Frequency Control And Economic Dispatch Of Power Systems With Electric Vehicles

It is an important task for power system to maintain the active power balance between the power generation and the load demand. With the increasing penetration of renewable generation such as wind and photovoltaic in power system, the traditional active power balance method which uses the generation side resources to track the fluctuations of load is meeting a great challenge. The large-scale application of electric vehicles will provide sufficient and flexible regulation resources in the load side of power system. Integration of electric vehicles into the active power dispatch and control of power system will have an important significance for guaranteeing the safely, steadily and economically operation of power system, as well as achieving the coordinated dispatch and control between the generation and load under the future electric environment. In view of this, the frequency control and economic dispatch problems of power systems with electric vehicles are chosen to be the research topics of the dissertation. The major work and results of the thesis are summarized as follows.An optimal PI/PID controller design method for AGC based on a social learning adaptive bacteria foraging algorithm is proposed. The social learning mechanism and adaptive step strategy are introduced into the standard bacteria foraging algorithm in this method. By social cognition and learning from the global best position and historical best position of itself, the chemotaxis step can be adjusted adaptively. By modifying the processes of chemotaxis, swarming, and reproduction during optimization, the algorithm convergence and accuracy can be improved. The simulation results show that the PI/PID controller designed by the proposed method has a better ITAE value and dynamic time domain response performance, compared to the controllers designed by other methods. It can resist the influence of system parameters variations on frequency control performance, which shows a strong robustness and stability.An optimal fuzzy PID controller design method for AGC system with electric vehicles based on a hybrid niching bacteria foraging-pattern search algorithm is proposed. This method introduces the niching technology and pattern search into the standard bacteria foraging algorithm. By using the health sharing mechanism and restrictive competition strategy, the population diversity in the production period can be increased, and the global optimization ability can be improved. The optimal solution obtained by the bacteria foraging algorithm is set to be the initial point of pattern search, which makes the algorithm continues in the neighborhood of the current optimal solution, thus improves the accuracy of the algorithm. The simulation results show that the fuzzy PID controller designed by the proposed method can improve the dynamic control performance of AGC system with electric vehicles. Compared to the controllers designed by other method, the proposed controller has faster settling time, less overshoot, lower ITAE value, and stronger robustness.A new load frequency control model considering the interaction between electric vehicle battery swapping stations and power grid is proposed. An interaction framework, namely station-to-grid (S2G), is presented to integrate BSS energy storage into power grid for giving benefits to frequency regulation. The BSS can be regarded as a lumped battery energy storage station through S2G framework. A supplementary AGC method using demand-side BSS energy storage is developed considering the constraint of available regulation reserve (ARR). The ARR is calculated based on the queueing theory and safety stock strategy. The effects to the AGC performance are evaluated through simulations by using a two-area interconnected power grid model with step and random load disturbance. The results show that the demand-side BSS can significantly suppress the frequency deviation and tie-line power fluctuations.A new supplementary AGC strategy considering the constraint of dynamic controllable capacity of BSS is proposed and the regulation economics are analyzed. A Monte-Carlo stochastic simulation (MCSS) method is utilized to estimate the equivalent controllable capacity (CC) of BSSs, and then the lumped S2G equivalent model subject to SOC limits and CC constrains is presented. A filter-based coordinated control strategy is used in the AGC model considering S2G, which regards all the BSSs in the control area as a virtual energy storage station. Only the fast-cyclic component of the control signal will be dispatched to BSS storage by the filter-based coordinated control strategy. The proposed AGC strategies are verified in a two-area interconnected power system with relatively large random load and wind power fluctuations. The simulation results and economic analysis demonstrate that the proposed strategies are feasible, and the energy storage in BSSs is a good supplementary resource for power system frequency control.A new dynamic economic dispatch model considering the constraints of electric vehicle dispatchable capacity and total charging demand as well as its solving algorithm are proposed. In this model, we consider the electric vehicle aggregator (EVA) as the dispatched objects, and get the dispatchable capacity and total charging demand at each interval by simulating the travelling of electric vehicles. Penalty cost, which is made up of load fluctuation variance, is used in the objective function to avoid the the charging at peak load. As for solving method, a self-adaptive chaotic biology based optimization is proposed. In case study, it reveals that peak-valley difference and total power generation cost can be decreased by optimizing the discharging and charging planning of EVA and generation scheduling. The proposed algorithm converges faster than other algorithms, and can also performs better in robustness, exploitation and so on.