Modeling And Optimization Of Multiple Energy Systems For Energy Internet

With the intensifying of depletion of traditional fossil fuels,scarcity of resources and environmental pollution,it has great sense to improve energy use efficiency and realize large-scale use of distributed renewable energy sources to establish green,efficient and sustainable energy use mode.This thesis focuses on the modeling and collaborative optimization of multi-energy system based on the idea of energy saving,emission reduction and promotion of renewable energy in energy internet.The full text takes full account of distributed renewable energy,and comprehensively considers the three major energy systems of electricity,gas and heat to realize deep coupling among multiple energy sources and then analyze the comprehensive benefits.This thesis presents for the first time the exergy analysis method,improving the traditional method to get more practical significance.The specific research contents are as follows:1.Mathematical modeling of multi-energy system in energy internet is built.Based on the energy hub model,multi-energy system is regarded as a system composed of several energy hubs and energy transmission networks.The energy hub is regarded as the generalized load nodes and of each network.The energy hub and the three major energy transmission networks in the energy intemet-the power,natural gas and distributed heating network are modeled respectively,which provides an important foundation for optimizing multi-energy system.2.Collaborative optimization of multi-energy system is researched.The general optimization conditions of coupling multi-energy system are deduced by analogy with power system.Optimization problems of multi-energy system are divided into two types:multi-energy coordinated optimization planning and multi-energy coordinated optimization operation.Physical structure and operation mechanism of each energy transmission network are considered.At the planning level,a planning method considering equipment selection and capacity is put forward.At the operational level,gas emission effect is taken into account and collaborative optimization of hybrid electricity-gas-heat energy system and wind power consumption are studied.Slow time characteristic of heat network is preliminary studied.The proposed method is universal and extensible.3.With the increase proportion of renewable energy,its volatility and intermittency have an increasingly significant impact on the operation of power grids.Existing energy storage systems can hardly meet the demand for new energy sources.Power-to-gas technology is a new t energy storage technology that has been emerging in recent years which is closely integrated with distributed renewable energy.Its role in multi-energy systems remains to be studied.For this purpose,this thesis presents an optimal scheduling model for multi-energy system that integrates the gas-to-power technology,realizing the closed-loop operation and bi-directional flow of the gas-electricity network.The distributed reinforcement learning algorithm is applied into multi-energy system to solve the problem.The important role of P2G for multi-energy system is analyzed.4.For solving the shortcomings of the traditional methods in energy measurement and assessment,multi-energy systems are studied from the perspective of "exergy".Traditional exergy analysi’s method is improved.The proposed new method takes fully consideration in distributed renewable energy output,balancing energy structure in the energy supply side and energy utilization level in the energy consumption side,and provided a brand new perspective for the optimization of multi-energy system.