Research On Distributed Optimization Method Of Integrated Energy System Considering Uncertainty

As the development direction of the modern energy system,the Integrated Energy system breaks the independent mode of traditional multi-type energy flow and information flow.And IES meets system needs through complementary energy such as electricity,gas,and heat,and improves the comprehensive utilization of energy.effectiveness.The multi-type random factors in the integrated energy system pose new challenges to the stability of the system operation.The traditional centralized decisionmaking method is limited by the information transmission bandwidth,and it is difficult to meet the scheduling requirements of the multi-regional complex system.There are slow decision-making speed and information.Confidentiality is not strong and other issues.In this context,this paper studies the robust optimization and distributed scheduling strategy of an integrated energy system considering multiple uncertainties.The main work is as follows:This paper first elaborates and analyzes the development trends of integrated energy systems at home and abroad and the current research status of robust optimization and distributed optimization scheduling.Aiming at the typical structure of the integrated energy system,a mathematical model of the main energy production,transmission,conversion and storage equipment of the integrated energy system has been established.In view of the characteristics of the source and load uncertain factors in the operation process,based on the traditional robust optimization theory,a system classification uncertainty set modeling method based on Wasserstein distance is proposed,and the goal is to take the lowest overall operating cost of the system as the goal,and the source and load uncertainties are considered.A two-stage robust optimization strategy for a comprehensive integrated energy system that selects the worst operating scenarios in the system through sub-problems to meet the robustness requirements of the system.The proposed strategy can reduce light abandonment and load shedding by reducing the impact of source load uncertainty in the worst operating environment,which can effectively reduce system operating costs and improve comprehensive energy utilization.The proposed robust optimization method is converted and solved by the Column And Constraints Generation(CCG)algorithm,and the simulation results of the simulation examples verify the effectiveness of the proposed robust optimization method.The above robust optimization strategy comprehensively considers the uncertainty of the internal source and load of the single-region system.For the background of the distributed integrated energy system with multi-region interconnection,this paper is based on the proposed inter-regional multi-energy decoupling scheme and the interregional information transmission framework.A regional autonomous distributed scheduling method is proposed.The proposed optimization method only needs to exchange shared information in adjacent areas,which can effectively reduce the bandwidth burden and protect information security.In order to solve the proposed optimization strategy in parallel,this paper proposes an improved ADMM algorithm to ensure convergence in the case of multi-energy flow.Finally,through the proposed improved ADMM algorithm,the distributed optimization example is solved and analyzed,which verifies the effectiveness of the proposed algorithm and distributed scheduling strategy.