To adapt to the “dual carbon" goals and effectively guide the charging of electric vehicles (EVs) while reducing issues such as long queueing times and underutilization of charging stations (CSs) due to unreasonable pricing by charging station operators (CSOs), this paper proposes an optimal spatial-temporal pricing strategy for CSs based on the group price response behavior of EVs. Firstly, this study predicts the spatial-temporal distribution of EV load demand using trip chain and probability theory. Then, the Monte Carlo method is employed to simulate the spatial-temporal distribution of EV load demand and group charging behavior. Subsequently, an EV-CSO two-layer pricing demand response model is established, comprising an upper-layer pricing model for CSOs and a lower-layer charging decision model for the EV group. Finally, the model is solved to obtain the optimal pricing strategy. Additionally, the decision behavior of EVs is simplified through node clustering, and the optimal spacing is obtained through the iterative search algorithm. The results show that compared to traditional pricing strategies, the proposed method improves the total profit of CSs and the average utilization rate of charging piles. Furthermore, the node clustering method significantly improves the computational efficiency of the pricing model, providing theoretical guidance for complex traffic network analysis of large-scale EVs.

With the rapid development of microgrids, the types of users and loads in microgrids are increasing. Comprehensively reflecting the value of loads in load shedding decisions is one of the main challenges for the intelligent control of an islanded microgrid. However, the existing load shedding schemes ignore the user’s subjective preference for multiple attributes of loads, which are insufficient for reflecting the comprehensive value of loads. To address the aforementioned problem, an underfrequency load shedding (UFLS) scheme based on the comprehensive weight of loads is proposed. This scheme evaluates the multiple attributes of loads and the user’s preference for load attributes based on the entropy method with high adaptability (EMHA) and the analytical hierarchy process (AHP) to obtain the objective and subjective weights of loads. Then, the comprehensive weight of loads is calculated based on the relative entropy to determine the sequence of load shedding. Furthermore, the optimal objective function of the comprehensive cost of load shedding is constructed, and gray wolf optimization (GWO) is used to solve the objective function and obtain the optimal load shedding scheme. Ultimately, the effectiveness of the proposed scheme is verified based on a modified standard IEEE 37-bus system.

The rapid drop of frequency under the disturbance is a major threat to the safe and stable operation of a microgrid (MG) system. Emergency load shedding is the main measure to prevent continuous frequency drop and power outage. The existing load shedding strategies have poor adaptability to deal with the problem of MG load shedding under different disturbance situations, and it is difficult to ensure the safe and stable operation of an MG in different operating environments. To address this problem, this paper proposes a data-driven load shedding strategy. First, considering the importance of the load and the frequency recovery time of the system, a load shedding contribution indicator is designed that takes into account the load frequency adjustment effect and the load shedding priority. This contribution indicator is introduced as a load shedding criterion into the reward value function of dueling deep Q learning. Second, considering the suddenness and uncertainty of emergency load shedding, a MG emergency load shedding strategy (ELSS) based on dueling deep Q-learning is proposed. On this basis, the dueling deep Q learning algorithm is used to obtain the load shedding decision with the maximum cumulative reward. Finally, taking the MG load shedding cases in two different scenarios as examples, a simulation study is carried out on a modified IEEE-25 bus MG. The simulation results show that, compared with the model-driven implicit enumeration strategy (IES), the proposed ELSS has superiority in maintaining stable power supply for important loads and reducing load shedding decision-making time and frequency fluctuations.

Distribution network failures can cause unintentional islanding of three-/single-phase multimicrogrids (MMGs). The transient impulse that occurs during the unintentional islanding period can affect the stability of the voltage and frequency in an MMG. To address this problem, this article proposes an unintentional islanding transition control strategy based on artificial emotional reinforcement learning (AERL) for a three-/single-phase MMG. First, to solve the three-phase unbalance problem that occurs during the unintentional islanding period, a three-phase combination method based on a merge sort is proposed to realize the combination optimization of the single-phase source-load-storage. Second, a load-shedding strategy based on AERL is proposed to deal with the tie-line power shortage caused by the distribution network failures. This strategy can quickly eliminate power shortages and ensure the uninterrupted power supply of critical loads. Finally, the performance of the proposed transition strategy is verified in a three-/single-phase MMG model based on a modified IEEE 37-bus system and a modified IEEE 118-bus system. During the unintentional islanding period, the proposed transition strategy reduces the frequency recovery time by 21.74%, 14.29%, and 10%, the voltage recovery time by 16.22%, 13.89%, and 8.82% compared with the mixed-integer second-order cone programming method (MISOCPM), the implicit enumeration method (IEM), and the compound storage-regulating and load-shedding method (CSLM) in the modified IEEE 37-bus system, respectively; the proposed transition strategy reduces the frequency recovery time by 25.64%, 21.62%, and 12.12%, the voltage recovery time by 19.15%, 15.56%, and 7.32% compared with the MISOCPM, IEM, and CSLM in the modified IEEE 118-bus system, respectively. The test results show that the proposed transition control strategy realizes the seamless transition of a three-/single-phase MMG and ensures the uninterrupted power supply of critical loads.