Research On Scheduling Optimization Considering Uncertainty Of Demand Response

Demand response refers to the user's initiative to change their original electricity mode according to price signals or incentive policies issued by the power sector.With the rapid development of China's power system and the increasing demand for electricity,it is difficult to meet the current power supply and demand gap by relying solely on the development of the power supply side.With the help of information exchange and two-way communication technology,demand response can effectively alleviate the enormous pressure of power supply and demand and environmental protection.Demand response technology can make use of existing unit capacity,allocate demand-side resources according to reasonable schemes,alleviate the contradiction between power supply and demand,and save energy effectively.This method has been widely used in the world.Demand response combines technical methods and economic means to rationally call demand side resources to respond to the operation status of the system and improve the stability of the power system.At present,solving the influence of uncertainties in the operation of power system,reducing the dispatching cost and energy consumption of power system are the main problems for further research on demand response.Therefore,on the basis of considering the uncertainty of demand response,this paper studies the optimization method of minimum dispatching cost before demand response day,and explores the influence of uncertainties on dispatching cost.This paper introduces the research background of demand response technology and the research status of demand response technology in the world,and expounds the main factors causing the uncertainty of demand response and the current research status.In order to calculate the dispatching cost of demand response,the baseline load of demand response is first calculated,and the calculation method of baseline load and the existing defects are described.In order to reduce the influence of production arrangement and weather conditions on the baseline load on demand response event day,a correction factor is introduced to modify the baseline load.The actual load of demand response is predicted by BP neural network,and then the load reduction in demand response events is calculated.According to the different types of user response,the uncertainty of demand response is further studied and analyzed in this paper.Taking China's incentive demand response project as the research object,the deterministic and uncertain mathematical model based on user baseline load is established.In order to optimize the scheduling cost of demand response,the mathematical model and optimization steps of basic particle swarm optimization(PSO)are described.The main parameters of PSO are analyzed in detail,and the limitations of current PSO are explored.In order to improve the PSO,the basic theory and digital characteristics of cloud model are analyzed and studied.In order to reduce the influence of uncertainty in the process of demand response,the forward cloud generator is used to optimize the inertia weight of PSO so that it can automatically adapt to the fitness value and realize the transformation from qualitative concept to quantitative data.Finally,the objective function and constraints are determined and the mathematical model is established with the objective of minimizing the dispatching cost before demand response day as the optimization objective.By comparing the average fitness values of all the particles in the current particle swarm optimization,the improved particle swarm optimization algorithm is divided into three subgroups,and the inertia weight of the particle swarm is adjusted dynamically and adaptively to optimize the scheduling cost before the demand response day.According to the actual example,the deterministic and uncertain models of demand response are numerically calculated and compared.By comparing the pre-and pre-day scheduling costs before and after the improvement of particle swarm optimization,the advantages and disadvantages of the algorithm are analyzed,and the impact of uncertainty on scheduling costs is explored.According to the simulation results,the impact of the two algorithms on the user response initiative and the risk factors on the day-ahead scheduling cost are analyzed,and the effectiveness and feasibility of the proposed method in solving the uncertainty of demand response are pointed out.