Research On Two-Layer Scheduling Optimization For Community Integrated Energy System Based On Model Predictive Control

Nowadays,the development of a clean,low-carbon integrated energy system that achieves energy cascade utilization and multi-energy complementarity has become an important trend in the development of energy technology.The construction of a community integrated energy system(CIES)is not only close to the user side,it can meet the diverse energy needs of users,furthermore,through an appropriate optimized scheduling and control strategy,it also can greatly improve the energy efficiency,operating economy and environmental protection of the system.In order to study the optimization of CIES operation economy and dynamic performance on multiple-time-scales,a two-layer optimization strategy based on model predictive control(MPC)is proposed in this thesis.The main research works are listed as follows:1)The composition of a typical CIES,the energy flow of system,the structure and operation mechanism of each equipment in the system are analyzed in detail,besides,the steady-state physical model applied to the upper-layer optimal scheduling is established for the equipment of CIES,and the dynamic physical model applied to the lower-layer optimal control is established for the energy-upstream equipment of CIES.2)A rolling economic optimization dispatch strategy based on MPC methods of upper-layer is proposed,in which a short-term rolling forecast model based on gray prediction is established for renewable energy output and user load in the system.Meanwhile,an rolling optimization scheduling model is constructed with the goal of the lowest cost of system in the forecast time domain,thereby reducing the forecast error,while meeting the diverse energy consumption needs of users,ensuring the economy of system and consuming a large amount of renewable energy.3)A dynamic performance optimization control strategy based on MPC algorithm of lower-layer is proposed.The energy-upstream equipment of CIES is selected as the controlled object,and the prediction model of it is established.On this basis,the dynamic performance optimization control model is constructed to realize closed-loop control.It is used to overcome the large delay and inertia dynamic performance of the controlled equipment,so that they can quickly track the upper-level optimization plan value,and drive the overall energy supply dynamic response rate of the system to be accelerated,in addition,the error between actual energy supply value and the planned value can be reduced and the user’s comfort also can be promoted.4)Finally,the calculation and analysis of the examples of CIES on typical days in summer or winter verify that the two-layer optimization strategy proposed in this thesis can ensure that the renewable energy output and user load in the CIES have better prediction accuracy,and the system can operate in an optimal economic state and realize the complementary advantages of the equipment performance in the system.In addition,compared to the traditional optimization scheduling method that ignores the dynamic response performance of the system,it can improve the dynamic tracking ability of the equipment in the system when the output plan is changed.The result of the example proves the feasibility and superiority of optimizing system scheduling and system dynamic performance control optimization at the same time.