Research On Collaborative Operation Decision For Distributed Energy System CCHP Under Uncertainty

With the global energy resources reducing and environmental pollution increasing,countries pay more attention to adjusting of energy structure,improving the utilization efficiency of energy and protecting the environment.In China,the traditional electric power grid has large transmission line energy loss and environmental pollution.Compared with the traditional electric power grid,the distributed energy system(DES)which generates energy near/on the demand side has the advantages of less transmission energy loss,lower pollution emission and higher energy efficiency.As a kind of distributed energy system,the natural gas-fired cominbed cooling,heating and power(CCHP)system has been booming in recent decades.The natural gas-fired CCHP system has effective utilization of low-grade waste heat and is encouraged from government.Therefore,the study of natural gas-fired CCHP system has become an important subject in the field of energy consumption in China.This thesis focuses on the CCHP system energy optimization problem regarding building clusters,building and manufacturing facility as users.The CCHP system coupled with energy storage,ground source heat pump(GSHP),and electric vehicles(EVs)is collaboratively optimized by considering uncertatinty,carbon emission regulations,and time-of-use tariff.From the perspective of economy,environment and energy,the mathematical programming models are formulated by using operational research methods such as stochastic programming,and solved by the corresponding algorithm.The main research work and results are summarized as below.(1)A collaborative operation decision for building clusters CCHP system under stochastic energy demand is studied.The energy demand of buildings cannot be predicted accurately due to variation with season,temperature and other factors.Therefore,the stochastic programming is adopted to describe uncertain factors.A two-stage multi-objective stochastic programming model is established with the aim to minimization of each building's cost under collaborative strategy.Furthermore,weighting method and Benders multi-cut algorithm are applied to solve the multi-objective programming model.The results show that 1)the collaborative strategy can reduce energy cost compared with non-cooperative strategy;2)it is significative and valuable that two-stage stochastic programming method is adopted to describe the uncertain energy demand.(2)A collaborative optimization decision problem is proposed for building clusters CCHP system coupled with energy storage under various uncertain factors.The energy storage plays an important role in peak load shifting.The mismatch between energy supply and demand can be alleviated by integrating energy storage devices and CCHP system.There are lots of stochastic factors in real life,thus a two-stage multi-objective stochastic programming is formulated to solve the optimization problem of building clusters CCHP system integrating energy storage under uncertainty with the aim to minimize each building's cost.The LP-Relaxation and Benders decomposition algorithm are adopted to solve the stochastic programming model.The results show that 1)the uncertainty of energy price has more influence on the optimal operation cost of energy suppy system than stochastic energy demand;2)two kinds of uncertain factors have cumulative effect on the optimal cost of building clusters energy supply system.(3)A collaborative optimization problem for building CCHP-GSHP system under carbon tax policy considering stochastic energy demand is studied.The CCHP system coupled with GSHP can provide electricity,cooling and heating to building with higher enegy efficiency.Considering the decision maker with different risk preference,a stochastic chance constraint method is proposed to formulate a stochastic programming model with the objective of minimizing the total cost,primary energy consumption and carbon dioxide emission.The different building categories(i.e.,office,warehouse,restaurant and hotel)are characterized and analyzed in the case study.The results are that 1)the economic,energy and environmental indexes of the four types of building show an increasing trend with the reliability level increasing;2)the operating cost and carbon emission of the four buildings are increased and decreased when carbon tax price is increased,respectively.Besides,the maximum reduction of carbon emission occurs for the hotel building.(4)The energy collaborative scheduling problem of integrating CCHP system,electric vehicle(EVs)and manufacturing facility under time-of-use(TOU)tariff is researched.The power grid overload can be aggravated due to the centralized charging behavior of EVs,when the EVs of industrial workers are parked in the parking lots.To solve this issue,the Vechile-to-Manufacturing(V2M)mode and collaborative decision optimization problem are proposed.A collaborative decision model is formulated to study the optimization of EVs,power grid and manufacturing facility overall system in order to minimize the total cost.The particle swarm optimization(PSO)algorithm is applied to solve the mathematical model.Finally,through numerical experiments,it finds that 1)the collaborative strategy can achieve 22.84% energy cost saving in a typical winter day for one-shift production,34.81% in a typical summer day for one-shift production,16.73% in the winter day for two-shift production,and 29.96% in the summer day for two-shift production compared with baseline case;2)the carbon dioxide emission and primary energy consumption of the system are reduced averagely by 22% and 23% after adopting the optimal strategy.Based on the above analyses and discussions,the main innovations of this dissertation are summarized as below.(1)The optimization problem for building clusters CCHP system under stochastic factors is studied.A novel collaborative strategy and a two-stage multi-objective stochastic programming model are proposed.Moreover,Benders multi-cut algorithm is applied to solve the mathematical model.The proposed model and improved algorithm are proved to be effective through numerical experiments.(2)The collaborative optimal problem on building clusters CCHP system integrating with energy storage devices considering various stochastic factors is studied.A two-stage multi-objective programming model is formulated and solved by LP-Relaxation and Benders decomposition algorithm.(3)The research idea of collaborative optimizing building CCHP-GSHP coupling system under carbon tax policy considering decision maker with different risk attitude is proposed.A stochastic chance constraint stochastic programming model is built,and the experiments demonstrate that the collaborative strategy can achieve economic,energy and envirmental benefits.(4)The collaborative optimization problem among CCHP system,EVs and manufacturing industy is studied.The concept of V2 M and the collaborative decision framework under V2M/V2 G integration are proposed.The electricity overload problem caused by a large number of EVs charging behaviors can be alleviated.A mixed integer nonlinear programming model is formulated and solved by improved PSO algorithm.This dissertation systematically studies the collaborative optimization problem of CCHP system for building clusters,building and manufacturing facility under uncertainty,carbon tax regulation and TOU tariff by applying various theoretical methods(e.g.,operations research,exact algorithm and heuristic algorithm).The theoretical research results of operations research and management science is enriched,and the advices for decision maker obtaining optimal energy scheduling scheme is proposed by this thesis.Finally,the paper points out several interesting directions about CCHP system optimal problem in the further work.For example,the performance of the collaborative operation strategy can be investigated under other carbon emission policies(e.g.,carbon trading policy).