Bo Zhang

Driven by “dual carbon” goals and stricter distribution-network (DN) security constraints, designing a multi-park electricity–carbon emission right–renewable energy certificates (ECR) coupled trading mechanism that ensures carbon reduction, high renewable energy integration and compliance with power flow constraints has become a pressing issue. This paper develops a bi-level coupled ECR trading and correction framework for day-ahead market (DAM) that accounts for the information barrier between levels, with integrated energy systems (IESs) as core participants. In the lower level, a peer-to-peer (P2P) trading mechanism based on asymmetric Nash bargaining (ANB) theory is developed for ECR coupled day-ahead trading market, which can capture the coexistence of diverse low-carbon indicators in the current trading environment and drive the IESs toward eco-economic operation. In the upper level, a multi-stakeholder-oriented correction strategy for ECR coupled day-ahead trading is established to mitigate potential power flow violations and inter-temporal violation transfers, which consists of a single-period correction strategy that consider both fairness and economic efficiency, and a flexible time window-based correction strategy under multi-period coupling. Through illustrative examples, the superiority of the proposed method is verified in terms of enhancing prosumer profits, reducing carbon emissions, promoting renewables utilization, and ensuring power-flow constraints with faster inter-level convergence.

The proliferation of Digital Twin (DT) in community-level smart grids makes data exchange between physical entity (i.e., Data Collection Points, DCPs) and the virtual Utility Node (UN) more frequent, and thus vulnerable to interception by potential Eavesdroppers (Eves), which imposes stringent requirements on both data freshness and transmission security. To address this, we propose a covert communication framework utilizing a full-duplex UN that emits artificial noise to obscure uplink transmissions from a potential fixed Eve. To ensure timely information updates and cyber-physical synchronization within the DT architecture, an average Peak Age-of-Information (PAoI) minimization problem is formulated under constraints on covertness, link reliability, total energy, and maximum power. Given the channel randomness, the closed-form minimum detection error rate is derived based on binary hypothesis testing. Then, we utilize the channel’s statistical distribution to convert the probabilistic reliability constraint into a deterministic form. Since the problem is proven to be a convex optimization problem, a distributed iterative algorithm is developed based on the Lagrange dual decomposition and the Karush–Kuhn–Tucker (KKT) conditions. Furthermore, we establish a rigorous theoretical proof of the solution’s uniqueness and optimality by employing the standard interference function framework, the Descartes’ rule of signs, and the intermediate value theorem. Finally, extensive numerical simulations demonstrate the proposed algorithm reduces the average PAoI by 34% and 49.2% compared to baseline strategies while lowering energy consumption by 14.8%, validating its effectiveness in securing DT-enabled community-level smart grids.

The degree of active power fluctuation is a key indicator for assessing the stability of active distribution networks. However, with the increasing clustering of distributed resources within these networks and the deepening integration of cyber-physical systems, uncertainties arising from cyber and physical domains, e.g., load variations and transmission congestion, will compound and exacerbate power fluctuations. Unlike existing methods that use cyber-physical cut-off control or firewall-based passive defenses, this paper proposes a bi-level active power control method based on a cyber-physical cooperation perspective to address these issues. At the upper level, which encompasses source-grid-storage clusters: in the physical layer, an active power support approach is proposed, which incorporates multi-factor matching while considering flow constraints to achieve multi-objective optimization regulation. In the cyber layer, we propose data sensitivity calculations along with demand-driven path planning techniques to ensure that planned paths align with regulatory requirements. At the lower level, focusing on in-cluster resources: in the physical layer, a multi-resource distributed control method based on fault-tolerance principles and a virtual leader-following consensus algorithm is proposed, which enables flexible responses to cluster commands while defending against light congestion interference. In the cyber layer, an eventtriggered path reconstruction method is proposed to defend against heavy congestion interference. The proposed methodology effectively harnesses the aggregation control capabilities of massive resources and facilitates an active defense against network congestion issues. Case studies show that these methods can generate optimal control commands for aggregators and internal resources within seconds to mitigate power fluctuations while ensuring reliable network performance in both planning and operational dimensions.

Dear Editor, The integration of distributed energy resources (DERs) and communication infrastructures makes distribution networks increasingly cyber-physical, requiring resilient and real-time voltage regulation. Network partitioning enables scalable control, yet existing methods often ignore communication and security constraints or rely on costly optimization, limiting practicality under dynamic and adversarial conditions. This letter presents a heterogeneous graph neural network (HGNN) for cyber-physical aware partitioning. The model jointly encodes electrical and communication layers, leverages optimization-guided labels for training, and enhances robustness via edge dropout and adversarial perturbations. Tests on the IEEE 33-bus system show that HGNN produces near-optimal partitions with low computation time and strong resilience to denial-of-service (DoS) and false data injection (FDI) attacks, highlighting its potential for secure and real-time voltage regulation.

With increasing integration of intermittent renewable energy into power systems, effective frequency regulation is more urgent. Virtual power plants (VPPs), aggregating demand-side distributed resources, have great potential for frequency regulation. Nevertheless, deep cyber-physical interactions render VPPs susceptible to diversified cyber attacks, such as denial of service (DoS), false data injection (FDI), and deception attacks, which degrade frequency performance. Notably, existing research on VPPs’ frequency regulation largely ignores comprehensive impacts of such attacks. To address this gap, this paper presents a multi-agent system based cooperative power control method for distributed VPPs to ensure frequency regulation under diversified cyber attacks. First, we introduce a novel VPPs cooperative power control strategy to obtain optimal power references for VPPs and mitigate the combined effects of DoS and FDI hybrid attacks. It combines acknowledgment technique, event-triggered mechanism, and trust agents-based state screening and compensation method, enhancing reliability and economic efficiency of VPPs’ frequency regulation. Moreover, we propose a unique inverters dynamic power control scheme for accurate reference tracking of each internal unit within VPPs under deception attack. Based on a new event-triggered dynamic power control model with deception attack, it employs a specially designed dynamic event-triggered communication-based control strategy, ensuring robust tracking control and reducing communication burdens. Finally, simulation results demonstrate the validity and superiority of our presented method.

To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply–demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.