In recent years, the access of various distributed power sources and electric vehicles (EVs) has brought more and more randomness and uncertainty to the operation and regulation of microgrids. Therefore, an optimal scheduling strategy for microgrids with EVs based on Deep Q-learning is proposed in this paper. Firstly, a vehicle-to-grid (V2G) model considering the mobility of EVs and the randomness of user charging behavior is proposed. The charging time distribution model, charging demand model, state-of-charge (SOC) dynamic model and the model of travel location are comprehensively established, thereby realizing the construction of the mathematical model of the microgrid with EVs: it can obtain the charging/discharging situation in the EV station, so as to obtain the overall output power of the EV station. Secondly, based on Deep Q-learning, the state space and action space are set up according to the actual microgrid system, and the design of the optimal scheduling reward function is completed with the goal of economy. Finally, the calculation example results show that compared with the traditional optimization algorithm, the strategy proposed in this paper has the ability of online learning and can cope with the randomness of renewable resources better. Meanwhile, the agent with experience replay ability can be trained to complete the evolution process, so as to adapt to the nonlinear influence caused by the mobility of EVs and the periodicity of user behavior, which is feasible and superior in the field of optimal scheduling of microgrids with renewable resources and EVs.

The inertia level of power systems decreases with the access of inverter-based generation. Considering electric vehicles (EVs) as a typical controllable load, a consistent collaboration control strategy based on adaptive virtual inertia in multiple charging stations (CSs) is proposed. First, the relationship between system frequency and inertia is analyzed, and virtual inertia control strategies and CS distributed network modeling methods are introduced. Second, considering the frequency recovery process and the collaboration among CSs, an adaptive virtual inertia control strategy is improved to meet the demands of the application scenarios of EV and V2G, as well as a simulation method for CS inertia supporting power. Furthermore, a hierarchical collaborative control strategy for multiple CS frequency regulation is proposed considering the leader-follower consistency algorithm as a secondary control. Simulation and experiment results show that the proposed strategy can provide extra inertia, mitigate frequency deterioration and suppress frequency oscillations, as well as reduce the frequency regulation cost of CSs. This method can provide guidance for the aggregated regulation of demand response at distributed CSs and EVs.

Frequency and voltage deviation are important standards for measuring energy indicators. It is important for microgrids to maintain the stability of voltage and frequency (VF). Aiming at the VF regulation of microgrid caused by wind disturbance and load fluctuation, a comprehensive VF control strategy for an islanded microgrid with electric vehicles (EVs) based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper. First of all, the SOC constraints of EVs are added to construct a cluster-EV charging model, by considering the randomness of users’ travel demand and charging behavior. In addition, a four-quadrant two-way charger capacity model is introduced to build a microgrid VF control model including load, micro gas turbine (MT), EVs, and their random power increment constraints. Secondly, according to the two control goals of microgrid frequency and voltage, the structure of DDPG controller is designed. Then, the definition of space, the design of global and local reward functions, and the selection of optimal hyperparameters are completed. Finally, different scenarios are set up in an islanded microgrid with EVs, and the simulation results are compared with traditional PI control and R(λ) control. The simulation results show that the proposed DDPG controller can quickly and efficiently suppress the VF fluctuations caused by wind disturbance and load fluctuations at the same time.

System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally.

To investigate the feasibility and differences between the grid-forming (GFM) and grid-following (GFL) control strategies for the frequency response (FR) in dc charging stations (CSs), this article first identifies the characteristics of the virtual inertia of GFM and GFL, and establishes an analytical model of the system FR considering FR in CSs. Second, the virtual inertia response control model of CSs is proposed under GFM and GFL controls, respectively. And their FR characteristics are analyzed. Finally, the FR characteristics of CS FR under variations of time delay, FR capacity, and virtual inertia are analyzed in the improved IEEE three-machine nine-node and ten-machine 39-node benchmarks. The results show that the FR of the GFM CS is less affected by time delay and can affect the grid frequency in the form of inertia when the amount of electric vehicles (EVs) required in the CS groups is about 300–400. Whereas the overcapacity of the GFL CS will cause the system frequency to oscillate. Therefore, the choice of virtual inertia control strategy adopted by the CS is closely related to the number of EVs in the station and its adjustable margin.