Communication Delay Compensation by Adaptive Parameter Estimation for Electric Vehicle Aggregators Joining Load Frequency Control

With the continuous increase of renewable energy, the power system will face the problem of insufficient frequency response capability. Electric vehicles (EVs) combine the characteristics of mobile energy storage and controllable loads and can provide capacity support in frequency control. However, as a distributed frequency regulation resource, there is inevitably a communication delay in the process of electric vehicles participating in frequency regulation, which will affect the stability of the system frequency. Therefore, this paper proposes a frequency regulation delay compensation control strategy based on model predictive control (MPC). Firstly, the model predictive control is utilized to design an electric vehicle frequency response model controller and establish a frequency regulation model of the power system containing electric vehicles; then, by establishing the selection rules of the control signal, the multi-step control signal is dynamically adjusted during the rolling optimization of the predictive model to form a delay compensation mechanism considering delay and packet loss; finally, multi-scenario simulation is conducted, and the results show that the proposed strategy can roll-optimize the auxiliary frequency regulation function of electric vehicles, significantly reduce the influence of communication time delay, and improve the system frequency stability.

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.

The aim of this thesis is to investigate novel recharging schemes for energy demand management (DM) of electric vehicles (EVs). While there has been a lot of work highlighting the importance of energy DM of EVs, most of the reported works do not expand on suggesting how such a DM system may be implemented. In this thesis the focus is on two aspects of DM system implementation. At the instantaneous control time scale, an alternative mechanism for frequency regulation with the aim of neutralising sudden changes in output power of electric generators is presented. At the recharge planning time scale, the aim is to avoid congestion and undesirable voltage drops in the distribution system, and a novel approach is presented that can improve voltage profiles. The problem of considering both voltage congestion and frequency regulation in a composite DM framework is also addressed. At the instantaneous control time scale, a novel distributed recharging rate controller is presented that is based on non-linear control and that yields a real time and distributed solution. This controller minimises communication overheads and allows EVs to join and leave at arbitrary times. From the perspective of recharging rate allocation, the controller achieves a Pareto efficient allocation which is also proportionally fair. The proposed controller is then applied to a system with a single, isolated, and unregulated synchronous machine and it is shown that the frequency can be used as proxy to the imbalance between produced and consumed electric power and hence communication overhead can be eliminated in such cases. A protocol is also discussed that can modify the controller and can implement the modified controller in a multi-machine system. Simulation is used to show the frequency regulation and fairness of recharging rates of EVs when the protocol and the modified controller are used. Subsequently, the integration of the recharging rate controller with the legacy protection system is also discussed. At the recharge planning time scale, the problem of congestion in the distribution system is addressed. Most of published literature on distribution system voltage issues deals with control of various network elements, for instance, on-load tap changers or banks of shunt capacitors on the distribution feeders. In this thesis, a complementary approach 7 8 is presented that can also improve voltage profile by scheduling EV load in such a manner that undesirable voltage drops are avoided or their severity is diminished. In this context, a novel approach is presented for recharging EVs in the same geographic neighbourhood that share the same secondary circuits when recharging. The approach is based on a numerical method called Smoothed Particle Hydrodynamics (SPH) that has been previously used by other researchers to solve the equations of fluid dynamics. The characteristics of the method used for the proposed approach as well as its performance in term of improvement in the reduction of voltage drops and its adaptation to elastic and non-elastic loads is highlighted via simulation. Finally, the approach is extended to also provide a frequency control reserve service.

Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies.

Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach

When electric vehicles (EVs) are integrated into the grid for frequency regulation, the energy stored the EVs would be released excessively without a proper schedule. The system cannot appropriately utilize the energy of the generator and EVs. Many studies only focus on frequency stability but ignore the output of EV cluster. Based on this, this paper proposes a control strategy (Fuzzy-PI) that uses fuzzy control to generate a variable coefficient, which is multiplied with the PI control signal as the control signal for the EV cluster. The coefficient can increase or decrease the EV output power depending on the variation of the frequency deviation. In order to contribute an appropriate number of EVs, this paper optimizes the ratio parameter of EVs invested in V2G with the goal of reducing EV cluster output and improving frequency stability. The private cars, buses and taxis are considered for integrating into an EV cluster to participate in the grid auxiliary frequency regulation. A single-area system model for the participation of EV cluster in the micro-grid frequency regulation is established. Finally, by the comparative study of simulation algorithms, the results demonstrate the superiority of EV cluster participation in frequency regulation, as well as the effectiveness of the proposed Fuzzy-PI control and optimization strategy.

With the development of complex renewable energy systems, the frequency control and regulation of the power grid powered by such renewable energies (e.g., wind turbine) are more critical, since the adopted different power generators can lead to frequency variations. To address the frequency regulation of such power grids, we will present a variable coefficient coordinated primary frequency regulation scheme for synchronous generator (SG) and doubly fed induction generator (DFIG). The variable adjustment coefficient of DFIG is defined according to the current reserve capacity, which can be applied to adjust different operation conditions to regulate the frequency variation within a predefined allowable range. Since the DFIG can make full use of the reserve wind power in the system frequency regulation, the proposed method can address both the frequency regulation response and the economic performance. Simulation results indicate that the proposed coordinated control scheme can achieve satisfactory frequency regulation response and lead to reduced demand for frequency regulation of SG.

Aiming at the problems of poor anti disturbance and insufficient regulation potential of traditional load frequency control (LFC) methods, model predictive control (MPC) is used to take large-scale electric vehicles (EVs) as controllable loads to participate in power system frequency regulation, so as to eliminate the frequency deviation caused by system delay. Firstly, the system control model of EV participating in primary frequency regulation is established, aggregated and added to the traditional load frequency control model. On this basis, the state-space dynamic frequency model of power system with EV is established. The MPC controller and a delay compensator based on predictive compensation method (PCM) are designed, which makes full use of the prediction and compensation characteristics, and makes dynamic adjustment for different time delays to compensate communication delay and strengthen frequency stability. Finally, simulations are conducted in MATLAB and the results show that compared with the traditional proportional integral control methods, the proposed method has less frequency fluctuation, faster response speed and better robustness.

The frequency control ability and strategy of wind turbine generators in wind farms are different. Decentralized control is difficult to meet the demand of a flexible control of a wind farm, which improves the requirement of coordinated control of wind farms to participate in system frequency regulation. In this paper, a wind farm frequency control method based on the frequency regulation ability of wind turbine generators is proposed, and a hierarchical control system structure is designed to coordinate the wind turbine generators to improve the system frequency according to the system frequency response characteristics. Simulation results show that the proposed method can improve the frequency control effect and effectively reduce the number of wind turbines participating in frequency regulation.

The contribution of electric vehicles (EV) in a microgrid with high intermittent renewable energy penetration could strengthen power system security and reliability. This paper investigates the contribution of EV in regulating and improving primary frequency response of the power system. EV is designed to halt charging and inject power to the grid during an under-frequency event and to consume surplus energy following an over-frequency event when EV state-of-charge (SOC) is available. The minimum SOC threshold is kept to a higher value to ensure EV is available to be driven at any given time. Simulation results demonstrate that EV enhances primary frequency response of the system and also better frequency regulation is observed as the levels of EV penetration increases. In addition, high EV power output during a short period of time indicates its low impact on EV SOC and the need of high power EV charger to reduce the number of EV participation.

Electric vehicles (EVs) have the potential to provide frequency regulation service to an independent system operator (ISO) by changing their real-time charging or discharging power according to an automatic generation control (AGC) signal. Recently, the Federal Energy Regulatory Commission has issued an order to ISOs to introduce a performance-based compensation paradigm. In this new compensation scheme, the ISOs make payments to the EVs for providing the frequency regulation capacity and the real-time dispatch when following the AGC signal. In this paper, we propose a robust EV frequency regulation (REF) algorithm to determine the hourly regulation capacity for each EV considering the randomness of the AGC signal. The proposed REF algorithm is based on a robust optimization problem formulation. It enables EVs to follow the random AGC signal reliably and to improve the EV frequency regulation revenue. Simulation results show that the proposed REF algorithm obtains a higher revenue compared with a benchmark algorithm from the literature under a performance-based compensation paradigm.

The improvement of smart grid interactive technology provides the basis for the development and utilization of demand-side resources, and the auxiliary frequency regulation by demand-side resources becomes development direction. The electric vehicles (EVs) not only meet the requirements of the society for energy and environmental protection, but also have the characteristics of fast response speed, which is suitable for frequency regulation. This paper establishes the power system frequency response model with the coordination of EVs and GFAs. Based on the model, the paper analyzes the frequency regulation performance with different capacity of EVs and GFAs in single area, and calculates the optimized capacity that can satisfy the requirements. Because centralized control has better response effects than decentralized, this paper focuses on centralized controlled EVs in multi area grid frequency regulation. The control logic contains frequency deviation and tie line power deviation, and sets the dead band to ensure centralized controlled EVs to quit in time. The optimal coordinated capacity, proportion and control strategy are analyzed. The simulation results show that the collaboration of EVs and GFAs has a better effect than only insufficient EVs involved in. Also, centralized controlled EVs can participate in restraining power deviation of tie line in multi area interconnected system, which contributes to the frequency recovery after disturbance.

In recent years, there has been a rise in the penetration of renewable energy. However, intermittency of renewable energy has resulted in several problems faced by the power systems such as system frequency fluctuation. In order to suppress the system frequency fluctuation, a battery inside an electric vehicle (EV) is expected to be used as a control device for Load Frequency Control (LFC). This paper proposes a new EV multi-step smart charging scheme with two charging sequences: first come first serve schedule and sequence with the ratio of ASOC to remaining grid connecting time. The total controllable capacity of the EVs is estimated based on the lumped EV model. The result shows the effectiveness of the proposed control by multi-step charging on EV which can suppress the frequency fluctuation. In addition, the customer's convenience is taken into consideration by using the satisfaction index defined as the ratio of charged energy to the required energy.

Cooperative grid frequency control under asymmetric V2G capacity via switched integral reinforcement learning

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities