Comparative Study of Optimistic and Fuzzy Logic-Based Energy Management Strategies for a Grid-Connected Hybrid PV–Wind System

The increasing integration of renewable energy sources into industrial electrical systems requires effective energy management strategies to improve renewable energy utilization, reduce grid dependence, and control energy costs. This study presents a comparative analysis of two energy management strategies for a grid-connected hybrid photovoltaic (PV)–wind energy system supplying the electrical demand of an industrial plant. The hybrid system is modeled in MATLAB/Simulink, including the PV and wind subsystems and their associated maximum power extraction controllers. Two management algorithms are investigated: an optimistic strategy based on renewable power production, load priorities, and variable electricity tariffs, and an intelligent strategy based on fuzzy logic for adaptive energy flow management. Both strategies determine the power exchanges among the renewable sources, industrial load, and utility grid under varying operating conditions. Their performances are evaluated and compared in terms of renewable energy utilization, load supply, grid energy exchange, and operating cost. The simulation results demonstrate the effectiveness of both strategies in coordinating energy flows within the hybrid system, while revealing differences in their ability to utilize renewable generation and manage grid exchanges. The comparative analysis provides useful insights into the application of optimistic and fuzzy logic-based strategies for energy management in grid-connected hybrid PV–wind systems supplying industrial loads.