Operation Optimization And Decision Making For Integrated Energy System

Human society is now in the ”energy era”,people have never paid so much attention to the sustainable development of energy resources,and the hot issues of energy situation all around the world have aroused more and more attention.The utilizations of solar energy,wind energy,geothermal energy,ocean energy,biomass energy and other renewable energy resources are transferred through the power grid,which serves as the backbone of the energy system transmission,to millions of households.Therefore,the researches on renewable energy power generation and its grid-connection technology have long been carried out,especially with the development of Smart Grid,these researches have rapidly become the hot topics of the researchers in electric power and energy areas all over the world.However,from the viewpoint of the entire energy system,there are many ways to convert from the solar energy,wind energy,fossil resources and other primary energy resources to the terminal cooling,heating,electricity and other types of energy consumption.To achieve the goal of comprehensive energy-saving and emission reduction,it is necessary to optimize the operation of whole energy system from primary energy resources to terminal energy consumption,rather than focusing on only one of the links.Therefore,this thesis studies the coordinated and optimal operation for the integrated energy system,and develops the corresponding optimization algorithm and scientific decision making support system:(1)In order to solve the problem of pumped storage stations dispatch in coordination with thermal power generation,this thesis establishes the model of the reliability constrained unit commitment with combined hydro and thermal generation embedded(RCHTUC),and ensures the reliability of the system operation by the constraint of the spinning reserve.Furthermore,a self-learning group search optimizer(SLGSO)is proposed to deal with the developed model.Under the premise of ensuring the global search performance of the algorithm,the local search efficiency of the algorithm is effectively improved and the convergence speed of the algorithm is accelerated.The simulation results show that the proposed RCHTUC can improve the reliability of system operation.Moreover,the comparisons between the SLGSO and the existing algorithms demonstrate that the proposed algorithm is effective in solving the RCHTUC problem.(2)A model of coordinated scheduling of energy resources(CSoERs)for distributed district heating and cooling units(DHCs)in an integrated energy system(IES)is proposed in this thesis.In the model,several kinds of new energy resources are connected to the distributed power consumption system to meet the demands of electricity loads,heating loads,cooling loads through the power grid.In order to coordinate the optimal operation of the grid and improve the reliability of the system operation,the proposed CSoERs also takes into account the heating and cooling energy storage system embedded in the distributed DHCs.In order to verify the feasibility of the model,the modes of grid-connected scenario and stand-alone scenario are respectively simulated and analysed.The simulation results show that the performance of the CSoERs contributes to significant energy saving and reliability operation in both grid-connected scenario and stand-alone scenario.(3)With a large number of distributed energy resources and distributed DHCs integrated to the power grid,the traditional power grid has been transformed into a large-scale integrated energy system(LSIES).This thesis proposes an optimal strategy based on multi-objective optimization and multi-attribute decision making for the large-scale integrated energy system,considering distributed DHCs in coordination with the traditional thermal plants in the electricity network to satisfy the supply and cooling,heating and electricity load demands.A multiobjective group search optimizer with adaptive covariance and Lévy flights(MGSO-ACL)is developed to optimize the power dispatch of the LSIES with the selected objectives compromising the competing benefits of both the power grid and the DHCs.Furthermore,a decision making method based on an evidential reasoning(ER)approach is used to determine a final optimal solution from the Pareto-optimal solutions obtained by the MGSO-ACL.The ER takes into account both the multiple objectives and the multiple evaluation criteria representing the economy and reliability interests of the power grid and the DHCs.Simulation studies are carried out to verify the effectiveness of the MGSO-ACL and ER.The results demonstrate that the MGSO-ACL can obtain superior Pareto-optimal solutions in terms of convergence and diversity,and the ER is pragmatic in handling the complex decision making problem,in particular for determining the final optimal solution of the power dispatch of the LSIES.(4)The increasing share of variable renewable energy sources and the improving requirements on system security and reliability are calling for important changes in the energy system.The synergies between energy supply networks are of great importance to satisfy the development of the integrated energy system.Hence this thesis presents the study of the coordinated scheduling strategy(CSS),in which,the models of the electricity network and gas network are established in detail,and the operation constraints of the networks are fully considered.In the CSS,a multi-objective optimization algorithm is applied to obtain a Pareto-optimal solution set,and a multiple attribute decision analysis(MADA)using interval evidential reasoning(IER)is developed to determine a final optimal daily operation solution for the IES.The IER takes the multiple attributes of the two networks into consideration and analyses the interdependency between the two networks.Simulation studies conducted on an IES consisting of a modified IEEE30-bus electricity network and a 15-node gas network verify the effectiveness of the CSS,and evaluate the interdependency between the electricity network and gas network.