Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization. (1st April 2022)

• Record Type: Journal Article
• Authors: Xiong, Guojiang; Shuai, Maohang; Hu, Xiao
• Is Part Of: Energy. Volume 244(2022)Part B
• Publication Date: 2022-04-01
• Subjects: Combined heat and power system -- Economic environment dispatch -- Multi-objective optimization -- Particle swarm optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123108
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. To conquer the population diversity deficiency and premature convergence of bare-bone particle swarm optimization, IBBMOPSO integrates four improved strategies, that is, (i) a non-linear adaptive particle updating strategy is presented to automatically tune the weights of the personal best position ( p best) and the global best position ( g best), and to shrink the standard deviation for generating new particles; (ii) an improved strategy by comparing the sparsity of the p best and the target particle instead of the domination is proposed to update the p best; (iii) an improved strategy by selecting a random Pareto optimal solution from a newly filtered subset of the external archive is designed to determine the g best for each target particle; and (iv) a modified strategy by combining the slope and the crowding distance is presented to determine the Pareto optimal frontier. IBBMOPSO is firstly validated by nine multi-objective benchmark test functions. Then, it is then applied to three test systems and the simulation results demonstrate that IBBMOPSO can achieve higher-quality dispatching schemes with lower generating fuel cost and less pollutant gas emission compared with other algorithms. Highlights: An improved bare-bone multi-objective particle swarm optimization algorithm is proposed. A nonlinear adaptive particle updating strategy based on exponential function is proposed. Improved strategies to update the p best and g best are proposed. The slope method and crowding distance method are combined to determine the POF. Benchmark test functions and three CHPEED problems are used to verify the performance.