| Based on the background of "carbon peaking and carbon neutrality",how to build a green,low-carbon,safe and stable energy system is one of the problems that the whole society needs to solve urgently.Energy hub(EH)can realize the coordination and complementarity of multiple energy sources and cascade utilization of energy,which is of great significance for promoting the consumption of renewable energy and improving environmental benefits.Under the environment of open energy market,EH includes multiple subjects in energy production,storage,consumption and multiple principals.How to develop a green and low-carbon energy operation model is the current research focus.In addition,the complex coupling relationship and the uncertain characteristics of source and load in EH also bring many difficulties and challenges to the optimization of EH.Therefore,this paper,focusing on EH cooperative optimization of multi-object game and optimization scheduling of multi-time dimension,studies EH multi-time dimension optimization scheduling including game theory.The main research contents are as follows:Firstly,a mathematical model of EH basic structure and its equipment is established.The uncertainties of wind power,photovoltaic and other renewable energy sources and multi-energy loads are processed by Latin hypercube sampling method and scene reduction method based on Kantorovich distance.Secondly,in order to build a green and low-carbon energy operation mode,an EH multi-energy collaborative optimization and scheduling model based on multi-subject game and ladder carbon trading mechanism is proposed,and a multi-subject game model is built with energy hub operators as leaders and energy storage operators and multi-energy users as followers.At the same time,a ladder carbon trading mechanism is introduced into the game model to reduce the carbon emissions generated by the interaction of EH players.A distributed algorithm combined genetic algorithm with the CPLEX solver is used to solve the proposed model.The simulation results show that the proposed multi-subject low-carbon game model can not only improve the economy of each player,but also effectively reduce the overall carbon emissions of EH.Finally,to reduce the impact of carbon emission level and the uncertainty of source and load on system operation,a multi-time dimension low-carbon optimization and scheduling strategy for EH based on a reward and punishment ladder carbon trading mechanism and distributed model predictive control(DMPC)is proposed.Based on the reward and punishment ladder carbon trading mechanism,the EH low-carbon optimization model with three time-dimensions,day-ahead,within the day and realtime is established.In the real-time optimization stage,considering the low solving efficiency of the traditional MPC,a real-time adjustment model based on DMPC control is built by decomposing the overall optimization.Through simulation examples,the influence of various parameters of the reward and punishment ladder carbon trading mechanism on EH low-carbon optimization and scheduling is analyzed,and the effectiveness of the multi-time dimension scheduling strategy based on DMPC is verified in terms of improving the system solution efficiency and reducing the source load uncertainty. |
