The aggregation of the remaining battery capacity of EVs can be used as distributed energy storage to participate in electricity market auxiliary services in the form of V2G, however, their reliability is affected by the uncertainty of EV travel. In this paper, we consider the uncertainty of EV load response and propose a resolution method for quantifying the reliability of EV aggregation capacity in the day-ahead market. The paper first classifies EV users by analyzing historical charging data. Secondly, based on the composite Poisson process, the V2G capacity and reliability model for the aggregator’s participation in the day-ahead auxiliary service bidding are proposed and validated using actual data. Based on this model, aggregators can decide the power of V2G bidding for any time slot on day t+1 according to the reliability requirements of the standby market for the standby capacity contracted before that day. Finally, this paper organically combines PV and EV aggregators in the microgrid and uses genetic algorithms to optimize the simulation of the microgrid voltage operation state under different scenarios in the IEEE 33-node system. The results show the effectiveness of the coordinated optimization scheme of PV and EV clusters for the optimization of microgrid voltage.

Large penetration of renewable energy sources into the power grid has increased the complexity of power system operation and greatly reduced the ability of the system to withstand large disturbances. The frequent occurrence of voltage instability problems in the power grid has brought new challenges to the assessment of transient stability analysis of the power system. To achieve fast and accurate assessment of transient voltages, a transfer learning-based transient voltage stability assessment with small samples is proposed, introducing a domain transfer learning approach, embedding a cooperative attention mechanism in the residual network during the feature extraction stage to capture long-range correlations between features, and using adversarial approaches to reduce the differences between samples from different data sets, using the source domain to guide the target domain for network training to improve model's evaluation capability when the number of samples is insufficient, enhance the generalisation performance of the network, and effectively improve the performance of real-time power system transient voltage stability evaluation in the absence of sufficient historical data. Testing on an improved New England 39-node system validates the superiority of this method in transient voltage stability assessment and provides a new approach to practical field transient voltage stability assessment.