Study Of Demand Response Based On Game Theory In Smart Distribution Network

With the gradual progress of the reform of the power system and the demand side of the market open,with new energy power generation and flexible intelligent load access to the demand side of the distribution network,the load side of power grid play a more dominant role,and participate in the grid scheduling through demand response.There are many stakeholders in this process,and the distribution of benefits is to be solved urgently.Game theory provides a powerful method to optimize the decision-making of multi-stakeholder equilibrium.In the various demand response project,firstly,a variety of smart home appliances to respond through power grid price signal;Followed by new energy,intelligent load and other forms of microgrid access to the distribution network,which can be independent energy optimization.Then the future intelligent distribution network highly developed to achieve distributed management,which can be seen as a large-scale multi-microgrid system,and the distributed power transactions show game features.This paper studies the application of game theory in demand response project,ranging from intelligent load demand response to multi-microgrid demand response,then discuss the game interaction mechanism among the stakeholders in the process of demand response.This paper firstly summarizes the basic form of demand response,price demand response and incentive demand response in the background of smart grid,and focuses on the demand response form of time-of-use price,real-time-price and interruptible load in demand response project.Then,this paper discusses the basic elements,classification and Nash equilibrium solution of game theory,and lays the foundation for the application of game theory in demand response.In this paper,a real-time demand response algorithm based on master-slave game is proposed,which takes into account the existence of power grid and the response order of user's smart home appliance response.In the built virtual power transaction process,the energy management center(leader)provides the virtual price as the strategy,the intelligent home appliances(followers)to their own energy demand as a strategy,both obey the master and slave game.Then through mathematical analysis and reverse Inductive analysis of the game equilibrium point that Nash equilibrium.The simulation results verify the validity of the model and algorithm can effectively avoid iterations to meet the real-time smart grid demand response to some extent.In the future more and more microgrids will get access to intelligent distribution network and form multiple piconets system,then this paper research on the demand response within the distribution network.Aiming at the problem of multi-microgrid game demand response,a double-layer interactive scheduling scheme is proposed.The power optimization model and the game-based multi-microgrid bidding model are established respectively.The microgrid inside model is considered to be based on the time-of-use price mechanism and the interruptible load to complete power optimization,and the scheduling result is applied to the further multi-microgrid game process.The bidding strategy of each microgrid is based on its own residual power supply and running cost.In the dynamic cooperative game process,get the optimal strategy and complete the demand response to the distribution network level.The validity of the proposed method and models is verified by simulation results based on IEEE9 system.Genetic algorithm was used to simulate the microgrid in the process of game bidding strategy,the results conform to the law of electricity market,demand response projects to provide reference for multiple piconets in power grid.On the other hand,the multi-microgrid energy transaction model based on block chain technology is proposed,which is based on the game trading strategy of each microgrid,the low cost of decentralized distributed structure guarantee transaction,and the distributed power in the background of smart grid trading reference.