Hybrid Optimization of Profit-Based Unit Commitment Allowing for Uncertainties of Renewable Energy Sources in Summer and Wintertime

 Environmental issues, due to the various gases that are harmful to fossil fuels, can cause disease and sickness worldwide. Renewable energy sources (RESs) are a crucial solution for decreasing reliance on fossil fuels. This is because they offer several advantages, such as significant cost reductions in operations, minimal depreciation over time, and the ability to provide electric power for various applications. As a result, they are highly desirable for use in the power sector. This kind of trouble becomes excessively challenging by developing the extent of the electric power market step by step. The authors developed a new optimization technique by combining chaotic maps with various nature-inspired optimization algorithms, such as the Harris hawks optimizer, sine cosine algorithm, and slime mold algorithm. This approach allowed them to improve the performance of these bioinspired optimization methods. The researchers evaluated an improved technique called hCHHO-SCA and hCSMA-SCA for solving the PBUCP considering renewable energy sources. They tested the techniques on both a 10-generating-unit system and a 100-generating-unit system. The authors were able to calculate the profit generated from each system as a result of applying the improved techniques. The adequacy of the analyzer is confirmed for a few benchmark issues that have been observed. The recommended optimizer is helpful in obtaining a solution to problems related to discrete and continuous optimization, including nonlinear types of optimization.

Keywords: Chaotic maps, Harris hawks Optimizer, Metaheuristics, Profit-based unit commitment Problem, Renewable energy sources, Slime mould algorithm, Sine cosine algorithm, Unit commitment problem.