Research On Local Energy Optimized Scheduling Module And System For Energy Internet

With the continuous growth of energy demand,the tight supply of traditional fossil energy and environmental pollution problems,the large-scale development and application of renewable energy has become an inevitable trend in the development of the global energy industry in the future.With the increasement of the penetration rate of renewable energy,the traditional energy scheduling and energy management system have gradually exposed various limitations and drawbacks,which can no longer meet the diversified power demand of users.The energy internet is an energy interconnection system that combines internet thinking and uses frontier information technology to realize the complementary integration of multiple complex energy networks such as electricity,heat and cold,and two-way information interaction,which can effectively improve the utilization efficiency of renewable energy.Moreover,the optimization of production and consumption of renewable energy is an important technical support for the energy internet.It is inevitable that energy scheduling and management need to consider multiple energy supplements and two-way information interaction.Therefore,it is of great theoretical and practical value to study the energy optimal scheduling model and system.Based on the characteristics of energy internet,this paper constructs an energy optimized scheduling model based on GA(Genetic Algorithm)+PSO(Particle Swarm Optimization).Firstly,the paper proposes an optimized scheduling strategy based on local energy internet by summarizing the problems of traditional scheduling strategy.According to the scheduling strategy,an optimized energy scheduling model based on GA+PSO is constructed.The model takes the proportion of electricity sales,intra-LAN and out-of-purity ratio as optimization variables,minimizes the user's electricity cost as the objective function,and establishes constraints of system scheduling,power balance and energy storage battery charging and discharging.Aiming at the shortcomings of PSO algorithm which is poorly convergent and easy to fall into local optimum,an optimized scheduling optimized algorithm based on GA+PSO is proposed.The method combines the Particle Swarm Optimization(PSO)with the Genetic Algorithm(GA)and adjusts the inertia factor by using the nonlinear inertia weight reduction function.Then the method is used to solve the model,and the program is written by MATLAB platform to complete the case simulation,which verifies the feasibility of the optimized scheduling model proposed in this paper.Compared with the simulation results of the traditional scheduling model,it is shown that the proposed optimized scheduling module not only reduces the user's electricity cost by about 46%,but also achieves the function of peaking and filling the valley and effectively extending the service life of the battery.At the same time,the proposed algorithm has stronger global search ability and faster convergence by comparing with the fitness value curve of PSO algorithm.Finally,this paper designs and implements a local energy information management system,and applies the proposed optimization scheduling model to the system.According to the characteristics of the openness and interconnection of the energy internet,demand of the energy management system is analyzed,and the scheme of the whole system that is composed of the system-level and user-level subsystems is proposed.The overall architecture and database of the system are designed,and the functional modules which mainly include energy demand forecasting,energy supply,energy online,and optimization scheduling are developed.The system function is realized by using relevant programming technology,and the performance test is carried out.The experimental results show that the system can basically meets the design requirements.