BMFO-SIG: A Novel Binary Moth Flame Optimizer Algorithm with Sigmoidal Transformation for Combinatorial Unit Commitment and Numerical Optimization Problems

Abstract

To achieve paramount economy, hybrid renewable energy sources are getting importance, as renewable sources are cost less. Over past few years, wind energy incorporation drew more consideration in electrical market, as wind power acting affirmative role in energy saving as well as sinking emission pollutants. Moth-flame optimizer is recently proposed meta-heuristics search algorithm, which is encouraged by direction-finding nature of moth and their convergence towards light. Albeit, moths are having a strong ability to maintain a fixed angle with respect to the moon and possess an effective mechanism for travelling in a straight line for long distances. However, they are ensnared in a deadly/useless spiral path around imitation source of lights. In proposed research, to improve the performance of moth-flame optimizer around deadly corkscrew path around imitation source of lights, for discrete optimization problems, two binary variants has been implemented and tested for 23 benchmark functional including combinatorial unit commitment problem. This paper critically analyze per megawatt cost saving while incorporating wind energy sources along with thermal units. The searches for allocation of generators (units that participate in generation to take up load) and once units are decided allocation of generations (economic load dispatch) is done by mixed integer quadratic programming (MIQP). To verify the feasibility and efficacy of operation of proposed algorithms, small- and medium-scale power systems consisting of 5-, 6-, 10-, 20- and 40-generaing unit systems taken into consideration. Commitment and scheduling pattern has been evaluated with and without wind integration and it has been experimentally found that proposed algorithm gives superior type of solutions as compared to other recently reported meta-heuristics search algorithms.