Optimal Coordinated Scheduling Of Integrated Energy System Considering Heat Network Flow Regulation And Uncertainty

Under the threat of energy crisis and environmental pollution,vigorously developing renewable energy sources(RES)such as wind power and photovoltaic is a major way to achieve energy transition and carbon peak and neutrality goals.However,with the increase of installed renewable capacity,the consumption capability of renewable energy has become a key factor to restrict the development of renewable energy.The integrated electricity and heating system(IEHS)can satisfy the diversified energy demand and improve energy efficiency through electro-thermal synergy and complementarity,which is conducive to the improvement of renewable energy consumption level.Therefore,the coordinated optimal scheduling strategies of IEHS are studied in this thesis.The main work is as follows:To realize the fullest dispatching potential of IEHS,an optimal dispatching model of IEHS with multiple functional areas considering heat network flow regulation is proposed.Firstly,the functional area of IEHS is classified and the functional area’s load characteristics are analyzed.Secondly,a heat network model considering refined resistance and dynamic characteristics is constructed and the regulation modes for operation of the heat network are analyzed.Thirdly,an optimal dispatching model of IEHS with multiple functional areas considering heat network flow regulation is established to minimize the operation cost of IEHS with multiple functional areas while considering the penalty cost of RES abandonment and time-of-use electricity price.Finally,a certain IEHS with multiple functional areas is taken as a case study to verify the effectiveness of the proposed optimal dispatching model.The case study shows that the quality regulation mode of heat network considering flow change in multi-stages can effectively reduce RES abandonment,purchased electricity,and the system operation cost.Besides,compared with the independent dispatching of each functional area,the joint dispatching of IEHS with multiple functional areas can reduce the amount of RES abandonment and purchased electricity,and lower the operation cost of IEHS.Aiming at the source-load double uncertainties of IEHS,a distributionally robust day-ahead scheduling model of IEHS with multiple functional areas considering source-load double uncertainties is proposed.Firstly,based on the Wasserstein metric,the source-load double uncertainties models of power system and thermal system are established respectively.Secondly,a distributionally robust day-ahead scheduling model of IEHS with multiple functional areas considering source-load double uncertainties is established to minimize the operation cost of IEHS with multiple functional areas while considering the uncertainty costs.Finally,a certain IEHS with multiple functional areas is taken as a case study to verify the effectiveness of the proposed day-ahead scheduling model.The case study shows that the proposed model with the Wasserstein metric-based distributionally robust optimization method can both overcome the over-optimism of the scheduling strategies obtained by the model with stochastic programming method and the over-conservativeness of the scheduling strategies obtained by the model with robust optimization method.Besides,the economy and the robustness of IEHS’s scheduling strategy obtained by the proposed model can be adjusted flexibly by the value of confidence level and the historical sample size of forecasting errors of RESs’ output power and load.