Traditional power grid infrastructure presents significant barriers to renewable energy integration and perpetuates energy access inequities, with low-income communities experiencing disproportionately longer power outages. This study develops a Markov Decision Process (MDP) framework to optimize renewable energy allocation while explicitly addressing social equity concerns in electricity distribution. The model incorporates budget constraints, energy demand variability, and social vulnerability indicators across eight major U.S. cities to evaluate policy alternatives for equitable clean energy transitions. Numerical experiments compare the MDP-based approach against baseline policies including random allocation, greedy renewable expansion, and expert heuristics. Results demonstrate that equity-focused optimization can achieve 32.9% renewable energy penetration while reducing underserved low-income populations by 55% compared to conventional approaches. The expert policy achieved the highest reward, while the Monte Carlo Tree Search baseline provided competitive performance with significantly lower budget utilization, demonstrating that fair distribution of clean energy resources is achievable without sacrificing overall system performance and providing ways for integrating social equity considerations with climate goals and inclusive access to clean power infrastructure.

Australia must "lean in hard" to the benefits of artificial intelligence or else risk ending up "on the end of somebody else's supply chain", according to the new industry and science minister, Tim Ayres, with the Labor government planning to further regulate the rapidly evolving technology. Ayres, a former official with the manufacturing union, acknowledged Australians remained sceptical about AI and stressed that employers and employees needed to have discussions about how automation could affect workplaces. The minister said Australia had "no alternative" but to embrace the new technology and seek to become a world leader in regulating and using AI. "It's the government's job to lean into the opportunity to outline that for businesses and for workers, but also to make sure that they are confident that we've got the capability to deal with the potential pitfalls," Ayres told Guardian Australia. "I think the Australian answer has got to be leaning in hard and focusing on strategy and regulation that is in the interest of Australians."

Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning, despite their ability to jeopardize the energy transition. We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system. First, we evaluate unexpected events assuming "perfect foresight", where decision-makers can anticipate such events in advance. This allows us to identify dealbreakers, i.e., conditions that make the transition infeasible. Then, we assess the events under "limited foresight" to evaluate the robustness of early-stage decisions against unforeseen unexpected events and the costs associated with managing them. A case study for Belgium demonstrates that a lack of electrofuel imports in 2050 is the main dealbreaker, while accelerating the deployment of renewables is the most robust policy. Our transferable method can help policymakers identify key dealbreakers and devise robust energy transition policies.

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.

V.EV, Vitol's fleet electrification business, offers a turnkey fleet electrification solution to fleets of all vehicle types. By offering an end to end service to identify the right solution to enable fleets to decarbonise, the provision and installation of charging infrastructure and the subsequent operation of your fleet's chargepoints, battery storage and onsite generation through our software solution we can accelerate the rate the UK's fleets decarbonise. Our parent company is the world's largest independent energy and commodities trading company. From 40 offices worldwide, Vitol seek to add value across the energy supply chain, including deploying its scale and market understanding to help facilitate the energy transition. To date, Vitol committed over $2.2 billion of capital to renewable projects, and are identifying and developing low-carbon opportunities around the world.

The energy transition potentially poses an existential risk for major international oil companies (IOCs) if they fail to adapt to low-carbon business models. Projections of energy futures, however, are met with diverging assumptions on its scale and pace, causing disagreement among IOC decision-makers and their stakeholders over what the business model of an incumbent fossil fuel company should be. In this work, we used deep multi-agent reinforcement learning to solve an energy systems wargame wherein players simulate IOC decision-making, including hydrocarbon and low-carbon investments decisions, dividend policies, and capital structure measures, through an uncertain energy transition to explore critical and non-linear governance questions, from leveraged transitions to reserve replacements. Adversarial play facilitated by state-of-the-art algorithms revealed decision-making strategies robust to energy transition uncertainty and against multiple IOCs. In all games, robust strategies emerged in the form of low-carbon business models as a result of early transition-oriented movement. IOCs adopting such strategies outperformed business-as-usual and delayed transition strategies regardless of hydrocarbon demand projections. In addition to maximizing value, these strategies benefit greater society by contributing substantial amounts of capital necessary to accelerate the global low-carbon energy transition. Our findings point towards the need for lenders and investors to effectively mobilize transition-oriented finance and engage with IOCs to ensure responsible reallocation of capital towards low-carbon business models that would enable the emergence of fossil fuel incumbents as future low-carbon leaders.

The Fourth Industrial Revolution – artificial intelligence in particular – has the potential to solve some of the current conundrums of the green transition. Over the last two centuries, the world's major energy transitions were driven primarily by technological breakthroughs. The steam engine allowed coal to fuel the Industrial Revolution and displaced traditional biomass in the world energy mix. Then the internal combustion engine opened the door for oil to dominate the transport sector and the global energy mix for decades – a position it still holds to date. Today, one major development is still unfolding; its final impact is difficult to predict or even comprehend at this stage.

The energy transition requires critical minerals. Though the U.S. has plentiful resources of its own, the country has largely relied on foreign sources. That's in part because one major roadblock to accessing American critical mineral deposits is that they remain largely unmapped. That may be about to change, though. The Department of Defense and the U.S. Geological Survey have issued two separate challenges to explore using artificial intelligence and machine learning to expedite USGS' task of assessing the availability and mining potential of 50 critical minerals.

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their awareness of associated benefits. In this view, this work analyses online news data on energy communities to understand people's awareness and the media importance of this topic. We use the Semantic Brand Score (SBS) indicator as an innovative measure of semantic importance, combining social network analysis and text mining methods. Results show different importance trends for energy communities and other energy and society-related topics, also allowing the identification of their connections. Our approach gives evidence to information gaps and possible actions that could be taken to promote a low-carbon energy transition.

While the Fukushima event led to a particularly strong change in energy policies in Germany, resulting in the so-called Energiewende, or energy transition, the trend toward renewables is visible worldwide. Here, we outline how major challenges of the energy transition have led to a strong need for essential contributions from the computer science community to maintain stability and security of supply, particularly for the electric power grid. As a result, the new discipline of Energy Informatics has emerged which is addressing this highly interdisciplinary and dynamic field of research and development. In tomorrow's energy system, electric power will be provided mainly by photo-voltaic modules on rooftops and in larger field installations, and by wind power plants, onshore as well as offshore. Being weather-dependent, this energy supply is inherently volatile and only partially controllable.