Multiobjective multi-verse optimization algorithm to solve combined economic, heat and power emission dispatch problems

Abstract

This study implements a potent Multiobjective Multi-Verse Optimization algorithm to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems. Solving these problems operates the power system integrated with cogeneration plants economically and reduces the environmental impacts caused by the pollutants of fossil fuel-fired power plants. A chaotic opposition based strategy is proposed to explore the search space extensively and to generate the initial populations for the multiobjective optimization algorithm. An effective constraint handling mechanism is also proposed to enable the population to remain within the bounds and in the feasible operating region of the cogeneration plants. The algorithm is applied to standard test functions, four test systems including a large 140 bus system considering valve-point effects, ramp limits, transmission power losses, and the feasible operating region of cogeneration units. The Pareto Optimal solutions obtained by the algorithm are well spread and diverse when compared with other optimization algorithms. The statistical analysis and various performance metrics used indicate the algorithm converges to true POF and is a viable alternative to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems.