Research On Low Carbon Economic Scheduling Of Microgrid With Electric Vehicles Considering Conditional Value At Risk

As the global energy crisis and environmental problems continue to deteriorate,countries around the world are actively promoting energy transition and accelerating the exploration of new ways of distributed power generation.Microgrid,as an important carrier to gather various distributed power sources,is conducive to improving the utilization rate of distributed power sources and reducing the impact of renewable energy on the grid.With the increase of Electric Vehicle(EV)penetration,if all EVs use thermal power in the process of charging,the advantages of energy saving and emission reduction of EVs will not be given full play.How to complement the charging and discharging characteristics of EV with the clean energy advantages of microgrid with stochasticity is important to realize the economic and low-carbon operation of microgrid and EV.Based on this background,this paper conducts further research on EV participation in optimal microgrid dispatching,and the main research contents are as follows.Firstly,based on the operating characteristics of each distributed power source of the grid-connected microgrid,the power output model of each distributed power source is established.Based on the historical data and spatio-temporal characteristics of EVs,the EV disorderly charging load model is established.By analyzing the adverse effects of EV unordered charging on microgrid,the V2 G technology is introduced and the implementation of V2 G technology is discussed.Secondly,to address the problem that the carbon trading mechanism does not adequately guide EVs to bring into play low-carbon benefits,this paper introduces the EV carbon quota mechanism to guide EVs to reduce carbon emissions caused by the charging process and sell the obtained carbon quotas in the carbon trading market.Considering the ladder-type carbon trading mechanism and EV carbon quota mechanism,an optimal dispatching model of microgrid based on EV and demand response load is established.Based on the time-of-use tariff,the model is solved by using multi-objective particle swarm algorithm and fuzzy affiliation function with the lowest EV charging cost and the lowest microgrid daily operation cost as the objective functions.The simulation results verify that the proposed model can fully mobilize EVs to participate in microgrid dispatching,improve the level of scenic power consumption,and balance the economy and low carbon of both supply and demand.Finally,considering that the uncertainty of the scenery output will bring large risk fluctuation to the microgrid revenue,the Conditional Value at Risk(CVa R)method is used to measure the risk fluctuation of the revenue brought by the uncertainty to the microgrid operation,and to achieve the balance of the microgrid risk and revenue.Under the laddertype carbon trading and EV carbon quota mechanism,a master-slave game two-layer optimization model of microgrid and EV with CVa R is developed.In the upper layer,the objective function is to maximize the revenue in the two phases of day-ahead dispatching and real-time regulation with CVa R,coordinate the output of each dispatching unit,and formulate a reasonable charging and discharging tariff strategy;in the lower layer,the objective function is to minimize the charging cost of EV users in response to the charging and discharging strategy.The KKT condition,Big-M method and linear programming strong pairwise theory are used to transform the game model into a single-level mixedinteger linear programming problem for solving.The validity of the proposed model is verified by arithmetic examples.