Optimization Of Steam Power System Operation Based On Environmental Protection Tax

The Steam Power System(SPS)is an important part of petrochemical companies,providing the necessary steam and power for the production of the process industry while also consuming a lot of energy.China’s energy status quo is facing the dual constraints of environmental pressure and economic pressure,if not adjusted,the pressure of energy shortage and environmental pollution will increase sharply.In the context of the era of national carbon neutrality in 2030,the adjustment and optimization of the tax system for steam-powered enterprises is particularly critical.Therefore,the regulation and optimization of the steam power system is of great significance for the protection of the environment and the sustainable development of human beings.The main contents and research results of this paper are as follows:First of all,On the basis of the existing research on the optimization of the steam power system,the idea of multi-level integrated modeling of the steam power system is used to construct the physical structure,logistics structure and functional hierarchy structure model of the steam power system,and the optimization mathematical and physical models are established for the main equipment such as boilers and steam turbines of the steam power system.A nonlinear programming integration model for multi-cycle operation optimization of steam power system is established,and the objective function of the model includes the cost of environmental protection,the depreciation of equipment,and the operating cost related to energy consumption.Secondly,this paper proposes a PSO algorithm with an acceleration factor of c1 index decreasing.Aiming at the problems that the particle swarm algorithm is easy to fall into the local optimal point and the convergence is slow in operation,without increasing the complexity of the algorithm,an improved PSO algorithm with decreasing acceleration factor c1 index is proposed,which adjusts the speed update mode of particles to reduce the value index of acceleration factor c1 exponentially.Six classical functions are selected to test the performance of the new algorithm,analyze the results,and compare them with the linear decreasing PSO algorithm and the standard PSO algorithm of inertia weight and apply it to the scheduling problem of steam power system.Simulation experiments and engineering examples show that the new strategy has a significant effect on the foraging behavior of particles,and can better deal with the optimal scheduling problem of steam power system.The complexity of nonlinear boiler and steam turbine models in the solution process of multi-cycle operation problems of steam power systems cannot be effectively solved by simply using traditional solution methods.On the basis of discussing the basic principles,advantages and disadvantages of the particle swarm algorithm,a PSO algorithm suitable for the acceleration factor c1 index decreasing in the steam power system is proposed,which can quickly and accurately obtain the optimal solution.Finally,due to the increasing attention paid to the polluting gases emitted by petrochemical enterprises in the production process,a multi-cycle operation optimization model for the steam power system of petrochemical enterprises,including the cost of environmental protection tax,is proposed.In order to meet the steam power demand of the power plant,ensure its operation in an optimal state,and consider its impact on humans and the environment,the PSO algorithm using the accelerating factor c1 index decreasing index is used to study the steam power system scheduling problem of a petrochemical enterprise,and the results show that compared with the system optimization scheduling scheme that does not consider the environmental protection tax,the two not only have different operating schemes,but also use the acceleration factor c1 index decreasing PSO algorithm to reduce the total operating cost.The optimization of the steam power system has been proven to be feasible and effective.