Research On Optimal Operation Of Integrated Energy System Based On Load Forecasting Considering Price Incentives

Since entering the 21st century,energy pressure has increased sharply around the world,and it is urgent to reshape the supply side of environmentally friendly and low-carbon energy.Build and improve energy utilization.Integrated energy systems integrate a variety of things traditional single energy supply,coordinates the operation of multiple energy flows,and effectively realizes the utilization of energy cascades,which has received widespread attention worldwide.In the process of deepening the reform of the energy market,the operating model will break the vertical integration structure,and the user’s role as the indirect manager of the energy market mechanism will become more and more critical in the transaction process.In this literature,the multi-energy forecasting technology under the influence of price incentives and the multi-objective optimization problems involved in the system are implemented in the existing infrastructure of the regional integrated energy system,and the specific work is as follows:(1)Based on the influence of the game interaction between operators and users on short-term multi-energy load forecasting in the process of energy supply and demand,a short-term load forecasting method considering the influence of price incentive mechanism is proposed.Based on the structure of Recurrent Neural Network(RNN),the working principle and characteristics of memory units in the structure of long-term short-term memory network are introduced,and a mathematical model of long-term short-term memory network for short-term load prediction is established on the basis of historical data of concurrent load and related influencing factors such as weather and energy price.Next,taking the comprehensive net economic benefits of energy operators and the comprehensive energy efficiency of users as the optimization direction,a master-slave game model guided by multiple types of energy prices and load demand is established,considering the price incentive effect in the process of supply and demand interaction,and obtaining stable energy prices through the game model,and then using them to modify the load forecasting model,iterating repeatedly,and finally obtaining stable load forecasting values and real-time energy prices,and finally verifying the model through examples.(2)This literature mathematically models the operation mechanism of each energy equipment,and on the basis of satisfying the various loads of users in the system,the Multi-objective Particle Swarm Optimization(MOPSO)algorithm is used to carry out systematic multi-objective optimization planning for the capacity of each energy supply and coupling equipment in the system.Optimized objective function for environmental friendliness;Next,the equipment operation state of the integrated energy system and many other constraints are analyzed and modeled,the optimization of the typical daily operation strategy in winter and summer is modeled as a linear optimization problem,and the simplex algorithm is used to classify and analyze the energy load curve for different typical days and solve the optimal operation scheme,and the optimal output planning of each energy equipment under each time granularity is calculated.Then,the best operation scheme under the typical day is used as the economic and environmental protection objective function in the planning scheme to realize the optimization and interoperability of the internal and external layers,and finally solve the configuration and operation planning problems of multi-objective optimization of the regional integrated energy system.