Application of Sooty Tern Optimization Algorithm on Solar-Wind-Battery-Thermal Integrated Dynamic Economic Emission Dispatch Problems

The current challenges faced by conventional power plants, including increasing load demand over time, high generation costs, and excessive emissions from fossil fuels, have been helped to overcome by the integration of renewable energy sources (RESs) alongside conventional thermal power units. In this article, traditional dynamic economic emission dispatch (DEED) is enhanced by incorporating RESs, including two solar units, two wind units, and one battery power unit, forming the solar-wind-battery-thermal (SWBT) integrated DEED (SWBTDEED). This integration aimed at reducing generation costs and minimizing excessive fuel emissions, and combining cost and emissions over a 24 h period. Four different test systems, each incorporating 6, 10, 30, and 40 thermal units alongside the same RESs, are considered to meet varying load demands hourly throughout the day. This article demonstrates that, in addition to reducing generation costs, SWBTDEED is capable of reducing emissions by 38.28%, 28.48%, 20.67%, and 20.44% for four test systems, thereby protecting the environment. The sooty tern optimization algorithm (STOA) is proposed in this article for solving complex DEED and SWBTDEED problems, considering various constraints such as generation limits, ramp rate limits, valve point loading, and Weibull distribution. Finally, the robustness, optimization efficiency, and capability of the STOA technique in handling complex nonlinear constraints are demonstrated in the results section, showcasing its ability to achieve optimal results compared to other algorithms such as the sine-cosine algorithm, backtracking search algorithm, differential evolution, and particle swarm optimization.