Optimal Planning Approach Of Urban Building Distributed Energy Systems Under Uncertainties

Urban building Distributed Energy System(DES)is considered as an effective solution to support carbon neutrality and to realize energy conservation and emission reduction in the construction field through the efficient utilization of local renewable energy resources.Urban building Distributed Energy System Planning(DESP)is a complex systematic project,and the planning process is affected by many factors.On one hand,the incomplete information on the demand side in the planning stage leads to the difficulty of building cooling,heating and power dynamic load forecasting,which increases the uncertainty of load forecasting;On the other hand,stochastic factors such as the evolution of energy price and the innovation of equipment technology on the supply side bring challenges to the modeling and optimization of DES.However,the key factors affecting building load and optimization objectives and their influence laws are not clear,and systematic models of urban building DESP integrating uncertainties on both the demand side and the supply side are still missing.Therefore,how to implement the practice of urban building DESP under the influence of uncertain factors has important research value.Taking high-rise office building in Tianjin as the research object and aiming at solving the problems brought by uncertain factors to DESP,this thesis mainly carries out three aspects of research work.Firstly,starting from solving the problem of demand side uncertainty,the uncertain parameters affecting building load forecasting are identified and characterized through literature and field research,the probability distribution of uncertain parameters is sampled by Latin Hypercube Sampling method,the building load forecasting models corresponding to the samples are generated by computer programming,and the forecasting models are simulated by Energy Plus software,a Monte Carlo load forecasting and uncertainty analysis method based on building performance simulation is established.Using this method,the uncertainty quantification results of peak cooling load,annual cooling demand,peak heat load and annual heating demand of high-rise office building in Tianjin are obtained.The SRC method based on linear regression and TGP method based on meta-model are used to analyze the global sensitivity of these four loads.The importance ranking of 11 influencing factors is identified,and the influence law and interaction of the key influencing factors are revealed.Then,as the preliminary work to solve the problem of supply side uncertainty,an urban building Distributed Energy System Mixed Integer Linear Programming(DESMILP)model is constructed based on the concept of Energy Hub,and the model is optimized from the four aspects of demand risk decision-making,time model decomposition and dimension reduction,off-design performance of equipment and foreseeability of the model.Based on the mathematical principle of the DES-MILP model,combined with the quantitative evaluation of economic,environmental and energy indicators,an urban building DESP tool is developed,which includes meteorological database,load database,equipment information database,resource information database and optimization results output.The risk level determination and typical day cluster analysis are carried out for the quantitative results of building load uncertainty,and the analysis results are input into the developed planning tool.The quantitative influence laws of load risk level,carbon emission constraint intensity and energy system structure on economic,environmental and energy indicators in the optimization schemes under different optimization objectives are explored.Finally,aiming at the solution of supply side uncertainty,the key uncertain factors affecting equivalent annual cost and annual carbon dioxide emission of DES are identified by global sensitivity analysis method.The discrete approximations to continuous distributions and random vector sampling methods are used to establish the probability scenarios of uncertain factors.Based on the DES-MILP model,an urban building Distributed Energy System Two-stage Stochastic Programming(DES-TSP)model is constructed.Using the DES-TSP model,the stochastic optimization schemes of urban building DESP considering various uncertain factors of load risk levels,different energy prices and feed-in tariffs are obtained.By comparing the optimization results of the DES-MILP and DES-TSP models,it is found that the optimal system configuration of DES-TSP is different from that of DES-MILP,which indicates that there may be a risk of suboptimal decision-making in the implementation of urban building DESP using DES-MILP model.In the practice of urban building DESP,considering the impact of various uncertain factors on the optimal configuration of the system is more realistic,and the obtained optimization scheme is more reliable as the basis of planning decision.However,the relatively efficient DES-MILP model cannot be replaced by the complex DES-TSP model.It should be weighed and selected according to the efficiency,accuracy and risk requirements of planning practice.The systematic models integrating uncertainties on both the demand side and the supply side established in the presentis thesis provide a basis of theory and method for the research of urban building DESP.The developed planning tool provides technical supports for the practice of urban building DESP.