Multi-objective Optimization And Dynamic Game Approach For Optimal Planning And Operation Of Integrated Energy Systems

With the increasing global concerns on the problems of intense relationship between energy supply and demand as well as the heavy environmental pollution,integrated energy utilization is becoming more and more important.Integrated energy system(IES)integrating various kinds of renewable and non-renewable energies,such as wind,solar and gas,optimizes the energy structure and reduces the huge dependence on traditional fossil fuel energy supply.However,various kinds of energies integration and different types of participants of IES make the planning and operation of it more complex.In another words,the planning and operation of IES involves different kinds of individual participants.Various coupling relationships between them lead to more complicated investment,planning and operation of IES.In addition,the diversity of energy demands and intermittence of energy supply caused by renewable sources in the IES will directly affect the economic and environmental performances of it.In terms of this,traditional deterministic single-objective optimization and those methods considering merely the overall system performance are no longer suitable for solving the planning and operation problems of future IES.Therefore,this paper researches the optimization and game-theoretical models and approaches for analyzing the planning and operation problems of IES.(1)In order to solve the investment and planning problems of small-scale integrated energy systems,we develop a framework of multi-objective interval optimization and evidential reasoning(ER)approach for the investor.With the consideration of uncertain renewable energies as well as various kinds of planning and operation constraints,the proposed framework provides the investor with efficient optimization and decision making methods for compromising his conflict interests on investment cost and risk.Case studies conducted on a small-scale district heating system and a small-scale district heating and cooling system demonstrate the applicability of the proposed optimization and decision making framework.(2)For solving the equilibrium interactive strategies of multiple distributed energy stations(DESs)and multiple energy users(EUs)who have leader-follower characteristic in the district energy network(DEN),we develop an energy trading model based on the multi-leader multifollower(MLMF)Stackelberg game.Through applying game-theoretical approach,we obtain the equilibrium generating and pricing strategies of DESs under the environment of perfectly competitive market,as well as the equilibrium demanding strategies of EUs who are actively participating in the energy trading.In addition to the analytical model and approach,a best response algorithm is also developed for solving the equilibrium interactive strategies in an iterative way.(3)For solving the optimal operation problem of large-scale IES composed of electricity network,natural-gas network and multiple district energy network(DEN),we propose a hybrid multi-objective optimization and game-theoretical approach(HMOGTA).In HMOGTA,multiobjective optimization method are used for solving the conflict interests of cost minimization of electricity and natural-gas network.While game-theoretical approach is applied for solving the energy trading problem between DESs and EUs who have the relationships of rivalry and affiliation.Through applying HMOGTA,the objective of each participant in the IES can be fairly satisfied.Simulation results demonstrate the feasibility and efficiency of the HMOGTA,which provides a method for analyzing the optimal strategies of various kinds of participants in the future large-scale IES.