Modeling And Optimization Of Local Energy Internet Based On SSA

The traditional energy system using the split supply model has restricted the improvement of energy efficiency.At the same time,with the increasing shortage of fossil energy,the disadvantages of the traditional energy structure have become increasingly prominent.People’s demand for the establishment of efficient and clean new energy systems has promoted the energy structure to be closely coupled to multiple energy sources and to increase the penetration rate of renewable energy sources,forming a multi-energy complementary energy Internet.In order to achieve the optimal allocation of resources in the Energy Internet and maximize the advantages of the Energy Internet,it is necessary to perform energy management on the Energy Internet.Based on the energy Internet system model,this thesis establishes different types of independent energy hub optimization models,and uses the Salp Swarm Intelligent Optimization Algorithm to solve this type of model,and proposes an energy hub-oriented energy management strategy.In this thesis,the traditional Intelligent Optimization Algorithm for Salp Swarm population is improved,and this thesis proposes novel algorithms for solving the optimization problem,including the neighborhood re-dispatch Salp Swarm algorithm and multiobjective linear weighted sum Salp Swarm algorithm.The new algorithms have the advantages of fast speed and high accuracy in solving energy management problems.The specific research content is as follows:(1)The topological structure of the energy internet is studied,and the devices in the system are modeled according to the coupling relationship between the energy sources.The energy hub model is introduced and the energy internet is considered as a system composed of several energy hubs and energy transmission networks.To this end,the three major energy transmission networks in the Internet of Energy,namely the power network,natural gas network and thermal network,are mathematically modeled to provide an important basis for the optimization of local energy Internet.(2)By considering the operation cost and environmental cost of the energy hub,an evaluation system of the energy hub operation is established,and an independent energy hub optimization model based on the scabbard population optimization algorithm is established on this basis.Through simulation,the research is carried out.The structure of two energy hubs with and without energy storage elements proves on the one hand the effectiveness and convergence of the Salp Swarm optimization algorithm,and on the other hand illustrates that adding energy storage elements can reduce system operating costs and environmental costs.Role.In addition,by changing the weights of the two objective functions,the coupling relationship between the objectives in the multi-objective energy management problem is analyzed.(3)In order to deeply explore the complementary and mutually-capable capabilities of energy in the Energy Internet,and to comprehensively consider the remaining energy interchange of the three energy hubs of residential,industrial,and commercial,this thesis proposes a local energy source with multiple energy hubs based on the Salp Swarm Algorithm.Aiming at minimizing the operation cost and pollution control cost of each energy hub,and maximizing the remaining energy utilization,considering the operating constraints of related equipment in the system,a multi-objective linear weighted sum Salp Swarm algorithm was proposed.Simulation experiments show that the multi-objective linear weighted sum Salp Swarm algorithm proposed in this thesis is obviously helpful for reducing the running costs and pollution control costs.