Multi-objective Optimization Scheduling Of Integrated Energy System Considering Wind Power Consumptio

With the rapid development of modern society,energy consumption increases sharply,energy crisis,environmental pollution and other phenomena are becoming more and more serious.Future energy system must be combined with natural gas,solar energy,wind power and other forms of new integrated energy system,and the traditional energy in the form of points for different,integrated energy system not only higher energy efficiency,but also conducive to renewable energy given,so integrated energy system optimization scheduling problem become the focus on the topic.This paper expounds the research background and significance of integrated energy system,and conducts modeling and analysis on how integrated energy system can absorb wind power.The specific research work is as follows:Firstly,the comprehensive energy system model is introduced and analyzed.According to the operation mechanism and characteristics of all kinds of equipment involved in the system,the available mathematical model of the comprehensive energy system is obtained,which lays a foundation for the subsequent modeling and analysis of the system in all aspects of scheduling.Secondly,a low-carbon economic dispatching model of integrated energy system considering wind power consumption was established.The objective function was to minimize the total cost of system operation,the cost of environmental governance and the penalty cost of wind curtailment,and the power balance and the upper and lower limits of equipment output were considered comprehensively.By comparing with simulated annealing particle swarm optimization algorithm in convergence effect,the superiority of chaotic simulated annealing particle swarm optimization algorithm in searching global optimal solution is verified,so chaotic simulated annealing particle swarm optimization algorithm is used to solve this model.Simulation results show that the proposed model can improve the wind power consumption capacity,reduce the system environmental governance costs,and reduce the total cost of the system.Finally,an optimal dispatch model of integrated energy system considering multiple objectives is established.Taking the system operating cost,greenhouse gas emissions and the minimum variance of the net load of the system as the objective function,and comprehensively considering the constraints of the output limit of each equipment,the energy storage constraints of the battery,and the balance of the system’s cooling,heating and power,etc.The Pareto optimal theory and the chaotic simulated annealing particle swarm optimization algorithm are combined to construct a multi-objective chaotic simulated annealing particle swarm optimization algorithm and solve the model.By setting a variety of scenarios,the comprehensive energy system model is analyzed,and the scheduling of each scenario is compared.Simulation results show that,in view of the multiple objective function to solve the problem of multi-objective chaotic simulated annealing particle swarm algorithm can find the more satisfactory compromise solution,and this paper have put forward the integrated energy system model can reduce the economic costs,reduce greenhouse gas emissions,flat load fluctuations,improve the system stability and reliability.