A Comparison of Optimization Techniques for Energy Scheduling of Hybrid Power Generation System

Hybrid power generation system not only reduces our dependency on fossil fuel power generation stations but also helps us to meet the peak load demand. Renewable energy resources such as solar and wind in hybrid power generation system can play a vital role in reducing fuel cost and emission of conventional power generation system. In this paper, energy scheduling of hybrid power generation system is carried out by considering the fuel cost of thermal power generators and their emissions. Different optimization techniques, genetic algorithm, pattern search method, and interior point algorithm are used in this paper to optimally schedule the energy required from the thermal power generators to meet the load demand by taking into account the power from the renewable energy resources. These optimization techniques are compared in respect of fuel cost, emissions and computational time complexity. Interior point algorithm outperforms patter search method and genetic algorithm. It achieves more reduced fuel cost and emission as compared to pattern search method and genetic algorithm.