Threatened by weather disasters and operational uncertainties, power systems require resilient and cost-effective decision making to ensure security. This article proposes a novel deep reinforcement learning algorithm, namely defender–attacker soft actor-critic (DA-SAC), designed for contingency-constrained optimal power flow underN–ksecurity criteria. A two-agent Markov decision process is formulated, where the defender learns robust control actions and the attacker identifies worst-case contingencies. The core soft actor-critic algorithm is enhanced by integrating constraint violation levels into the reward function and employing a two-timescale learning scheme to improve feasibility and stability. The proposed method is validated on the IEEE 30-bus and 118-bus systems. Simulation results show that DA-SAC significantly reduces unserved energy, load shedding, and constraint violations, outperforming conventional and deep-reinforcement-learning-based benchmarks underN–1,N–2, andN–3scenarios. These results demonstrate that DA-SAC offers a fast, resilient, and practical solution for real-time power system operation under severe contingencies.

The rapid development of electric vehicles raises a higher requirement for charging station operation and management. Therefore, this work proposes a data-driven method aimed at enhancing the economical operation of charging stations. Considering the privacy of charging data and the influence of traffic flow, this data-driven method simulates the spatiotemporal charging demand based on the predicted traffic flow. To obtain precise prediction, this work develops an efficient gated-attention spatiotemporal convolutional network (GSTCN) to explore the long-term spatial and temporal dependence of traffic flow. GSTCN is constructed by two main components: a spatial gated attention (SGA) unit and a temporal gated (T-Gated) attention layer. The spatial pattern of traffic flow is unearthed by the well-designed SGA unit, while the temporal correlation between different time steps is captured through the proposed T-Gated attention layer. Then, an energy storage system (ESS) is employed to improve the effective management of charging stations. Numerical results demonstrate the efficiency of GSTCN in charging station management. GSTCN can produce more accurate prediction data, which leads to a more economical operation of the energy storage system compared with the benchmarks.

Constructing the prediction intervals (PIs) for electric vehicle (EV) charging demand based on traffic flow information is crucial for the efficient operation of EV charging stations. However, due to the volatile nature of traffic flow, obtaining long-term traffic flow information (e.g., one week in advance) is challenging, particularly for multiple neighbored sites. To address this issue, this work establishes a deep learning prediction framework called Spatiotemporal Periodic Network (STPNet), which utilizes an encoder-decoder architecture. The STPNet incorporates a spatiotemporal and periodic pattern learning technique, and leverages the advantages of convolutional long short-term memory units (ConvLSTM) to quantify the uncertainty of traffic flow. Furthermore, to improve the performance of traffic flow prediction, a spatiotemporal series decomposition strategy based on Seasonal and Trend decomposition using Loess (STL) is employed, and a spatiotemporal PI performance-based loss function is creatively developed in this work. Then, the PIs of the EV charging demand are obtained based on the predicted traffic flow information and an M/M/C/K queuing model. Validated using a real-world dataset, the proposed model has been demonstrated to exhibit effectiveness in generating high-quality EV charging demand PIs for multiple locations.

Promotion of charging facilities (CFs) can ameliorate range anxiety and facilitate long-distance travel by electric vehicles (EVs). In this context, a multistage low-carbon EV CFs planning model is proposed in this paper for the coupled transportation and power systems. This model not only takes into account the construction costs of newly-built CFs and their adverse impacts on the distribution system, but also considers the carbon emissions of CFs and the penalty due to the inconvenience for EVs to be recharged. In addition, a rational travel route choice model is significantly important to accurately evaluate the service capability of CFs to be constructed and thereby to obtain an optimal CF planning result. Therefore, a novel travel route choice model developed in this work allows EV drivers to take detours according to the CF locations to charge their EVs multiple times and then finish their trips. Carbon emission flow (CEF) model is innovatively employed to precisely calculate CFs’ carbon emission amount from the perspective of consumption side. Subsequently, uncertainties involved in CF planning, i.e., distribution and growth rate of traffic flow and electric load, popularity of different types of EVs, as well as location of the connected node of clean electricity, are fully considered to obtain a robust CF planning scheme that can achieve a good performance in the current stage and exhibit robustness for the uncertainties in the future stage. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model. The impacts of future carbon price and EV cruising range on the planning results are also comprehensively evaluated.

The optimal frequency control of autonomous microgrids (MGs), i.e., to achieve fast frequency recovery and dynamic power adjustment of the distributed generators in proportion to predefined participation factors, can be achieved in a fully distributed way based on the subgradient consensus protocol. However, such a distributively controlled MG is susceptible to different types of cyber attacks infiltrated from different locations. In this article, a robust and resilient distributed optimal frequency control scheme is proposed to address the threat of cyber attacks. It is facilitated by introducing an auxiliary networked system interconnecting with the original cooperative control system. On condition that the cyber attacks are within certain ranges, the robust design can maintain the functionalities by significantly attenuating the impact. Otherwise, the cyber attacks can be easily detected, and resilient reactions can be taken to mitigate their influences via isolation. Simulation results in a modified IEEE 34-bus MG validate the effectiveness of the proposed approach.

The increasing penetration of distributed energy resources and a large volume of unprecedented data from smart metering infrastructure can help consumers transit to an active role in the smart grid. In this article, we propose a human-machine reinforcement learning (RL) framework in the smart grid context to formulate an energy management strategy for electric vehicles and thermostatically controlled loads aggregators. The proposed model-free method accelerates the decision-making speed by substituting the conventional optimization process, and it is more capable of coping with the diverse system environment via online learning. The human intervention is coordinated with machine learning to: 1) prevent the huge loss during the learning process; 2) realize emergency control; and 3) find preferable control policy. The performance of the proposed human-machine RL framework is verified in case studies. It can be concluded that our proposed method performs better than the conventional deep Q-learning and deep deterministic policy gradient in terms of convergence capability and preferable result exploration. Besides, the proposed method can better deal with emergent events, such as a sudden drop of photovoltaic (PV) output. Compared with the conventional model-based method, there are slight deviations between our method and the optimal solution, but the decision-making time is significantly reduced.

This work proposes a novel uncertainty quantification framework for long-term traffic flow prediction (TFP) based on a sequential deep learning model. Quantifying the uncertainty of TFP is crucial for intelligent transportation system (ITS) to make robust traffic congestion analysis and efficient traffic management due to the inherent uncertain and fluctuating nature of traffic flow. However, the performance (e.g., reliability and sharpness) of uncertainty quantification is hard to guarantee, particularly for long-term traffic flow (e.g., one week or two weeks in advance). To this end, this work develops a nonparametric performance-oriented prediction interval (PI) construction approach based on an enhanced sequential convolutional long short-term memory units (ConvLSTM) model, which is named as PI-ConvLSTM. This model can well learn the temporal correlations involved in the multivariate explanatory samples. Specifically, a periodic pattern learning strategy and a performance-oriented loss function are developed to ensure the quality of the derived PIs. Through validating on the real-life England freeway traffic flow dataset, the proposed PI-ConvLSTM proves to be capable of producing the skillful PIs for long-term TFP. For instance, the performance of derived PIs for two-week ahead is 0.175%, 0.198 and 1957.127 in average in terms of reliability, average width and sharpness, respectively. As compared to the benchmark models the proposed model shows at least 68.1% improvement on reliability, 3.4% on average width and 1.7% on sharpness.

With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based convolutional neural network (CNN), and different forecast uncertainties are evaluated to formulate the TF prediction intervals (PIs). Then, the EV arrival rates are calculated according to the historical data and the proposed mixture model. Based on TF forecasting and arrival rate results, the EV charging process is studied to convert the TF to the charging load using a novel probabilistic queuing model that takes into consideration charging service limitations and driver behaviors. The proposed models are assessed using the actual TF data, and the results show that the uncertainties of the EV charging load can be learned comprehensively, indicating significant potential for practical applications.

The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.