The 2019-20 Australia bushfire incurred numerous economic losses and significantly affected the operations of power systems. A power station or transmission line can be significantly affected due to bushfires, leading to an increase in operational costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems subject to bushfires. Considering the stochastic nature of bushfire spread, we develop a model to capture such dynamics based on Moore's neighborhood model. Under a periodic inspection scheme that reveals the in-situ bushfire status, we propose an online optimization modeling framework that sequentially plans the power flows in the electricity network. Our framework assumes that the spread of bushfires is non-stationary over time, and the spread and containment probabilities are unknown. To meet these challenges, we develop a contextual online learning algorithm that treats the in-situ geographical information of the bushfire as a 'spatial context'. The online learning algorithm learns the unknown probabilities sequentially based on the observed data and then makes the OPF decision accordingly. The sequential OPF decisions aim to minimize the regret function, which is defined as the cumulative loss against the clairvoyant strategy that knows the true model parameters. We provide a theoretical guarantee of our algorithm by deriving a bound on the regret function, which outperforms the regret bound achieved by other benchmark algorithms. Our model assumptions are verified by the real bushfire data from NSW, Australia, and we apply our model to two power systems to illustrate its applicability.

Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us. Potential applications include protecting fragile facilities and designing robust topologies, etc. Due to the strong correlation between different topological characteristics and infrastructure vulnerability and their complicated evolution mechanisms, some heuristic and machine-assisted analysis fall short in addressing such a scenario. In this paper, we model the interdependent network as a heterogeneous graph and propose a system based on graph neural network with reinforcement learning, which can be trained on real-world data, to characterize the vulnerability of the city system accurately. The presented system leverages deep learning techniques to understand and analyze the heterogeneous graph, which enables us to capture the risk of cascade failure and discover vulnerable infrastructures of cities. Extensive experiments with various requests demonstrate not only the expressive power of our system but also transferring ability and necessity of the specific components.

Smart grids are part of a growing "smart" phenomenon involving distributed devices that are wirelessly connected and intelligently controlled to automate decisions normally left to people. The Internet of Things (IoT) is the most popular example of this trend, with smart phones, thermostats, fridges, and even cars working in concert to share real-time data and make decisions autonomously. Smart grid technology does the same thing – but for energy. This comprehensive guide explains how smart electrical grids work, why they are important, and how they are helping to revolutionize the electricity landscape – especially as distributed energy sources (DERs) like solar, wind, and battery storage continue to place stress on America's aging power infrastructure. You may also enjoy this brief 30-minute podcast that introduces the challenges of smart grids and highlights some of the benefits of AI to improve energy and utility operations.

A linear function submission-based double-auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market.The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework except for a constant factor.

The electricity network balancing problem consists of ensuring that the electricity demands of the consumers are met by the committed supply. Constraints are imposed on the different elements of the network, so that damage to the equipment is prevented when transformers are stepped up or down, or generation is increased. We consider this problem within zones, which are sub-networks constructed using carefully chosen decomposition principles. The automation of decision making in electricity networks is a step forward in their management which is necessary for coping with the increase in power system complexity that we expect in the near term. In this paper we explore the deployment of planning techniques to solve the zone-balancing problem. Embedding electricity networks in a domain description presents new challenges for planning. The key point is that the propagation of information requires complex updates to the state when an action is applied. We have developed a method in which the computation of the critical numeric quantities is performed calling an external power flow equation solver, demonstrating a clean interface between the planner and this domain-specific computation. This solver allows us to move the power flow computations outside of the planning process and update the values efficiently. We also examine a second important feature of this problem, which is the interaction between exogenous events and constraints over the entire plan trajectory within a zone.