High-Dimensional Multiobjective Optimization Approach For Energy Hub Planning With Consideration Of Economic,Environmental And Social Factors

In recent years,with the deterioration of the global ecological environment and the growing issue of climate change,it has become imperative to actively develop and utilize various types of renewable energy.Compared with the traditional energy system,which has poor correlation and operates in relatively independent ways,the energy hub uses advanced conversion and storage equipment to achieve flexible conversion and unified management between different energy sources,which become the main form of urban energy supply in the future.Considering that renewable energy has the characteristics of uncertainty,how to reasonably optimize the configuration of the equipment to improve the development and utilization of renewable energy is an important task for the energy hub.Thus,this thesis has carried out the following work for the optimization planning of the energy hub:Firstly,based on the energy hub structure,this thesis establishes mathematical models for all components.By analyzing of the characteristics of different types of loads in the energy hub and their impact on renewable energy utilization,an interval optimization model is developed to describe the uncertainty of demand-side load and its response to real-time price,which lays a foundation for the energy hub planning.Secondly,in view of the natural contradictions among the economic,environmental,and social benefits in the energy hub,this thesis builds a high-dimensional multi-objective interval optimization model for the energy hub.aiming at the minimal system economic cost.the lowest carbon emission,the maximum comprehensive energy efficiency and the lowest user dissatisfaction.In addition,it takes the system equipment configuration capacity.pricing range,system safety and component operating characteristics as constraints.Thirdly,according to the characteristics of the model,this thesis comprehensively uses the interval order relationship and probability degree to deal with the objective function and constraint which includes interval numbers in the optimization model.It transforms the uncertainty optimization model into a deterministic one.At the same time,a non-dominated sorting genetic algorithm based on dimensionality reduction decomposition is employed to solve the problem,which realizes the efficient solution of the optimization model.Finally,this thesis conducts a simulation analysis based on a park-level energy hub to verify the feasibility of the model.Through the analysis of the optimization results,the conflict between multiple goals is verified.The differences between the planning schemes of different energy hubs are compared.which proves that the contribution of demand response to the energy hub is affected by many factors.Meanwhile,the effectiveness of the proposed interval optimization is verified and the performance of the algorithm used in this paper is analyzed.The simulation results of the calculation examples show that the energy hub high-dimensional multi-objective interval planning model proposed in this paper can effectively improve the economic,environmental and social benefits of energy hubs.It can better solve the problem of energy hub planning and operation with uncertain supply and demand side resources.In addition,it can flexibly meet different planning requirements.Therefore,it is supposed to have greater value in engineering practice.