In microgrids, distributed energy sources play a crucial role in meeting load demands. To ensure power stability within a grid-connected microgrid, this study presents a novel fuzzy logic-based management system for a residential setup. The system utilizes a photovoltaic (PV) system as a renewable energy source to power the residential load and charge electric vehicles (EVs). With a focus on minimizing grid dependency during peak hours and high-load demand, a carefully designed fuzzy logic controller comprising 48 rules has been implemented. The system takes into account the electricity tariff specific to Milan, Italy. The controller's rules and membership functions have been optimized to meet the desired objectives, and the implementation has been carried out using MATLAB's fuzzy logic toolbox (version 2020b). Extensive simulations using the MATLAB/Simulink environment (version 2020b) validate the effectiveness of the proposed system. Furthermore, a comprehensive cost analysis of the power exchange with the grid, considering the fuzzy-based energy management system, demonstrates a notable reduction in grid power consumption while satisfying the load demands.

Solar and Grid Power Integration for Dynamic Energy Management in Electric Vehicle Charging and Load Fulfilment with Fuzzy Logic

Zehra S. S.;Wood M. J.;Grimaccia F.;Leva S.;Mussetta M.
2023-01-01

Abstract

In microgrids, distributed energy sources play a crucial role in meeting load demands. To ensure power stability within a grid-connected microgrid, this study presents a novel fuzzy logic-based management system for a residential setup. The system utilizes a photovoltaic (PV) system as a renewable energy source to power the residential load and charge electric vehicles (EVs). With a focus on minimizing grid dependency during peak hours and high-load demand, a carefully designed fuzzy logic controller comprising 48 rules has been implemented. The system takes into account the electricity tariff specific to Milan, Italy. The controller's rules and membership functions have been optimized to meet the desired objectives, and the implementation has been carried out using MATLAB's fuzzy logic toolbox (version 2020b). Extensive simulations using the MATLAB/Simulink environment (version 2020b) validate the effectiveness of the proposed system. Furthermore, a comprehensive cost analysis of the power exchange with the grid, considering the fuzzy-based energy management system, demonstrates a notable reduction in grid power consumption while satisfying the load demands.
2023
2023 AEIT International Conference on Electrical and Electronic Technologies for Automotive, AEIT AUTOMOTIVE 2023
978-88-87237-57-3
978-88-87237-58-0
979-8-3503-4034-1
demand-response management
EV charging
fuzzy logic control
optimal power sharing
Renewable energy generation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1263278
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