Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers at Texas A&M University are perfecting a deceptively simple solution to our increasingly overburdened energy grid: ice-cooled buildings. This approach, known as thermal energy storage or sometimes referred to colloquially as "ice batteries," uses energy to freeze liquid overnight, when most people are asleep and electricity demand is lower. That stored ice is then melted to help cool building temperatures during peak hours. If successful, the end result is reduced electricity use for air conditioning during the day, which could decrease overall energy demand and help lower costs.

AI's integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. Today, new analysis by MIT Technology Review provides an unprecedented and comprehensive look at how much energy the AI industry uses--down to a single query--to trace where its carbon footprint stands now, and where it's headed, as AI barrels towards billions of daily users. This story is a part of MIT Technology Review's series "Power Hungry: AI and our energy future," on the energy demands and carbon costs of the artificial-intelligence revolution. We spoke to two dozen experts measuring AI's energy demands, evaluated different AI models and prompts, pored over hundreds of pages of projections and reports, and questioned top AI model makers about their plans.

Russian drone strikes near a nuclear power plant in western Ukraine this week have revived anxiety among Ukrainian officials and civilians over one of the most oppressive hardships of the war: a winter assault on their nation's energy grid. The strikes Wednesday, which landed near the Khmelnytsky nuclear facility, drew an angry response from President Volodymyr Zelenskyy of Ukraine, who said it was "highly likely" that the power plant was the target. They also prompted another warning from the head of the United Nations' nuclear watchdog agency about the precarious nuclear safety situation in Ukraine. Zelenskyy vowed Wednesday night that Ukraine would hit back at targets inside Russia if Moscow tried once again to plunge his nation into cold and darkness.

Ensuring electricity grid reliability becomes increasingly challenging with the shift towards renewable energy and declining conventional capacities. Distribution System Operators (DSOs) aim to achieve grid reliability by verifying the n-1 principle, ensuring continuous operation in case of component failure. Electricity networks' complex graph-based data holds crucial information for n-1 assessment: graph structure and data about stations/cables. Unlike traditional machine learning methods, Graph Neural Networks (GNNs) directly handle graph-structured data. This paper proposes using Graph Isomorphic Networks (GINs) for n-1 assessments in medium voltage grids. The GIN framework is designed to generalise to unseen grids and utilise graph structure and data about stations/cables. The proposed GIN approach demonstrates faster and more reliable grid assessments than a traditional mathematical optimisation approach, reducing prediction times by approximately a factor of 1000. The findings offer a promising approach to address computational challenges and enhance the reliability and efficiency of energy grid assessments.

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

In March, a troop of engineers gathered in an unkept green field in rural Nottinghamshire, England. They were there to test a drone piloting software that they hoped could one day be in charge of maintaining the high-voltage pylons that transmit electricity across the country. Assuming the software was working, a drone was about to inspect a pylon from a few meters away, maneuvered not by a nearby pilot but a computer in a control station hundreds of meters away. Seconds later, the dance began. Whizzing around, the drone took 65 photos that documented the condition of the pylon's steel arms, fittings, and conductors.

In the fast-changing world, technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are leading the next wave of productivity gains and tech changes. Artificial intelligence is the main branch of prediction-based technologies which includes domains like machine learning, neural networks and data science. The AI and ML technologies have thrown open a plethora of doors to new applications, solving complex problems and lessening the efforts. In the aame way, AI and ML may endorse the growth of the renewable energy sector in myriad ways. Enabling AI in grid management will also mean shifting from infrastructure-heavy legacy models to a grid that is more resilient and flexible.

Physical systems, such as electricity grids, are very complex and thereby difficult to model. Digital twins provide a solution. "Digital Twins are one of the top technological trends" Here will we discuss a literature review (2021) that was performed by researchers at Bosch Engineering. The review focused on how digital twin and "big data" technology can be applied to complex physical systems, such as energy grids. Without further ado, let's dive in.

Ten years ago, I was engaged in the writing of an energy power grid report that was part of a national initiative to assess the health of our electrical energy grid and its resilience. Assets like wind farms and contemporary fossil and nuclear fuel systems were in place for energy distribution, but to my surprise there was also equipment in the grid that dated back to the 1890s and was still in production. I began to understand the challenges of using renewable energy such as wind and solar when it came to assessing energy supply and demand and ensuring there is enough on-hand energy to power the homes and businesses that are relying on it. When utilities were using gas, coal, or nuclear energy to power the grid, the in-flow of that fuel from its source was consistent, so it was easy to assess supply and demand on any given day and to deliver the energy needed to power homes and businesses. What if the wind gusted to 40 mph one day, and was perfectly still on the next day?

For the energy industry, securing the grid is mission-critical. Increasingly, too, securing devices that lie beyond the centralized grid -- at the edge, so to speak -- is also critical as well as a moving target. Zero-trust cybersecurity, 5G connectivity and machine learning, though, may ultimately help this "smart grid," as this connected energy grid is known, become more resilient in the face of attacks. While the shift toward sustainable energy could help secure a better future for the planet and reduce carbon footprint, the smart grid -- fueled by connected things, microgrids and so on -- creates two-way, risky data flows that add complexity to an already antiquated energy grid. Smart grid technologies can balance peak demand, flatten the load curve and make energy generation sources more efficient, said Brian Crow, Sensus' vice president of analytic solutions, in a recent article on the role of IoT in utilities.