Research On Intelligent Power Demand Response Resource Optimization Method Considering User Demand Constraints

With the development of social economy and the improvement of people's living standard,the load difference between peak and valley increases,while the maximum load utilization hours decrease year by year.The increasing load leads to the increasing peak-valley difference of load,which may lead to unbalanced supply and demand of power system.In order to improve the economy,security and stability of social electricity consumption,the concept of demand response(DR)emerges as the times require.Then the DR can realize power load transfer or power load reduction in a certain period of time and ensure the stability of power grid system.Based on the analysis of consumers' consumption habits,willingness of using energy and price sensitivity,this paper classifies diversified demand response resources according to the load nature and adjustable ability of typical electric-thermal equipment,and forms a demand response combination adjustment model taking into account the multi-time scale nature of wind power,photovoltaic,energy storage,flexible load and other resources of electric-thermal system.The main contents of this paper are as follows:(1)Firstly,considering the difference in demand response process and the application of load differentiation,starting from the demand response resource model,the load model is established by selecting typical demand response loads such as interruptible industrial load,air conditioning in commercial buildings,centralized water heater in commercial buildings and residential load,and the important control parameters of different demand response resources are studied.(2)Establish the user satisfaction model.Based on the different load characteristics on the user side,build the capacity model that users can participate in regulation and control under different response levels.Propose the user automatic screening strategy of demand response considering the user's willingness to use energy and load characteristics.Construct the integer programming model of user automatic screening in demand response,and use branch and bound method to find the optimal solution.The validity and feasibility of the model are verified by an example.(3)A demand response scheduling strategy for intelligent community based on multi-population Co-evolutionary Genetic algorithm is proposed.The optimalscheduling model including photovoltaic,energy storage and electric vehicles is constructed with the objective function of minimizing the total operating cost of the system and the exchange power of the grid.The optimal results are obtained by using multi-population co-purification genetic algorithm.Finally,the validity and feasibility of the model are verified by an example analysis.The results show that the strategy is fair and effective.