Chao Long

This paper investigates demand response management in local energy markets of smart girds involving multiple utility companies (UCs) and multiple consumers by using the Stackelberg game model. Most existing Stackelberg game-based studies focus solely on competition within UC or consumer groups, overlooking the practical scenarios where both groups may operate in competitive environments simultaneously. A novel nested game-based framework is established to provide equilibrium-based insights to derive optimal strategies for UCs and consumers. Specifically, the proposed nested game comprises a Stackelberg game that models the coupling behavior between UCs and consumers, along with two separate Nash games that characterize the competition within each group. The intra-group competition among consumers stems from their pursuit of price-based demand response strategies subject to the coupled constraint imposed by limited generation capacity, while the intra-group competition among UCs arises from competitive payoffs in the wholesale market.By utilizing the best-response-based analysis, the analytical expressions of the Nash equilibria in both Nash games are derived, thereby facilitating the computation of the Stackelberg equilibrium. Numerical results show that the proposed method increases the total incomes of all the UCs and consumers by 7.25%(compared to the time-of-use pricing method) and 26.81%(compared to the fixed pricing method), respectively.

Hile multi-device coordination significantly enhances the capability for comprehensive power quality management in distribution networks, its practical effectiveness is severely hindered by communication imperfections such as delays, packet loss, and intermittent interruptions. To address these challenges, this paper proposes a communication-resilient coordinated power quality management framework based on multi-agent reinforcement learning (MARL). The non-ideal communication process across the MARL training and execution phases is considered. Incomplete or unreliable data caused by packet loss and interruptions is recovered via data-driven spatiotemporal reconstruction, while impacts of communication delays are mitigated through a timestamp-verified event-triggered execution mechanism. Furthermore, to mitigate the policy learning bias induced by training data disturbances from communication imperfections, a novel MARL algorithm is proposed. By modeling the distribution rather than only the expected value of decision outcomes, the proposed method captures uncertainty from noisy and incomplete data, improving robustness to communication impairments. Extensive simulations on a modified IEEE 123-node system demonstrate that the proposed method significantly outperforms baselines under imperfect communication conditions and maintains robust effectiveness under severe communication impairments.

To enhance the resilience of coordinated dispatch in port micro-energy grid (PMEG) clusters under typhoon conditions, this paper develops a two-stage rolling resilient dispatch strategy that explicitly considers the spatio-temporal transfer of hydrogen-powered vessels. A typhoon-driven scenario model first maps the evolving wind field into wind-power availability, port-load corrections, external-grid support limits, and maritime route-accessibility conditions. Hydrogen-powered vessels are then represented as mobile hydrogen-storage and emergency ship-to-grid (S2G) resources in a time-expanded network, where navigation bans, wind-induced speed reduction, sailing energy consumption, berthing, refueling, and S2G constraints are jointly enforced. In the pre-disaster stage, unit commitment, port and vessel hydrogen preparation, vessel pre-deployment, and a forecast-feasible baseline sailing-operating schedule are determined from forecast information. In the intra-disaster stage, port electricity-hydrogen dispatch, vessel transfer, S2G support, and load curtailment are re-optimized in a receding-horizon manner using updated meteorological and operating states. After the physical schedule is finalized, an ex-post asymmetric Nash bargaining mechanism allocates the cooperative benefit among ports, preserving the coordinated dispatch while ensuring individual rationality. Case studies demonstrate the value of combining advance preparation, typhoon-constrained vessel mobility, and ex-post settlement for resilient multi-port operation.

While multi-device coordination significantly enhances the capability for comprehensive power quality management in distribution networks, its practical effectiveness is severely hindered by communication imperfections such as delays, packet loss, and intermittent interruptions. To address these challenges, this paper proposes a communication-resilient coordinated power quality management framework based on multi-agent reinforcement learning (MARL). T he non-ideal communication process across the MARL training and execution phases is considered . Incomplete or unreliable data caused by packet loss and interruptions is recovered via data-driven spatiotemporal reconstruction, while impacts of communication delays are mitigated through a timestamp-verified event-triggered execution mechanism . Furthermore, to mitigate the policy learning bias induced by training data disturbances from communication imperfections, a novel MARL algorithm is proposed. By modeling the distribution rather than only the expected value of decision outcomes , the proposed method captures uncertainty from noisy and incomplete data, improving robustness to communication impairments. Extensive simulations on a modified IEEE 123-node system demonstrate that the proposed method significantly outperforms baselines under im perfect communication conditions and maintains robust effectiveness under severe communication impairments.

While multi-device coordination significantly enhances the capability for comprehensive power quality management in distribution networks, its practical effectiveness is severely hindered by communication imperfections such as delays, packet loss, and intermittent interruptions. To address these challenges, this paper proposes a communication-resilient coordinated power quality management framework based on multi-agent reinforcement learning (MARL). The non-ideal communication process across the MARL training and execution phases is considered. Incomplete or unreliable data caused by packet loss and interruptions is recovered via data-driven spatiotemporal reconstruction, while impacts of communication delays are mitigated through a timestamp-verified event-triggered execution mechanism. Furthermore, to mitigate the policy learning bias induced by training data disturbances from communication imperfections, a novel MARL algorithm is proposed. By modeling the distribution rather than only the expected value of decision outcomes, the proposed method captures uncertainty from noisy and incomplete data, improving robustness to communication impairments. Extensive simulations on a modified IEEE 123-node system demonstrate that the proposed method significantly outperforms baselines under imperfect communication conditions and maintains robust effectiveness under severe communication impairments.

While multi-device coordination significantly enhances the capability for comprehensive power quality management in distribution networks, its practical effectiveness is severely hindered by communication imperfections such as delays, packet loss, and intermittent interruptions. To address these challenges, this paper proposes a communication-resilient coordinated power quality management framework based on multi-agent reinforcement learning (MARL). The non-ideal communication process across the MARL training and execution phases is considered. Incomplete or unreliable data caused by packet loss and interruptions is recovered via data-driven spatiotemporal reconstruction, while impacts of communication delays are mitigated through a timestamp-verified event-triggered execution mechanism. Furthermore, to mitigate the policy learning bias induced by training data disturbances from communication imperfections, a novel MARL algorithm is proposed. By modeling the distribution rather than only the expected value of decision outcomes, the proposed method captures uncertainty from noisy and incomplete data, improving robustness to communication impairments. Extensive simulations on a modified IEEE 123-node system demonstrate that the proposed method significantly outperforms baselines under imperfect communication conditions and maintains robust effectiveness under severe communication impairments.