To realize the win-win benefits and resource coordination of the multilevel operating entities of a “microgrid cluster (MGC), microgrid (MG) and user” and improve the self-consumption of new energy in the MGC, this paper proposes an energy trading model and solution algorithm of an “MGC, MG and user” based on a multi-agent-system, incentive demand response, and hierarchical Stackelberg game theory. By analyzing the game objectives and strategies of these participants, the unique Stackelberg equilibrium (SE) of the hierarchical Stackelberg game is proved theoretically. The game optimization process is divided into two levels. In the upper-level game, the MGC as a leader stimulates the MG to participate in intracluster dispatching by establishing an internal price incentive mechanism. As the follower, the MG determines the number of electricity transactions based on the realized internal price to maximize its own profits. In the lower-level game, the MG leads the game by deciding electricity selling prices based on the load demands of users, and the user as follower adjust electricity consumption using strategies to balance expenditure and experience of electricity usage. Simulation results verified the effectiveness and good convergence of the proposed method and demonstrated that the proposed hierarchical game strategy can improve the economic benefits of each participant, which is conducive to the establishment of friendly grid-connected MGC.

Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs’ charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method, and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner’s subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra’s shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability.

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.

In order to accurately evaluate power system stability in a timely manner after faults, and further improve the feature extraction ability of the model, this paper presents an improved transient stability assessment (TSA) method of CNN + GRU. This comprises a convolutional neural network (CNN) and gated recurrent unit (GRU). CNN has the feature extraction capability for a micro short-term time sequence, while GRU can extract characteristics contained in a macro long-term time sequence. The two are integrated to comprehensively extract the high-order features that are contained in a transient process. To overcome the difficulty of sample misclassification, a multiple parallel (MP) CNN + GRU, with multiple CNN + GRU connected in parallel, is created. Additionally, an improved focal loss (FL) function which can implement self-adaptive adjustment according to the neural network training is introduced to guide model training. Finally, the proposed methods are verified on the IEEE 39 and 145-bus systems. The simulation results indicate that the proposed methods have better TSA performance than other existing methods.

With the continuous development of microgrids (MGs), a hybrid structure consisting of single-phase MGs and three-phase MGs has been widely promoted. When each submicrogrid (SMG) pursues its economic benefit, it can easily cause a three-phase power unbalance for a grid-connected three/single-phase hybrid multimicrogrid (MMG). To address this problem, an optimization model based on the multileader and multifollower game theory is proposed for a grid-connected three/single-phase hybrid MMG in this paper. In the proposed optimization model, the three-phase unbalance constraints are integrated into the utility functions of the leaders and followers to reduce the impact of the unbalance constraints on the benefits of the participants. The performance of the proposed optimization model is verified by a series of tests. The test results show that the proposed optimization model can effectively reduce the three-phase power unbalance factor of the system and improve the profit of the three/single-phase hybrid MMG.