Research On Environmental Economic Dispatch Of Cogeneration Based On Multi-objective Backbone Particle Swarm Optimization Algorith

With the introduction of the concept of carbon peaking and carbon neutrality,energy conservation and emission reduction have become an important dispatching goal of the power system.How to realize energy saving and emission reduction and improve the comprehensive utilization efficiency of energy in the process of electric energy production is a hot topic of current research in various countries.Combined heat and power technology can simultaneously provide electricity and heat without increasing coal consumption,which has good economic and environmental benefits.In recent years,the proportion of cogeneration units in the entire power generation unit has been increasing,and the installed capacity is also increasing year by year.At the same time,the environment is also deteriorating.Therefore,it is of great practical significance to carry out research on cogeneration.At the same time,it is also conducive to meeting the national macro-strategic requirements for energy conservation and emission reduction and the construction of a green power system.The bare-bone particle swarm optimization algorithm has the advantages of no need to tune parameters and simple principle,which can effectively solve the problems caused by parameter settings.At the same time,there are the problems of insufficient diversity,slow convergence in the late iteration,easy to fall into local optimization and low solution accuracy.In the single-objective bare-bone particle algorithm,the optimal particle will be in a stagnant state and lose diversity.In this regard,this paper improves it to solve problems related to cogeneration.This paper proposes a solution method for cogeneration economic dispatch based on hybrid firefly and improved bare-bone particle swarm optimization.The optimal particle in the firefly algorithm will not lose its vitality and be in a stagnant state.Therefore,this paper uses a hybrid improvement strategy to make the optimal particle in the bare-bone particle swarm algorithm maintain its vitality and enhance the optimization ability of the algorithm.The improved algorithm adopts a nonlinear adaptive particle update strategy,and assigns different weights to the global optimal and individual optimal in different periods to meet the search requirements in different stages.effectiveness.In this paper,an adaptive multi-objective bare-bone particle swarm optimization algorithm is proposed to solve the economic emission dispatch of power system.The algorithm adopts a nonlinear decreasing strategy of search weight to improve the position update mode of the bare-bone particle swarm,and designs different position update strategies for the worst particle in different search stages.Compared with other algorithms,the results show that the algorithm has good feasibility and effectiveness.The economic emission dispatch problem of cogeneration needs to consider not only many constraints of cogeneration,but also the conflict between the two objectives,which increases the difficulty of solving.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(pbest)and the global best position(gbest),and to shrink the standard deviation for generating new particles;(ii)an improved strategy by comparing the sparsity of the pbest and the target particle instead of the domination is proposed to update the pbest;(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 gbest 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.