Optimal Scheduling Of Active Distribution Network Based On Game Theory

In recent years,With a large number of distributed power sources,energy storage equipment,electric vehicles(EVs),and microgrids connected to the distribution network,the trend of the traditional distribution network has changed from unidirectional flow to bidirectional,line capacity cannot meet requirements,and operational control The strategy cannot adapt to the new environment.For this reason,the Active Distribution Network(ADN)was born.The strong intermittent nature of distributed energy output and the large number of EV disorderly charging bring new challenges to the optimal scheduling of ADN.At the same time,the stakeholders in the ADN system have the right to maximize their own interests,and the benefits of each stakeholder are affected by the decision variables of other stakeholders.Aiming at the non-cooperative characteristics due to different interest subjects,it is urgent to formulate an ADN optimized scheduling strategy considering the participation of multi-stakeholders,and game theory is an effective method to study the conflicts of interest among multiple decision-making subjects.In this paper,EV and microgrid are used as entry points,and the ADN optimized scheduling for EV owners as price receivers to participate in ADN and multiple microgrids to participate in market bidding are studied.The specific research content is as follows:1)As the most important active load on the demand side of the distribution network,electric vehicle,one of its important characteristics is the change in power demand with electricity prices.With the increase in the scale of electric vehicles connected to the grid,in order to achieve a win-win situation for the power grid and vehicle owners,not only the impact of disorderly charging and discharging on the grid load,but also the costs of both parties must be taken into account.Based on the above reasons,a master-slave game model of ADN and electric vehicles was established.The upper layer aims at the lowest operating cost of the distribution network,guides the charging and discharging of electric vehicles through reasonable electricity prices and incentive strategies,and coordinates and optimizes distributed power and energy storage.The lower layer is based on a greedy strategy for two-stage optimization.First,the charging and discharging strategy is optimized with the goal of minimizing the cost of charging and discharging at the time-of-use electricity price.Under the constraints of unrestricted returns,the incentive adjustment strategy given by the grid to reduce load fluctuations is maximized.The analysis of the improved IEEE33 node example shows that the model greatly reduces the peak-to-valley difference while maximizing the interests of both parties,avoiding new peaks caused by a large number of electric vehicle charging.2)With the liberalization of the electricity sales side market,it is of great significance to study ADN optimized dispatching under the environment of multiple microgrids participating in the market bidding environment.Therefore,a multi-master single-slave game model based on multi-micro-network bidding is established: the upper-level micro-grid operator first reports the electricity price for electricity sales based on its own revenue.Lower-layer active power distribution system operators consider user-side demand response and achieve market clearing and dispatching of various microgrids with the lowest total cost of their own.An improved IEEE33 node example is used to iteratively obtain the model’s Nash equilibrium solution through genetic algorithm and gurobi solver.The analysis of a numerical example shows that the strategy maximizes the profit of the microgrid on the basis of guaranteeing the interests of the ADN and demand-side users,and increases the enthusiasm of the microgrid to participate in the trading of the distribution market.