As photovoltaic (PV) generation has been one of the major renewable energy sources around the world, its PV capacity has also increased. When the large-scale PV systems are integrated into the distribution network, the complexity of the assessment process of the distribution network reliability will increase hazardously. In order to accurately assess this reliability in the distribution network combined with the PV generation, a reliability assessment procedure is proposed. In order to accurately evaluate the impact of the failure of conventional power equipment on reliability, the time-varying failure rate of conventional power equipment is modeled, taking into account the aging period. Then, in order to accurately evaluate the reliability improvement with PV systems integration, the new procedure is proposed highlighting the following contributions: 1) five new indices are added. 2) the PV output is modeled so that not only the radiation intensity but also the failure and degradation of PV modules are represented. 3) the time-varying islanding operation is considered and integrated. A case study using real-life distribution network topology and data in China is applied to verify that the newly proposed reliability indices display more sensitivity, and the proposed procedure significantly improves the accuracy of the reliability assessment.

This paper proposes a multi-agent deep graph reinforcement learning-based EV on-route charging guidance strategy, aiming at minimizing the charging cost for EV drivers in an uncertain and complex environment. First, a real-time online EV charging guidance framework with bi-timescales coupled transportation electrification is proposed. On the slow timescale, the distribution locational marginal pricing of the node where the charging station is located is solved by the power purchase cost optimization of the power distribution network. On the fast timescale, multi-agent deep reinforcement learning is used to solve real-time EV requests. Second, charging stations are considered as agents, and the potential competition for future charging demand at charging stations is considered. A multi-agent actor critic algorithm with embedded graph attention networks is proposed to optimize charging decision-making of EV drivers, which exploits the interactions between agents' observations using graph attention networks. Case studies are carried out in a practical area of Xi'an, China. We analyze the necessity of model components. The effectiveness of the proposed approach in reducing charging cost and its applicability in PV-equipped scenarios is verified. The convergence performance and scalability are verified by the comparison with SAC and MADDPG algorithms.

This article proposes the operating status prediction model at electric vehicle (EV) charging stations based on the spatiotemporal graph convolutional network (SGCN). The SGCN combines graph convolutional network (GCN) and the gated recurrent unit (GRU), alleviating the queuing time at charging stations due to the lack of information for EV users. First, an urban charging station-traffic flow model is established to portray the interrelationship between charging stations and traffic. Second, a multistep prediction model based on SGCN for operating status at charging stations is proposed to forecast the occupancy of charging stations over the next tens of minutes. The comparison case study with the forecast and actual data reveals that the mean forecast error is around 19.21% when estimating 18 min ahead. Incrementing errors are subtle even after adding random noise to the original data. Finally, the model is applied to charging guidance decisions. Our model can reduce the number of EV queuing by 60% during high charging demand. It also shortens the average charging waiting time by 4 min.

As a new energy vehicle, fuel cell vehicles (FCVs) have the advantages of pollution-free and zero-emission, which have attracted wide attention from all over the world. By predicting the ownership of FCVs, decision-makers can grasp the future development potential of FCVs, so that the national government can adopt corresponding policies and measures. To this end, based on analyzing the development trend of FCVs, this article proposes an FCVs ownership prediction method based on the generalized Bass model. From the perspective of technology, application, and market conditions of FCVs, this article first analyzes the development trend of FCVs in the future, which provides the basis for the establishment of the ownership prediction method and the estimation of model parameters. Then, considering the influence of levelized cost of driving (LCD) reduction and hydrogen refueling stations (HRSs) constructions, a prediction method of FCVs ownership based on the generalized Bass model is proposed, and the model parameters are estimated by Grey Verhulst model, electric vehicles data, and liquefied natural gas vehicles data. Through the proposed method, the ownership of FCVs in China from 2021 to 2070 is predicted and the results show that the ownership of FCVs in China will reach about 1.43 million in 2030 and about 10.41 million in 2040. Finally, through sensitivity analysis, this article discusses the influence of external factors such as policy, LCD, and HRSs on the market diffusion of FCVs, and puts forward corresponding suggestions.

Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi‐objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second‐order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air‐conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak‐valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN.

EV drivers have experienced a charging inconvenience due to a limited number of charging facilities and mileage anxiety due to the limited driving distance for a single full charge. This paper developed a user-friendly online EV charging guidance algorithm to cope with the two aforementioned issues using multi-agent deep reinforcement learning. First, three models, i.e., the traffic network model, charging station model, and EV driver model, are established, respectively, considering the traffic condition, the potential competition of future charging demand at charging stations, and the drivers’ mileage anxiety. Second, the charging guidance process is modeled as a Markov decision process, and charging stations are taken as agents. The attentional multi-agent actor-critic algorithm based on the centralized training with decentralized execution framework is built. Finally, compared to the comparison algorithm, the performance does not diminish with the increase in the number of agents, indicating that the approach has the scalability to be applied to large-scale agent systems. The model still has the generalization in extreme scenarios such as traffic road and charger failures. The testing time within various numbers of charging stations is about 23ms per EV, which is sufficient to apply the proposed model to real-time decision-making and online recommendation.