Research On Dynamic Economic Dispatch Of Power System Connected To Plug-in Electric Vehicles

Carbon peak and carbon neutral policy drives the automotive industry toward significant changes.Green low-carbon with the electric vehicles and its related industries tend to scale development,will gradually replace the traditional fuel cars to reduce the emission of polluting gases.However,the emergence of a large number of electric vehicles and their connection with the grid charging and discharging behavior is bound to bring enormous pressure on the power grid,especially in the evening peak hours,the power system is forced to increase production,causing great damage to the safety and economy of the grid adverse impact.Considering that electric vehicles are powered by clean and efficient power batteries,which have the advantages of bi-directional charging and discharging,fast response,high energy density,long life and high flexibility,and electric vehicles are located in parking lots most of the time,therefore,under the premise of meeting the normal needs of users,the charging and discharging moments and hours are managed by vehicle to grid（V2G）technology Therefore,under the premise of normal user demand,the vehicle to grid（V2G）technology can effectively manage the charging and discharging time and duration,i.e.,reverse transmission of electricity to the grid during the peak period and charging through the grid during the low period,which can achieve the effect of peak shaving and valley filling,and relieve the pressure of grid peaking.Therefore,the popularity of electric vehicles is an opportunity and a challenge for the grid,depending on whether the charging and discharging behavior of electric vehicles can be effectively managed.Although wind,light and other new energy generation is the trend of the times,however,from the perspective of China’s and the world’s thermal power generation share and today’s huge power demand gap,in a short time still follow the traditional energy-based power generation structure,supplemented by new energy.In addition,in the face of energy shortage,rising coal prices around the world and tight power supply in recent years,it is of utmost importance to do a good job in energy saving and consumption reduction of thermal power units.In view of this,this study firstly models electric vehicle charging and discharging and introduces it into the dynamic economic dispatching model,then proposes an enhanced exploratory whale algorithm and a constraint processing method based on a mixture of heuristic repair and hybrid repair for this model,and finally,by applying the "thermal power unit only","thermal power unit + disorder",and "thermal power unit + disorder" to the model,we propose an enhanced exploratory whale algorithm.Finally,the proposed algorithm is validated by solving six typical cases of "thermal unit only","thermal unit + unordered pluggable electric vehicle" and "thermal unit + ordered pluggable electric vehicle" scenarios at two scales of 5 and 10 units.The results show that the proposed algorithm has higher accuracy and convergence speed compared with other intelligent algorithms,and has the ability to repair infeasible solutions efficiently compared with other constraint processing methods.The main work can be summarized as follows:（1）Construct a dynamic economic scheduling model considering V2 G.Firstly,based on the objective conditions such as the number of controllable EVs,charging and discharging power,the driving mileage of EVs and the influence of charging and discharging times on battery life,the aggregated capacity of dispatched EVs is analyzed,and the charging and discharging power constraints,as well as the charging and discharging state constraints,are calculated;secondly,the 24-hour hour-by-hour peak-to-valley difference variance is used as the objective function to form a V2 G dispatching model for peak shaving and valley filling;finally,the model is Finally,the model is incorporated into the dynamic economic dispatch to form the dynamic economic dispatch model considering V2 G.（2）To address the complex transmission loss constraints in dynamic economic scheduling,which are difficult to be effectively handled by general constraint handling methods,like the critical handling method,feasibility rule-based method,penalty function method,and heuristic repair methods,this research paper proposes a hybrid repair technique combining heuristic repair and direct repair,i.e.,first reducing the amount of constraint violations to a larger extent by heuristic methods,and then This technique is not only applicable to this problem,but can also be extended to deal with complex inequality constraint problems.（3）To better verify the robustness of the algorithm in this paper,firstly,the effectiveness of the proposed algorithm is verified by 13 standard Benchmark functions;secondly,the effectiveness of the proposed algorithm is verified by the following methods: "thermal power unit only","thermal power unit + unordered pluggable electric vehicle","thermal power unit + ordered electric vehicle",and "thermal power unit + ordered electric vehicle".Secondly,the proposed algorithm is validated by solving six typical cases of "thermal unit only","thermal unit + unordered plug-in electric vehicle",and "thermal unit + ordered plug-in electric vehicle" with two scales of5 and 10 units.The results show that the algorithm has higher accuracy and convergence speed compared with other intelligent optimization algorithms,and the constraint processing method also has efficient infeasible solution repair capability.