Research On Environmental Economic Optimization Dispatch Of CCHP And Parameter Space PSO Algorithms

In recent years, the combined cold heat and power (CCHP) due to its not only reduce the harmful gas emissions and alleviate the pressure of the environment, but also can realize the energy cascade utilization and improve the energy comprehensive utilization. As a result, under the energy crisis and environment worsening double pressures, CCHP will play an important role in economic sustainable development in our country.To implement the conversion between different pollution emissions, the carbon equivalent conversion coefficient has been proposed. Which has implemented between the SO2, NOx emissions and CO2 emissions, and the environmental cost model were further simplified. Then the CCHP environmental economic optimization scheduling model considered the environment cost and fuel cost was established. According to the shortage that particle swarm optimization (PSO) algorithm easily falls into local optimum and premature convergence, a parameter space particle swarm optimization (PS-PSO) algorithm is proposed. The moving direction and distance of particles are not only decided by its speed, but also the height by adding the parameter of height, which composed of a three dimensional parameter space including position, speed, and height. So the optimization plane made from each dimension variable speed, the position of two types of parameters become a new optimization of space including new height parameters,which means PSO can jump out of the precocious interval easily, resulting in reducing the randomness of results further because particles can search in a space with the parameter of height. The parameter space particle swarm optimization (PS-PSO) algorithm is applied to solve optimization scheduling problem of CCHP. To consider carbon emissions influence on combined cool and heat and power (CCHP), a carbon emissions trading cost function has been introduced. And a CCHP low carbon dispatching multi-objective optimization model has been established, which has considered carbon trading cost, fuel cost and environmental cost. In view of the particle swarm algorithm treated the inertia weight as global variables to update, which has lead to the premature problem. A fuzzy self-correction particle swarm optimization (FS-PSO) algorithm was proposed to solve this optimization problem. A membership function that response to particles’own fitness is established using the fuzzy reasoning mechanism. The value of inertia weight was modified by current membership function value of the particle fitness during optimization, which can improve particle precocity defect and enhance its global search ability.The example analysis results have shown that compared to the classical particle swarm algorithm and improved particle swarm algorithm, space particle swarm algorithm shows better global searching ability, improves the reliability of the optimization results, verifies the validity and superiority in solving nonlinear, non-convex and discrete optimization problems. The model can effectively control the CO2 emissions and get additional earnings, which also reduced integrated operation cost of CCHP. The CCHP system environmental economic optimization scheduling and considering the costs of carbon emissions trading have been researched, which not only has great significance to the energy conservation and emissions reduction of CCHP system to promote, but also for the popularization and application of the system to provide certain reference basis. The parameter space PSO (PS-PSO) algorithm and the fuzzy self-correction PSO (FS-PSO) algorithm were proposed for optimization problem solving opens a new route; which better able to meet the requirements of precision of optimization results.