Mathematical Modeling and Optimization in Sustainable Electrical Engineering: Recent Trends and Future Directions

The movement towards sustainability in electrical engineering requires precise mathematical models and optimization methods for handling the variability of the renewable resources, scheduling storage systems, and assuring economic feasibility. This work provides an organized review of recent developments in optimization research related to hybrid system sizing, unit commitment with renewable generation, demand-side management, and EVs charging schedule optimization. A combined approach that uses mixed-integer nonlinear programming for the unit commitment and particle swarm optimization for optimal power flow problem solving serves as an example of one such framework. Analysis of recent literature shows that hybrid optimization methods surpass simple methods in constraint handling and multi-objective optimization quality, and reinforcement learning has proven useful for scheduling and curtailing reduction. The table below compares the performance of different algorithms in recent research. Finally, the future direction of development in this field includes hybridization, data-based, and multi-scale optimization.