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Artificial intelligence may have the potential to deliver more efficient, reliable and sustainable energy systems, particularly by helping to manage the complexity of distributed generation and demand-side management. However, stakeholders expect interpretability and explainability, and AI tools must also be transparent, secure and trustworthy. Explainable AI (XAI) models are intended to make AI algorithms more transparent and understandable without reducing their performance and efficiency.

Energy management involves the planning and operation of energy production, consumption, distribution and storage, with objectives including resource conservation, climate protection and cost savings. Growth in renewable energy - essential for the transition to a decarbonised energy system - adds the challenge of intermittency, making energy management all the more important.

Batteries are a necessary part of a low-emission energy system, as they can store renewable electricity and assist the grid. Utility-scale batteries, with capacities of several to hundreds of MWh, are particularly important for condominiums, local grid nodes, and EV charging arrays. However, such batteries are expensive and need to be monitored and managed well to maintain capacity and reliability. Artificial intelligence offers a solution for effective monitoring and management of utility-scale batteries.

Wind power is unanimously recognized as one of the major drivers of the energy transition. Increasing renewable power generation introduces significant challenges for both the operation and planning of power systems, driven by the intrinsic uncertainty of stochastic renewable resources and by the growing spatial distribution of generation assets. Wind power presents a distinctive set of challenges in this context.

The widespread deployment of photovoltaics (PV) technology has emerged as a key element in the global shift toward a carbon-neutral and sustainable energy system. Driven by a combination of supportive regulatory frameworks, government incentive programs, technical developments, and increasing environmental awareness, the adoption of PV technologies has witnessed remarkable growth in recent years. However, the rapid integration of distributed PV systems into existing electricity grid infrastructure introduces new challenges, particularly concerning voltage regulation, reverse power flow, and congestion within the electricity grid. These issues are intensified when PV systems are integrated without proper strategy. In this context, solar PV power forecasting has become an essential tool for ensuring the reliable and efficient integration of solar PV systems into power systems. Artificial intelligence (AI) and machine learning (ML) offer means to forecast PV power and energy generation based on historical data of PV generation, meteorological data, and/or weather forecasts.

Polycrystalline thin-film solar cells have reached a levelized cost of energy that is competitive with all other sources of electricity. The technology has significantly improved in recent years, with laboratory cell efficiencies for cadmium telluride (CdTe), perovskites, and copper indium gallium diselenide (CIGS) each exceeding 22 percent. Both CdTe and CIGS solar panels are now produced at the gigawatt scale. However, there are ongoing challenges, including the continued need to improve performance and stability while reducing cost. Advancing polycrystalline solar cell technology demands an in-depth understanding of efficiency, scaling, and degradation mechanisms, which requires sophisticated characterization methods. These methods will enable researchers and manufacturers to improve future solar modules and systems.

Agrivoltaics, also called agricultural-photovoltaics (Agri-PV or APV), integrates solar power generation into an agricultural activity on farmland.

As glaciers melt, treasured species such as salmon and crab decline, oil reserves wane, and consumer energy bills continue to rise, Alaska is reevaluating its energy future, and the state’s rural communities are leading the way. Alaska's Microgrid Innovators explores how climate change refocuses the story of the state’s energy development history, looking not at the well-told story of oil and natural gas but instead at wind, solar, hydro, and other low-carbon energy sources that have delivered immense economic savings and environmental benefits to both Alaska state residents and the world.

Renewable energy integration projects have sprung up in Alaska’s rural communities since the mid-1990s not due to regulatory fiat but rather to economic necessity. In Alaska’s Microgrid Innovators, pioneers across the stakeholder spectrum—including professionals, academics, Native peoples, policy makers, and other industry participants—describe how they have helped to develop more affordable and sustainable energy systems.

Along with chronicling technological innovation, Alaska’s Microgrid Innovators looks to the future, making the case for succession planning and new public policies as integral for Alaska to meet today’s energy challenges and those to come.

Emerging low-emission energy systems will depend on a range of energy vectors or fuels, for electricity, heating, storage and transport. Each has advantages and shortcomings, qualifying it for certain roles and applications. Liquid ammonia offers a higher energy density than liquid hydrogen.

The urgent need to reduce carbon emissions is leading to growing use of renewable electricity, particularly from wind and photovoltaics. However, the intermittent nature of these power sources presents challenges to power systems, which need to ensure high and consistent power quality. Going forward, power systems also need to be able to respond to changes in loads, for example from EV charging. Neither production nor load changes can be predicted precisely, and so there is a degree of uncertainty or fuzziness. One way to meet these challenges is to use a kind of artificial intelligence - fuzzy logic.