Research On Synergistic Optimization Of Wind Power And Electric Vehicle Battery Switch Station Considering Its Multiple Benefits

The safe and economical operation of the power system is based on a reliable power supply and accurate load forecasting.However,substantial indeterminacy caused by large-scale wind power integration presents the power system with significant challenges.As the scale of wind power integration continues to increase,fossil energy power generation is gradually subjected to certain constraints.The traditional method of using only fossil energy generators to passively manage the significant indeterminacy of wind power no longer works.This has led to a need to explore regulated means to completely or partially eliminate the uncertainties of wind power before the wind farm becomes integrated into the power system.This initiative can help reduce the impact that wind power integration has on the power grid.In the context of large-scale wind power integration and rapid development of electric vehicles,a cooperative pattern is proposed to use a battery switch station(BSS)to address high uncertainties incurred by wind power integration.This would directly remove the significant indeterminacy of wind power.The synergistic optimization of wind power and BSS is taken as the core problem in this thesis.It has important theoretical and practical significance for boosting the utilization of wind power,promoting the development of electric vehicle industry,and alleviating the dual pressure of energy crisis and environmental pollution.Rather than the traditional approach of passively dealing with wind power integration,a joint operation pattern of centralized charging station(CCS)and wind farm is proposed to directly eliminate uncertainties of wind power and to actively cope with its indeterminacy.Based on the battery swapping pattern of centralized charging and unified distributing,a cooperative framework of the CCS and the wind farm is established.Being built adjacent to a wind power gathering station,the CCS could work jointly with the wind farm to operate in power system as an independent enterprise.By combining the actual operational characteristics of the wind farm and the CCS,a multidimensional operating index evaluation system is created for the integrated system.Under the cooperative framework,the capacity optimization issue,the day-ahead generation scheduling issue and the synergistic dispatching issue of the integrated system are studied respectively.In order to balance the initial investment and operation revenues of the integrated system,a multi-objective capacity planning model is proposed for the CCS based on the wind farm’s known capacity.The model aims at maximizing the probability of realizing diverse indices of the integrated system.Additionally,in this model,planning and dispatching are combined to improve the feasibility of planning results in the operational stage.This approach takes the effects of randomness into account for wind power and battery swapping demand.The model is solved through combining Monte Carlo simulation and a genetic algorithm(GA)based on segmented encoding.The simulation results verify the validity of the model and the algorithm.As shown in the simulation results,the proposed capacity optimization model could comprehensively accounts for investment cost,wind power price,battery life,and other factors,and then the integrated system weighs whether a principle of “charge when low and discharge if high” can be adopted to earn profits.On this basis,the optimal capacity of the CCS is determined and the operating indices of the integrated system are improved as well.As a special power plant in power grid,the generation scheduling of integrated system not only affects its own operation revenues,but also affects the security of power system.Therefore,based on dependent chance goal programming,a multiobjective generation schedule optimizing method for integrated system is proposed.Firstly,an operational control strategy of the integrated system is proposed according to its cooperative framework.Then,on the basis of the probability distribution of forecasting error of wind power and battery swapping demand,a day-ahead generation scheduling optimization model is constructed.Finally,through the comparative analysis of three scenarios,it shows that the model can optimize the generation schedule to meet the decision-maker’s expectations as far as possible according to the importance of different operating indices and risk tolerance level.Joint operation of the CCS and the wind farm generates a benefit in coordination,through which a multi-win situation of wind power,electric vehicle and power grid are achieved.In order to deal with the indeterminacy of wind power and battery swapping demand,a two-stage collaborative scheduling model is designed for the integrated system.In the first stage,a day-ahead dispatching model is proposed,aiming at maximizing the probability of realizing diverse indices of the integrated system.The decision variable in the first stage is the stored energy rather than the charging/discharging power of CCS so as to provide effective guidance on further adjustment of CCS in the intra-day stage.In this way,the overall optimal of the reserve capability of the CCS is realized.In the second stage,an intra-day optimization model is established.In order to cope with the randomness,the proposed model utilizes ultrashort term prediction of the wind power and the battery swapping demand to schedule the charging/discharging power of CCS in a rolling manner.The simulation results show that compared with the single time scale scheduling model,this two-stage model is more conducive to explore the multiple benefits of CCS,and to avoid the situation that the adjustment capability is exhausted prematurely.In addition,the generation schedule tracking ability of the integrated system is improved accordingly.In the situation that wind power and battery switch station belong to different investment bodies and do not want to constitute an integrated system,a cooperative framework of distributed wind power and battery charging & swapping station is proposed based on the charging and swapping pattern.Based the game theory,the process of pursuing self-interest maximization is modeled as a Stackelberg game model in which the wind power operator is the leader and the battery charging & swapping station is the follower.According to the characteristics of strong Stackelberg equilibrium and weak Stackelberg equilibrium of the model,the corresponding solving methods are proposed respectively.Simulation results show that the proposed model can automatically realize the optimal allocation of resources and achieve win-win situation in the process of pursuing the maximum interests of wind power operators and battery charging & swapping station.