based on the state of charge of the energy storage system (ESS) and solar irradiance, adding to the intelligence of the system and providing smart reasoning capability and (b) logical decisions based on solar power and battery state of charge, considering all possible operating scenarios. With the proposed power management strategy in place, the standalone hybrid microgrid system is found to be able to prolong the discharge of the energy storage system under critical conditions of low battery state of charge and PV power availability and also under highly critical situations will be able to meet the critical load demand making use of the power available in the EV connected. Emphasis has also been given to maintaining the stability of the system while performing shifts in operating modes and during sudden changes in the scenario. Different operating conditions are recognized by the power management module in the model and an appropriate power management strategy is applied. Real-time simulation of the model was done in OPAL-RT real-time simulator using RT-LAB software and simulation results were reproduced. © 2023 Walter de Gruyter GmbH, Berlin/Boston.