The inertia level of microgrids decreases with the access of inverter-based distributed generations. Uncertainty of source loads in islanded microgrids can lead to frequency instability. Considering electric vehicles (EVs) as a typical regulable load, a charging station (CS) virtual synchronous generator (VSG) frequency control (FC) strategy that considers the user charging and grid FC demand is proposed. First, the control architecture of CS based on the VSG is designed. Second, considering the impact of VSG FC on EV user charging demand, the charging power response model considering the charging power FC cost is designed. Furthermore, multiobjective model predictive control algorithm is designed to improve the VSG control strategy, and the FC strategy is obtained. Simulation results show that the proposed method can provide inertial support and frequency regulation capability by the CS with VSG control strategy while satisfying EV charging demand. In addition, the FC cost can be decreased.

The increasing popularity of electric vehicles (EVs) and the enhanced energy storage capability of batteries have made EVs adjustable resources in economic dispatching for power grids. The guidance and control of discharging EVs have become issues with ever‐increasing concerns, and the EVs can be discharged to other entities in the grid, which is called vehicle to everything in the power grid (V2eG) technology. When V2eG technologies become widespread, EVs could be widely distributed, fully controllable, and marketable power sources. As ideal mobile power sources, EVs are geographically available all the time. In this paper, the application process of V2eG technology is first introduced, and the current research status of V2eG technology is discussed. The modelling technology involved in V2eG technology is next analysed and discussed in detail. Then, the applications of V2eG technology in economic dispatch are summarized. Furthermore, research on communication technology for V2eG technology is investigated. In addition, the policies and electricity market access criteria related to V2eG are discussed, and the corresponding trading mechanisms are analysed. Finally, the related V2eG technologies are summarized, and the future development direction of V2eG technology is prospected.

The global installed capacity of renewable energy sources (RESs) is increasing rapidly. The 100% RESs power supply is being pursued. When renewable energy penetration tends to be 100%, the source-load coordination problem becomes extreme. The problems of market failure caused by the near-zero marginal cost of RESs and the scarcity pricing of flexible resources (FRs) participating in the rapid energy deviation balance need to be solved. Accordingly, a spot market framework including the day-ahead renewable energy temporal capacity market and the real-time energy sharing market is proposed in this paper. The RESs and FRs are decoupled to ensure that the homogenous commodities compete together. In the day-ahead stage, to obtain the locational marginal price with the guiding role of resource allocation, the fixed cost of the RESs is apportioned to obtain the spatial-temporal capacity price. In the real-time stage, a price-driven distributed energy sharing mechanism is proposed. It is proven that the sharing mechanism can achieve optimum social welfare and satisfy incentive compatibility. The simulation verifies that the new framework can solve market failure. The real value of FRs is discovered when a price consensus is reached.

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.