Residential flexible resource is attracting much attention in demand response (DR) for peak load shifting. This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communities considering the incomplete information. Communities in the scenario can decide whether to participate in DR in each stage, but the decision is the private information that is unknown to other communities. To optimize the energy consumption, a Bayesian game approach is formulated, in which the probability characteristic of the decision-making of residential communities is described with Markov chain considering human behavior of bounded rationality. Simulation results show that the proposed approach can benefit all residential communities and power grid, but the optimization effect is slightly inferior to that in complete information game approach.

Electric vehicle charging networks (EVCNs) are vulnerable to power outages. The resilience of critical charging infrastructures is vital for providing uninterrupted service. When a hurricane occurs, the offline charging stations (CSs) may cause the redistribution of EV traffic flow, resulting in the congestion at the healthy CSs and potentially leading to a cascade failure. This paper presents a probabilistic graphical modeling (PGM) framework for predicting the hurricane-induced cascading failures of EVCNs. A Bayesian network is proposed to capture the relationships between a hurricane, the power and traffic systems coupled by an EVCN. An AC-based Cascading Failure model (ACCF) is established to simulate the line overloading and load shedding. EV load redistribution is formulated as a traffic assignment problem considering the road impedance between the nearby CSs. The proposed PGM is trained by the Expectation-Maximization (EM) algorithm considering the traffic flow as the hidden variable. Clique tree method is used to infer the propagation probability of cascading failure. The case study demonstrates the causal relationship between hazards and cascading failures of EVCNs under different hurricane scenarios. Results show that the proposed method can predict the propagation of cascading failures in EVCNs by identifying and quantifying the correlation among the susceptible CSs.

In order to realize massive residential consumers participating in demand response (DR), load aggregator (LA) can be employed to aggregate flexible loads. In this article, a distributed optimization approach based on state-based potential game (SPG) is proposed to describe the bidding behavior among LAs in day-ahead DR market for load shifting amount. Under the consideration of DR resource's energy consumption satisfaction, profit-oriented bidding decision problem is modeled from the perspective of individual LA and DR market. Basically, the establishment and proof of an SPG model is presented in details. Then, the best-strategy-response iterative algorithm is proposed to search for the Nash equilibrium in a distributed way. Finally, simulation is carried out to verify the efficiency and effectiveness of the proposed approach. Simulation results show that the whole profit of all LAs increases 0.15%, while the running time of the algorithm reduces 59.53% compared with the conventional algorithm in the noncooperative game.

Urban power-transportation systems are prone to cascading failures in cold weather, rooted in the risk of charging station outages caused by faults of the distribution grid and mileage reduction of electric vehicles (EVs). Motivated by this, this article proposes an online graph-attentional multiagent deep reinforcement learning (GAMD) method to enhance the power system resilience and EV charging convenience in cold weather. To address the high-dimensional and nonEuclidean characteristics of the state of power-transportation network, the method integrates a graph attention network for global state perception with a decentralized multiagent system for collaborative decision-making. The proposed method optimizes conflicting objectives, including the resilience of EV charging service, traffic efficiency, and the economic efficiency of emergency power supply. For the first time, the proposed method incorporates the traffic impedance variations induced by V2G dispatch to take into account the dynamic traffic conditions. The proposed method is validated with a high-fidelity microscopic simulation platform simulation of urban mobility (SUMO) driven by real-world data. Results suggest the superiority of the proposed method over state-of-the-art benchmarks, with noted improvements across multiple performance metrics. The aforementioned endeavor establishes a robust and scalable paradigm for managing a dispatch-intensified coupled urban power-transportation system.