Research On The Planning And Operation Approaches For Integrated Energy Systems With Agent-based Modelling And Multi-objective Optimization

In recent years,the energy consumption and emission have been growing rapidly in building sector with the accelerating socioeconomic development and urbanization of China.Residential building is an important type of buildings with distinctive individual characteristics,whose energy consumption depends on not only the building physics,but also the occupants’ behavior consciousness,living habits,movement trajectories,and occupant density.To fulfill the increasing individual and diverse energy demand of the occupants,integrated energy systems and services are becoming the best choice for residential building energy supply.Integrated energy systems can use multiple fossil fuels and renewable sources to drive various kinds of energy technologies and fulfill users’ all kinds of energy demand with high energy efficiency in a region.Therefore,integrated energy systems have been considered as a promising solution for residential building energy supply.However,a series of challenges still exist in the design,operation and optimization of integrated energy systems due to the stochastic dynamics and scenario uncertainties.When optimizing the design of integrated energy systems,the energy demands are usually assumed as given input parameters.However,the demand information of energy systems may not always be available and it can be uncertain,particularly for projects at planning stage.Moreover,the occupant behavior has significant impacts on building energy consumption,and the behavior might also be affected by technical,economic or policy interventions,which in turn will affect the building energy consumption.Therefore,this thesis proposed a holistic approach which combines the agentbased modelling with multi-objective optimization for integrated energy system design and dispatch.The approach considers the occupant behavior effects on the energy supply and demand system,which is able to provide practical solution for system planning without historical data.The major contents of this thesis are as follows:In chapter 1,the state-of-the-art research on building energy demand forecasting,agent-based building energy demand modelling and integrated energy system planning are reviewed.Based on the review,the motivation and contribution of this study are described.In chapter 2,the energy demand forecasting model is developed by agent-based modelling approach for the residential community including different types of flats and households.The energy consuming equipment,energy consuming behavior,occupant interaction and indoor temperature control are modelled in detail.Dozens of demand scenarios are obtained via iterative simulation,with k-means clustering approach being further applied to generate representative stochastic scenarios.A stochastic demand scenario tree is developed as the input of system optimization model.In chapter 3,a multi-objective and stochastic Mixed Integer Linear Programming model is developed for the optimization of the integrated energy system,whereas the minimum annualized total cost and annual carbon emission are two objectives.The stochastic demand scenarios generated at the demand forecasting stage are served as the model input.The ε-constraint method is applied to generate the Pareto frontier,based on which the overall best solution is determined by the multi-objective decisionmaking approach.In chapter 4,the reliability and validity of the approach and models are verified by a case study to determine the integrated energy systems planning for a residential community located in Shanghai.In chapter 5,the main conclusions and limits of this thesis are summarized,and future research focus are pointed out.In summary,this thesis proposed a holistic approach to combine the agent-based modelling with multi-objective optimization for the integrated energy system design and dispatch,which is expected to provide theoretical guidance and reference for similar research.