Energy Optimization Scheduling Strategy Of Industrial Park For Price-type Power Demand Response

With the sustained and rapid development of economy,the demand of human society for energy is increasing,which leads to a serious energy crisis.As an effective way to optimize energy allocation,smart grid demand response energy management has been widely concerned by the academic community.Industry is the pillar of the national economic development,and surveys show that industrial energy consumption accounts for 70% of total energy consumption.The main body of smart grid demand response energy management returns to industrial enterprises,which can optimize industrial energy consumption strategy,improve energy efficiency,and ensure the coordinated development of industry and electricity.Therefore,it is of great significance to study industrial demand response energy management.This paper studies the demand response strategy of industrial park based on power price in smart grid.There are two major energy-consuming system in the industrial park: production system and heating,ventilation,and air conditioning(HVAC)system.The main research content is divided into the following three points:Firstly,the application of industrial demand side response in industrial Heating,ventilation,and air conditioning(HVAC)system is studied to solve the model-free non-convex function problem in the process of energy optimization management.Under the constraint of energy supply and demand balance between the factory and the utility company,a total cost optimization model is established to optimize the energy consumption discomfort cost of the factory and the energy cost of utility company.Fanger thermal comfort model is used to describe human discomfort in industrial HVAC system.Due to its mathematical relation is expressed as an unknown function,the optimization iterative algorithm based on Powell direction acceleration method is designed to determine the optimal solution of the non-convex optimization problem,i.e.the optimal electricity price,the optimal temperature settings of the industrial HVAC system and the optimal energy supply of the utility company under the proposed industrial energy management strategy.It is verified form the simulation results that the algorithm is convergent.And the impact of the tradeoff factor on the total cost is analyzed.Secondly,an electricity price control algorithm in the electricity market is studied.According to the load regulation capacity of industrial HVAC users at the lowest electricity cost,the electricity purchase cost model of utility company is established.Based on the market clearing price and electricity load forecast error formulated by the independent electricity system operator(IESO),it provides utility companies with electricity price that purchase adjustment services from industrial users to reduce the cost of utility company load forecast errors.Considering the measurement error and signal interference in the communication process,an iterative optimization algorithm of pricing control is designed.It is verified from the simulation results that the electricity price and market clearing price of utility companies is convergent.And the influence of disturbance on pricing and different prices on the cost of utility companies is analyzed.Finally,in view of the energy management of industrial production systems,a distributed energy scheduling algorithm is designed to improve the speed and accuracy of energy management decisions.An energy management system composed of industrial users,utility companies and data control centers is considered.With the goal of minimizing the total cost of energy supply and demand,an optimization model is established that takes into account the energy consumption cost of factory workstations and energy supply cost of utility company.The traditional gradient method is improved based on feedback theory,and a distributed iterative algorithm is designed based on the improved gradient method.The optimal work efficiency,power supply and retail price of utility company are solved by simulation.Under the influence of measurement noise,the convergence speed and error of the distributed algorithm based on modified gradient method are compared with that of the traditional distributed algorithm without considering feedback.The result shows that the distributed energy management algorithm based on modified gradient method has better convergence performance.