Research On Combined Heat And Power Economic Scheduling Based On Constrained Evolutionary Algorithm

Now,more and more thermal power plants are required power and heating supply.But the traditional thermal power scheduling algorithm does not contain the heating dispatch,it can not meet the requirements of the energy conservation and reducing cost.If power and heating supply are scheduled at the same time,it can effectively improve the integrated operation capability and production efficiency of thermal power plants.This paper is based on the power supply scheduling problem of a thermal power plant.Firstly,the mathematical model for heating dispatch is estimated under the condition of existing electric load economic dispatch in thermal power plant.Secondly,a scheduling mathematical model,which combined power and heating in the thermal power plants,is estimated by the model of power supply and the feature of heating supply model.Lastly,the brain storming optimization algorithm(BSO)and the multi-agent constraint evolution algorithm(MACEA)are used to solve the heating supply dispatch model and the cogeneration dispatch model,in order to complete the heating combined power supply scheduling in thermal power plants.This paper completes the following aspects:Firstly,this paper analyzes the power generation and heating modes of thermal power plants and studies the current mathematical model for power plant dispatching in thermal power plants.Then,this paper estimates the heating supply dispatch mathematical model under the condition of existing electric load economic dispatch.Lastly,the cogeneration economic dispatch model is also estimated by meeting the requirement of electric load of thermal power plants,even through the value of electric load is uncertain.Secondly,the improved brain storming optimization algorithm,which based on the variation cloud model,solve the heating supply dispatch mathematical model under the condition of the reference value of electrical load in thermal power plant.And compare with the mean dispatch algorithm,the PSO(particle swarm optimization)algorithm and the DE(differential evolution)algorithm.From the result of every algorithm,the cost of the improved BSO algorithm is less than other algorithms.It not only verifies the feasibility of applying the fuzzy uncertainty variation method in brain storming optimization algorithms,but also verifies the effectiveness of the improved algorithm.It provides a new solution to the thermal power plant heating scheduling problem.Thirdly,the paper analyzes the mathematical model of heat and power economic dispatch in thermal power plant,and considers the complexity of constraint condition and the single direction of evolution in BSO algorithm,a frame of multi-agent constraint evolution algorithm is built,and then the performance of the proposed algorithm test by the standard of CEC 2006 and CEC 2010.Compare with other algorithms,the proposed algorithm has better optimizing performance.Lastly,the multi-agent constraint evolution algorithm is applied to solve the heating scheduling problem given the electric load economic conditions of the thermal power plant.Compared with other algorithms,the simulation results show that the cost of the proposed algorithm is less than other algorithms.Then the multi-agent constraint evolution algorithm is applied to solve the problem of cogeneration economic dispatch in thermal power plant,and analysis the result of 7 unit small-scale scheduling problem and 48 unit large-scale scheduling problem,the result verity that the feasibility of solving the problem of cogeneration economic dispatch in thermal power plant,and the effectiveness of solving large-scale optimization problems.