Optimization of Economic Load Dispatch for Distributed Energy System Using Metaheuristic Method

The increasing demand for electricity has led to the use of multiple energy generation sources. Among these sources, renewable energy is favored due to its environmentally friendly nature. The integration of distributed generation sources with the conventional grid makes the system highly dense and complex. Different heuristic methods are commonly used to determine the optimal or near-optimal operational strategy of a distributed system while maintaining system constraints. To address this issue, a meta-heuristic method based on genetic algorithms (GA) is proposed in this paper to solve the economic load dispatch problem for a distributed energy system that incorporates both thermal and photovoltaic (PV) systems. In addition, the prohibited-operating zones of the thermal generators are considered. The power output of the PV system for each subinterval is calculated by considering the irradiance and temperature values in that interval. The algorithm is tested for a daily load of 700 MW and the optimal share of each generator for different scheduling hours is determined. To validate the proposed algorithm, it is compared with benchmarking methods such as particle swarm optimization, quadratic programming, and lambda iteration. The results indicate that the proposed algorithm is effective in reducing operating costs and has a faster convergence rate than benchmark methods.