Game-theoretic Optimization Of Demand Side Based On Residential Energy Consumption Behavior

With the development of advanced measurement technology,communication technology,and electricity market reform,demand side management(DSM)can take part in power grid dispatch in the form of demand response(DR)project.As an important part of demand side,residential users have rich flexible load resources.However,the optimal decision of residents becomes extremely challenging due to the randomness of energy consumption,the diversity of decision-making subjects,and the incompleteness of market information.At the same time,such factors also limit the further development of residential DR.Therefore,the research on energy consumption behavior and multi-subject decision-making optimization will contribute the in-depth excavation of residential DR and alleviate the contradiction between energy supply and demand.Under such background,based on the study of residential consumption behavior,this dissertation focuses on the DR decision problem in residential flexible load,distributed energy equipment,and DR day-ahead bidding from the aspect of game information’s integrity.The main research work of this dissertation is as follows:Firstly,aiming at the problem how to obtain residential load demand and flexible load dispatching interval,a bottom-up prediction model of consumption behavior is proposed in the dissertation.Prediction model of consumption behavior mainly includes extraction modular of historical similar days,analysis modular of consumption behavior,and prediction modular of energy consumption.In which,extraction modular of historical similar days is proposed to select similar days from historical days by formulating similarity eigenvector;analysis modular of consumption behavior is proposed to forecast appliances’ consumption behavior by analyzing appliances’ behavior in historical similar days;prediction modular of energy consumption is proposed to forecast residential energy demand by formulating consumption behavior model and appliance’s electrical model.Secondly,aiming at the optimal problem of flexible load in DR process under complete information,non-cooperative game approach and cooperative game approach are proposed,to optimize storage capacity and load consumption.In the non-cooperative game,game model is founded for the minimal daily cost of each community;then,Nash equilibrium and optimal storage capacity are proved;last,distribution algorithm is proposed combining particle swarm optimization and interior point method.In the cooperative game,game model is founded for the minimal daily cost of all communities;then,cost allocation method is promoted from the perspective of probability;last,individual rationality and equivalence of allocation method is proved.Thirdly,aiming at the optimal problem in two-way energy trading with grid under incomplete information,economic dispatch problem of EV is proposed with Bayesian game approach.For the two-way energy trading scenario with incomplete information,basic assumptions are stated at the beginning.And then,cost model and profit model of EVs are founded.Furthermore,Bayesian game model is founded for the maximal expected profit of each community by optimizing EVs’ charging and discharging strategy.And then,the existence and uniqueness of Bayesian equilibrium is proved.Fourthly,aiming at the flexible load resource breach and incomplete information problem in DR day-ahead bidding market,game-theoretic bidding strategy is proposed under the electricity market.For the breach problem of DR resource,auxiliary equipment,such as gas boiler and storage,are used to reduce breach ratio of DR resource.And then,day-ahead bidding price model,auxiliary equipment model,and DR resource breach model are founded,respectively.Basically,profit model is founded by selling DR resource,electricity and heat.Furthermore,Bayesian game model is founded for community operator’s bidding strategy,and then distribution algorithm is designed to search the equilibrium.Finally,aiming at the practicability problem of game-theoretical technology in the real system,experimental study is designed with residential load management system.Combining the experimental condition,complete information game and incomplete information game are founded for experimental system,in which residential game decision strategy is discretized into load switching variables.Furthermore,the founded game models are verified with experiment.Experimental result shows that the proposed game approach can make the optimal decision for the system operation and has a good practicability.This dissertation has given the modeling techniques on residential consumption behavior prediction,complete information game-based optimization for flexible load,Bayesian game game-based optimization for distributed energy equipment and day-ahead bidding of DR resource.The result of the dissertation can provide theoretical support for the design of operation mechanism in residential DR.