A vehicular energy network (VEN) enables energy transfer by leveraging electric vehicles as mobile carriers through wireless exchange across large geographic areas. This article presents a scalable and robust framework for energy routing in stochastic VENs with the objective of minimizing transmission loss. The problem is formulated as a graph generalized flow optimization, solvable to global optimality via linear programming. To ensure scalability, a flow-guided graph reduction method is proposed, which preserves critical supply-demand connectivity by prioritizing high-impact routes based on vehicular flow patterns. Building upon this, a route-guided time-expanded graph construction strategy is developed to avoid exhaustive temporal replication by generating only time-relevant nodes and arcs along active routes. To address long-horizon stochasticity, a long short-term memory-based model predictive control framework is designed, which captures both randomness and uncertainty via data-driven forecasting and residual-aware robust correction under a rolling-horizon decomposition. The proposed methods are validated on 100 real-world U.S. datasets, demonstrating significant gains in computational efficiency, scalability, and solution robustness across both time-invariant and time-varying VENs.

The dynamic wireless charging (DWC) system based on wireless charging lanes (WCLs) is an important component of smart cities, allowing electric vehicles (EVs) to charge while moving. It is necessary to establish a user-oriented real-time DWC navigation system to achieve the joint optimization of EV routing and charging. However, the modeling characteristics of DWC and the risk preferences of EV owners towards congested WCLs are completely different from those in traditional wired charging. Furthermore, optimal EV charging navigation is always challenging without prior knowledge of uncertainty in electricity prices and traffic conditions. This paper first proposes a novel dynamic charging routing model for individual EVs to minimize travel and charging costs, and reformulates it as a twostep optimization problem to facilitate feature extraction. Then, mirror-symmetrical Dijkstras algorithm (MSDA) is proposed to solve the reformulated model in linear time and extract advanced features from the stochastic information. By feeding the system state containing extracted features into the deep Q network (DQN) in an event-triggered manner, the near-optimal charging navigation strategy is finally obtained. The proposed MSDADQN approach not only efficiently extracts low-dimensional interpretable input features, but also adaptively learns the unknown dynamics of system uncertainty. Numerical results based on simulated and real-world data validate the proposed approach.

Electric vehicle energy network (EVEN) enables the transmission of renewable energy from rural to urban area by the flexibility of EVs via energy exchange. In this paper, (dis)charging behavior modelling and bidirectional charging station (BCS) deployment optimization are addressed, since they are crucial in EVEN for EV accommodation, renewable energy utilization, drivers’ profitability estimation, operators’ cost assessment, and financial policy establishment. A novel stochastic Markov (dis)charging behavior model is proposed to calculate the spatiotemporal load pattern considering the realistic factors such as personal features, state of charge (SoC), electricity price, and BCS locations. Unlike most works ignoring energy trading, six scenarios are explored: (S1) no trade; (S2) trade in main battery. (S3) trade in extra battery. (S4) trade in extra ultracapacitor; (S5) trade in both main and extra battery; (S6) trade in both main battery and ultracapacitor. Also, a multi-objective BCS deployment strategy is newly designed, aiming at minimizing installation cost and driver’s electricity bill, while quality of service (QoS) and voltage stability are ensured. An improved hybrid algorithm is developed, which combines hill climbing for enhanced exploitation and particle swarm optimization for better evolvement based on genetic algorithm framework. The simulation validates the fitting ability of charging model, the effectiveness of parameter selection algorithm and the deployment approach. Comparing 6 scenarios, benefits of energy trading in EVEN is confirmed and the superiority of ultracapacitor for trading is demonstrated. The feasibility of financial policies is also studied, and certain guidance is provided for drivers to improve their cost.

In modern Internet of electric energy, i.e., networked power systems, data-driven schemes based on advanced machine learning methods have shown high potential in system emergency stability control, e.g., undervoltage load shedding (UVLS) against the short-term voltage stability (SVS) problem. However, how to efficiently and adaptively select the most effective UVLS sites for online SVS enhancement is still a challenging task. Faced with this issue, this paper develops an intelligent short-term voltage trajectory sensitivity index (VTSI) prediction scheme for adaptive UVLS site selection. Specifically, the scheme is realized by designing a powerful structure-aware recurrent learning machine (SRLM), which systematically combines the emerging graph convolutional network (GCN) with the recurrent long short-term memory algorithm. By doing so, the SRLM is not only fully aware of the non-Euclidean structure of the power grid, but also capable of amply capturing temporal features during SVS dynamics. Consequently, it manages to implement efficient and precise VTSI prediction, thereby reliably identifying critical UVLS sites in various scenarios. Numerical case studies on the Nordic test system illustrate the efficacy of the proposed scheme.

Weather-sensitive resources are the main source of uncertainties in power systems. However, the unpredictable climate change further introduces ambiguity into the system, since the weather-sensitive resources would evolve with the climate and gradually exhibit a different probability distribution from the past in an uncertain manner. Lack of considering this climate-induced ambiguity in transmission network expansion planning (TNEP) may cause misunderstanding of future operational scenarios. Aiming at a higher security operation level under climate change yet less line investment, this paper proposes a climate-adaptive TNEP, which is essentially a robust TNEP equipped with a climate-adaptive uncertainty set (CUS). Determination of the CUS involves three steps. First, model future unknown distribution under climate change. Specifically, the climate-driven evolution in distributions is quantified by an evolutionary distance between historical and future true distributions, whose upper bound is derived from practical data, while the future unknown distribution is then modeled by a distance-based ambiguity set; Second, determine the CUS which has a minimal volume yet a desired confidence level in the face of the ambiguous future distribution. To that end, a parametric Wasserstein distance-based distributionally robust optimization (p-WDRO) is developed over the ambiguity set; Third, solve thep-WDRO by a data-clustering-incorporated reformulation. After the CUS is determined, the overall climate-adaptive TNEP is solved by a column-and-constraint-generation method with an inner multi-loop algorithm tailored for the CUS. Simulations are conducted on three test systems with practical data, which demonstrate that the climate-adaptive TNEP can improve operational security under climate change while reducing investment costs.

In this article, we study the joint energy and reserve sharing problem considering renewable generation uncertainty and limited communication resources. We propose a data-driven distributionally robust energy and reserve sharing model among different agents in electricity markets. We put forward data-driven distributionally robust chance constraints (DRCC) to determine the reserve capacity, which cannot be directly solved. The inner approximation is employed to convert the DRCC into tractable linear constraints. Taking into account the agents in the Internet of Things exchange information by a resource-limited communication network, we develop a communication-censored consensus alternating direction method of multipliers (ADMMs) to utilize the limited communication resources and solve the sharing problem in a fully decentralized manner. We analyze the convergence of the proposed algorithm, and propose an adaptive penalty parameter method to speed up the convergence. Extensive simulations are conducted to verify the effectiveness of the proposed model and theoretical results.

Data integrity of power system states is critical to modern power grid operation and control due to communication latency, state measurements are not immediately available at the control center, rendering slow responses of time-sensitive applications. In this paper, a new graph-based deep learning approach is proposed to recover and predict the states ahead of time utilizing the power network topology and existing measurements. A graph-convolutional recurrent adversarial network is devised to process available information and extract graphical and temporal data correlations. This approach overcomes drawbacks of the existing synchrophasor recovery and prediction implementation to improve the overall system performance. Additionally, the approach offers an adaptive data processing method to handle power grids of various sizes. Case studies demonstrate the outstanding recovery and prediction accuracy of the proposed approach, and investigations are conducted to illustrate its robustness against bad communication conditions, measurement noise, and system topology changes.

This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance the utilization ratio for CSs, and also flatten the unevenness of charging request for power grid.

State estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.

Air pollution problems are attracting increasing attention, especially among the developing countries with frequent haze events. Renewable energy sources such as wind power are expected to help relieve such environmental concerns. However, air pollution issues under such a changing energy structure receive inadequate attention. Mostly, constraints for total pollutant emissions are considered in unit commitment (UC) and economic dispatch problems. In this paper, we propose a UC model with wind power that considers the dispersion of air pollutants. The dispersion process is described by models involving meteorological conditions and the system's geographical distribution, to estimate the spatial distribution of air pollutants, i.e., the concentration of ground-level air pollutants at monitored locations, such as load centers. A penalty cost is introduced based on this estimation. Particulate matter 2.5 μm or less in diameter, the major air pollutant concerning most developing countries, is selected as the focus of this paper. To properly estimate and sufficiently utilize the benefits of wind power for air pollutant dispersion control, robust optimization is applied to accommodate wind power uncertainty. Case studies justify this consideration of air pollutant dispersion, and demonstrate the effectiveness of the proposed model for improving load centers' air pollution control and utilizing wind power benefits.

In this paper, an improved multiperiod risk-limiting dispatch (IMRLD) is proposed as an operational method in power systems with high percentage renewables integration. The basic risk-limiting dispatch (BRLD) is chosen as an operational paradigm to address the uncertainty of renewables in this paper due to its three good features. In this paper, the BRLD is extended to the IMRLD so that it satisfies the fundamental operational requirements in the power industry. In order to solve the IMRLD problem, the convexity of the IMRLD is verified. A theorem is stated and proved to transform the IMRLD into a piece-wise linear optimization problem that can be efficiently solved. In addition, the locational marginal price of the IMRLD is derived to analyze the effect of renewables integration on the marginal operational cost. Finally, two numerical tests are conducted to validate the IMRLD.