Research On Robust Optimal Scheduling Of Integrated Energy System Considering Uncertainty

In order to respond to the national policy of the "14th Five-Year Plan",achieve China’s double carbon goal and promote the coordinated development of energy economy with environment,so it is urgent to build a clean,low-carbon,safe and efficient energy system.The traditional energy production system,which depends on non-renewable energy such as oil and coal,can no longer adapt to the current situation of energy and economic development in China.With a high proportion of renewable energy has been used in energy system,renewable energy is increasingly important and has been ushered large-scale development.The IES is a novel type of energy system which can reach the flexible access of renewable energy and benefit for the conversion and circulation of various forms of energy flow.However,the access of renewable energy with the deep coupling of energy in various forms will bring much uncertainty and burden in IES.Moreover,the complexity of multi-regional IES creates additional challenges for coordinating energy flows across multiple regions.Therefore,improve the structure of the integrated energy system is needed,it can raise the energy efficiency and further promote the early realization of the double carbon goal.Taking the power-gas-thermal IES as the research object,our study is focus on relieve the electrical uncertainty of integrated energy system and the distributed optimization of the multi-regional IES.The main work contents are as follows:First of all,the power-gas-thermal was constructed based on the analysis of electric power system,natural gas system and heating system.Considering the volatility and uncertainty of wind power generation in the system,a data-driven probability distribution set was used to describe the uncertainty of wind power output,and a two-stage robust optimization model was constructed for a comprehensive energy system.The CCG algorithm was used to solve the two-stage robust optimization problems.Furthermore,in order to improve the economy of the system,a comprehensive demand response model is adopted to deal with the load on the load side according to the load characteristics,which reduces the peak-valley difference of the load and enhances the economy of the system.Secondly,the optimal scheduling model of multi-region IES is constructed based on the optimization work of single region IES.Aiming at the uncertainty caused by largescale injection of electricity and complex energy flow in multi-regional integrated energy systems,a distributed robust opportunity constrained economic scheduling model is proposed to deal with the uncertainty of renewable energy in IES,and various flexible resources are developed in different energy systems to alleviate the uncertainty.Then,in order to realize decision independence and information privacy of multiregional IES optimization,a completely decentralized parallel distributed optimization scheme based on accelerated ADMM algorithm is proposed.This project improves the convergence rate of dispersion optimization and proposes fully decentralized parallel strategies for multi-region IES.