The high penetration of renewable energy and frequent extreme weather events bring significant uncertainty to the operation and planning of power systems. The extreme scenarios exhibit high impact and low probability characteristics, posing a challenge in providing sufficient and reliable samples for power system decision-making. The existing scenario generation methods struggle to balance the generation efficiency and interpretability. The causality between meteorological factors and power system operation has not provided effective guidance for the extreme scenario generation. In this paper, firstly, the mechanism analysis and sample construction of extreme scenarios were achieved through meteorological factor extraction, meteorological-scenario causality test and scenario clustering. Secondly, an enhanced GAN model considering training stability is established. The physical constraints of power systems are used to ensure the effectiveness of generated scenarios. Finally, a statistical analysis considering the individuals and the whole samples is conducted. The accuracy evaluation method of the generated scenarios is proposed. The robustness of the model is validated based on accuracy. The results indicate that the generated scenario have statistical consistency and high accuracy.

The advantages and disadvantages of Battery Swapping Stations (BSS) for heavy-duty trucks are poorly understood, relative to Fast Charging Stations (FCS) systems. This study evaluates these two charging mechanisms for electric heavyduty trucks, aiming to compare the systems efficiency and identify their optimal design. A model was developed to address the planning and operation of BSS in a charging network, considering in-station batteries as assets for various services. We assess performance metrics including transportation efficiency and battery utilization efficiency. Our evaluation reveals that BSS significantly increased transportation efficiency by reducing vehicle downtime compared to fast charging, but may require more batteries. BSS with medium-sized batteries offers improved transportation efficiency in terms of time and labor. FCS-reliant trucks require larger batteries to compensate for extended charging times. To understand the trade-off between these two metrics, a cost-benefit analysis was performed under different scenarios involving potential shifts in battery prices and labor costs. Additionally, BSS shows potential for significant CO2 emission reductions and increased profitability through energy arbitrage and grid ancillary services. These findings emphasize the importance of integrating BSS into future electric truck charging networks and adopting carbon-aware operational frameworks.

Grid-forming converter faults in islanded hybrid AC/DC microgrid clusters could lead to voltage and frequency in-stability risk in the subgrid, thus threatening system operating safety. To solve this issue, this paper has proposed an uninter-ruptable hierarchical distributed coordinated control scheme of is-landed hybrid AC/DC microgrid clusters. Firstly, when grid-form-ing converter faults occur in a certain AC microgrid (ACMG), the interlinking converter connected with the faulty subgrid switches its control mode into grid-forming, providing stable voltage and frequency support for the faulty ACMG. Then, a two-layer coor-dinated control scheme is presented, where the first layer is de-signed for the remaining interlinking converters, and the second layer is designed for converters in normal subgrids and grid-fol-lowing converters in abnormal ACMG together with its interlink-ing converter. By the control mode switch of the IC and the two-layer coordinated control scheme, accurate power sharing can quickly fill the power deficit in the faulty subgrid, and the voltage and frequency of the microgrid clusters can remain stable. Verifi-cation results indicate that with the proposed scheme, the islanded AC/DC microgrid clusters can operate uninterruptedly under dif-ferent kinds of grid-forming converter faults.

This paper proposes SecureDec, a decentralized and secure computational pipeline for the day-ahead scheduling of electricity-gas coupled systems. At the lower level, we propose SecureADMM for the decentralized optimization of the coupled subsystems, where all exchanged variables in decentralized ADMM are properly secured with ex-ante Diffie-Hellman (D-H) keys. The generation of D-H keys, being the most time-consuming step in encryption, can be independently performed ahead of optimization, allowing subsystems to collaboratively update variables without compromising optimization efficiency. As the convergence of ADMM is contingent on the quality of starting points, at the upper level, we introduce multi-entity SecureBoost, a decentralized federated learning framework, to predict the warm starts of SecureADMM in a similarly decentralized and privacy-preserving manner. A two-stage statistical packing algorithm is incorporated in the preceding SecureBoost training process to improve the efficiency in data protection, which reduces the frequency of time-consuming encryption/decryption by 2–6 times. Case studies demonstrate that the convergence of SecureDec is up by 50.0% higher than the conventional ADMM due to hyper-accurate warm starts provided by SecureBoost. Further, 63.9% and 96.7% efficiency boosts are observed in the federated learning and day-ahead optimization of an electricity-gas coupled system, respectively, with no compromises in information privacy and scheduling quality. The pipeline is further scalable to larger systems, and demonstrates potentials in addressing non-convergence in the conventional ADMM.

The adoption of renewable energy sources (RES) is essential for accelerating the low-carbon transition. Accurate forecasting of renewable energy generation is vital for its effective integration into power systems, allowing for mitigation on variability and intermittency of RES and facilitating economic and secure grid operation. In this context, collaborative wind generation forecasting task faces multiple challenges, including secure information sharing, spatial-temporal dependencies characterization and utilization, as well as uncertainty quantification via probabilistic forecasting. To this end, this paper proposes a novel collaborative forecasting framework which features 4 core functionalities: a temporal convolutional network-autoencoder based secure data encryption and temporal dependencies extraction module; a self-attention based secure data sharing and selective spatial-temporal dependencies extraction module; a privacy-preserving collaborative point/probabilistic forecasting module based on improved pinball loss function; and an adaptive fine-tuning procedure to further leverage feature extraction performance and prediction accuracy through joint training. Case studies using real-world data from 10 wind farms located across Jiangsu province, China, validated superior performance of the proposed method while highlighting the value of the developed functionalities. The proposed collaborative forecasting method achieves 18.21%, 15.23%, 8.69% improvement in RMSE compared to the worst-performing baseline method, in benchmarking against individual forecasting, pre-training only and statistical pre-screening based methods. Furthermore, a maximum 26.62% improvement in Coverage Width-based Criterion is attained with respect to parametric and non-parametric baseline methods.

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies

The emergence of load aggregators (LAs) has significantly elevated the trading position of residential users in demand response (DR). Effective incentive designs can greatly enhance the responsiveness of residential users. However, a key but often overlooked issue in practical implementation is strengthening the connection between pricing and actual responses to ensure the executability of DR scheduling results. To address this issue, this paper presents a competitive incentive mechanism for multi-agents in DR through a hierarchical game that considers joint uncertainties, and examines the rationality and interactive behaviors of different agents to ensure the efficient implementation of scheduling. The agents are the independent system operator (ISO), LAs, and residential users. First, the rationality levels of the various agents are analyzed, and corresponding utility functions are developed to target each agent specifically. Then, the interactive relationships among the various agents are examined, and a bi-level game model that incorporates a “pricing-response” approach is developed to enhance the feasibility of scheduling results, utilizing both Stackelberg and evolutionary game theories. Finally, uncertain factors are introduced, and the correlations between them are analyzed using copula theory, which results in model refinements that improve accuracy and pertinence. Simulations verify the effectiveness and economic efficiency of the proposed competitive incentive mechanism in terms of achieving a win-win situation for all agents.

This letter presents an optimization-based Heating, Ventilation, and Air Conditioning (HVAC) and PV disaggregation approach. This letter builds on the previous works of authors, which discuss HVAC disaggregation strategy for aggregated levels without requiring sub-metered information. This letter expands on previous works to discuss optimization-based HVAC disaggregation by varying PV penetration scenarios with joint disaggregation of HVAC load and PV.

Aggregating a large number of electric vehicles (EVs) can provide considerable capacity for frequency regulation (FR) services. However, disaggregating real-time FR commands to individual EVs results in both immediate impacts to EVs and cascading impacts on future FR bidding. It is crucial to manage the trade-off between these two impacts during disaggregation. This paper proposes a deep reinforcement learning (DRL)-based FR provision method that balances user charging anxiety and aggregate flexibility in real-time disaggregation. The immediate and cascading impacts of disaggregation are characterized in two objectives: 1) user compensation costs related to charging anxiety, and 2) the preservation of EV aggregate flexibility. Incorporating these two objectives, a multi-objective disaggregation optimization problem is formulated with a learnable soft switching factor to balance these objectives. To determine the optimal soft switching factor and achieve coordination between bidding and disaggregation, the DRL algorithm is employed to jointly optimize market bidding decisions and the soft switching factor. Additionally, a consistency-based solution is derived from the disaggregation problem to alleviate computational burdens and protect user privacy. Numerical simulations verify that the proposed method achieves at least 21.2% higher profits for EV aggregators compared to single-objective methods while maintaining low computational complexity.

This paper proposes a safe reinforcement learning (RL)-based transient stability emergency control (TSEC) method for islanded microgrids. RL requires extensive interaction with the environment to learn control strategies, hence, a data-driven approach is used as a substitute for time-consuming time-domain simulation calculations. Deep sigma point processes (DSPP), which is a Gaussian process model, is utilized to predict the normal distribution of transient stability of microgrids and to construct a transient stability chance constraint. Reward-constrained policy optimization (RCPO) can simultaneously achieve objective prediction, policy learning, and constraint cost coefficient update across multiple timescales. RCPO interacts with the DSPP-based microgrid environment through a multi-process parallel manner, greatly increasing the training speed. Case studies on a real islanded microgrid demonstrate that the proposed method can efficiently and quickly obtain the optimal emergency control strategy while adhering to all hard constraints.