Dispatching Strategy Of Electric Vehicles In Intelligent Community

With its features of energy saving,environmental protection,and low carbon,electric vehicles will replace fuel vehicles as the main transportation in the future.After the rapid development of electric vehicles,intelligent community is the main charging place.In uncontrollable condition,unregulated charging of electric vehicles will increase grid uncertainty.At the same time,the development of technologies such as distributed energy sources and smart electric meter also bring new challenges to the safe and economic operation of distribution systems.Therefore,actively guiding the charging behavior of electric vehicles and flexibly using distributed energy and controllable load scheduling flexibility are crucial for improving the system load characteristics,relieving power grid load fluctuations,and improving dispatch economy.The main contents of the thesis are as follows:1)A two-step dispatch strategy considering time and space dual controllability for electric vehicles is established.Firstly,based on the vehicle-driving characteristics,the charging demand of electric vehicle users was established.Secondly,the access location of electric vehicles in the distribution network was optimized with the objective of minimizing the active power losses.Then using this optimized access plan as a guide for the further optimization of electric vehicle charging scheme.After that,a best charging scheme of electric vehicle under the condition of fixed access scheme would be obtained.What's more,the optimized electric vehicle charging scheme should be used as the basis to correct the access location which has been previously optimized.Through this constant cycle of two-step optimization,the access and charging scheme of electric vehicles which allows the minimal active power losses of distribution network could be acquired.Finally,the proposed model is simulated in IEEE 33 bus system.2)A bilayer rolling dispatch strategy considering uncertainty is established.Firstly,Chance constrained programming was introduced to solve the uncertain model with multiple chance constraints.Secondly,voltage qualification rate expectation was estimated with approach of dynamic probability power flow,and this dispatching strategy taking account of user demand,power margin and voltage qualification rate.In addition,based on the hierarchical structure of bi-level optimization,the upper-level focused on the economy,aiming to minimize charging cost of users,and the lower-level focused on safety,aiming to minimize system load fluctuations.The best charging scheme for electric vehicles would be obtained through bi-level optimization.Finally,in order to verify the effectiveness of the proposed rolling optimization scheduling model,the user charging costs and system load fluctuations under different scheduling modes and different confidence levels were compared.3)A two-stage which include day-ahead dispatch and real-time dispatch strategy considering controllable load scheduling potential is established.This dispatch strategy considers the effect of PV prediction and temperature prediction deviation on the coordinated dispatch of electric vehicles and controllable loads in an intelligent communities.Firstly,based on residential area,controllable load characteristics and thermodynamic theorems,a controllable load output model was established.Secondly,day-ahead dispatch able to get appropriate time-of-use price was established to ensure profit maximization for operators after orderly charging of electric vehicle.Real-time dispatch was introduced to modify imbalance between power supply and demand due to recent prediction error.In real-time dispatch,electric vehicle charging scheme was optimized based on time-of-use price to achieve minimized real-time dispatch cost.Finally,through simulation verification on the proposed model and algorithm,results show that the proposed two-stage dispatch optimization approach can fully release potential dispatch capacity of electric vehicles and controllable load,thus enhancing operator revenue,reducing user costs and improving system load characteristics.