Grid interconnection is a key strategy for strengthening power system resilience to extreme weather events by facilitating intersystem mutual assistance. Despite the overall reduction in risk exposure, significant residual risks remain that could still lead to catastrophic consequences. While insurance offers a means to transfer these risks, conventional standalone models struggle to balance insurer solvency with premium affordability and fail to incentivize participation from lower risk areas. Inspired by spatial risk diversification, this article proposes a novel coalitional insurance framework for weather-related risk management of interconnected transmission systems (ITS). The framework is built on a joint resilience assessment model that quantifies power outage risks in ITS, accounting for intersystem mutual assistance. To solve this model with guaranteed convergence and well-preserved privacy, a distributed optimization approach based on the Bregman alternating direction method of multipliers and iterative optimization is developed. Furthermore, specially designed exante premium and expost indemnity policies ensure equitable allocation and promote coalition participation. Numerical experiments on the IEEE RTS-96 system validate the effectiveness and superiority of the proposed coalitional insurance scheme.

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based onresilience importance indicesare designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude.

Quantum communication is a promising technique for enhancing the information security. However, the limited availability of quantum keys due to low generation rates still remains a challenge in the quantum key distribution of power systems. To address this issue and optimize the utilization of quantum resources in the gradually developed quantum-secured power communication systems, this article proposes a hybrid resource optimization method by leveraging network virtualization technologies. First, we present a virtualized architecture using software-defined networking and network function virtualization to support multiapplication scenarios in power system communications. Subsequently, a two-stage resource optimization strategy is proposed to manage both classical and quantum resources. At the first stage, we design a network slicing scheme for allocating classical communication resources, such as channel capacity and time delay. This scheme aims to segregate information flows from applications with diversified quality of service requirements. At the second stage, encryption resources are allocated by matching limited quantum keys with communication requests with different priorities, thereby improving the overall performance of quantum-classical power communication systems. Finally, we construct a cosimulation testbed to validate the effectiveness of the proposed method through case studies.

With the rapid development of power distribution systems (PDSs), the number of terminal devices and the types of delivered services involved are constantly growing. These trends make the operations of PDSs highly dependent on the support of advanced communication networks, which face two related challenges. The first is to provide sufficient flexibility, resilience, and security to meet varying demands and ensure the proper operation of gradually diversifying network services. The second is to realize the automatic identification of terminal devices, thus reducing the network maintenance burden. To solve these problems, this paper presents a novel multiservice network integration and device authentication slice-based network slicing scheme. In this scheme, the integration of PDS communication networks enables network resource sharing, and recovery from communication interruption is achieved through network slicing in the integrated network. Authentication servers periodically poll terminal devices, adjusting network slice ranges based on authentication results, thereby facilitating dynamic network slicing. Additionally, secure plug-and-play support for PDS terminal devices and network protection are achieved through device identification and dynamic adjustment of network slices. On this basis, a network optimization and upgrading methodology for load balancing and robustness enhancement is further proposed. This approach is designed to improve the performance of PDS communication networks, adapting to ongoing PDS development and the evolution of PDS services. The simulation results show that the proposed schemes endow a PDS communication network with favorable resource utilization, fault recovery, terminal device plug-and-play support, load balancing, and improved network robustness.

This article proposes an integrated optimization model for unbalanced distribution system restoration after large-scale power outages caused by extreme events. The model can coordinate the control actions of multiple types of distributed energy resources (DERs), including dispatchable distributed generators (DGs), renewable DGs (mainly wind and solar), and energy storage systems (ESSs). The model also considers topology flexibility by forming dynamic islands through reconfiguration. Besides, the optimal dispatch model of repair crews and mobile emergency generators are also proposed to leverage the restoration capabilities of existing DERs installed in the distribution systems. The integrated optimization model is linearized to be a mixed-integer linear programming form, which can be effectively solved by off-the-shelf solvers such as Cplex and Gurobi. Numerical results on IEEE 123 and 8500 node test feeders validated the effectiveness of the proposed model and highlighted the necessity of coordinating various flexible resources.

The Smart Grid represents an unprecedented opportunity to move the electric grid into a new era of reliability, availability, and efficiency. It uses two-way communications, digital technologies, advanced sensing and computing infrastructure, and software abilities to provide improved monitoring, protection and optimization of all the grids’ components including generation, transmission, distribution and consumers.

Recent severe power outages caused by extreme weather hazards have highlighted the importance and urgency of improving the resilience of the electric power grid. As the distribution grids still remain vulnerable to natural disasters, the power industry has focused on methods of restoring distribution systems after disasters in an effective and quick manner. The current distribution system restoration practice for utilities is mainly based on predetermined priorities and tends to be inefficient and suboptimal, and the lack of situational awareness after the hazard significantly delays the restoration process. As a result, customers may experience an extended blackout, which causes large economic loss. On the other hand, the emerging advanced devices and technologies enabled through grid modernization efforts have the potential to improve the distribution system restoration strategy. However, utilizing these resources to aid the utilities in better distribution system restoration decision making in response to extreme weather events is a challenging task. Therefore, this paper proposes an integrated solution: a distribution system restoration decision support tool designed by leveraging resources developed for grid modernization. First, we review the current distribution restoration practice and discuss why it is inadequate in response to extreme weather events. Then, we describe how the grid modernization efforts could benefit distribution system restoration, and we propose an integrated solution in the form of a decision support tool to achieve the goal. The advantages of the solution include improving situational awareness of the system damage status and facilitating survivability for customers. The paper provides a comprehensive review of how the existing methodologies in the literature could be leveraged to achieve the key advantages. The benefits of the developed system restoration decision support tool include the optimal and efficient allocation of repair crews and resources, the expediting of the restoration process, and the reduction of outage durations for customers, in response to severe blackouts due to extreme weather hazards.

In this article, we perform a comprehensive survey of the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs); the power architecture with multibus characteristics; different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings; the communication technologies and network architectures; and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. The current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.

This paper proposes a Real-Time Pricing (RTP)-based power scheduling scheme as demand response for residential power usage. In this scheme, the Energy Management Controller (EMC) in each home and the service provider form a Stackelberg game, in which the EMC who schedules appliances' operation plays the follower level game, and the provider who sets the real-time prices according to current power usage profile plays the leader level game. The sequential equilibrium is obtained through the information exchange between them. Simulation results indicate that our scheme can not only save money for consumers, but also reduce peak load and the variance between demand and supply, while avoiding the "rebound" peak problem.