Decarbonising power grids requires affordable storage to accommodate surplus wind and solar electricity over periods of hours to days. Batteries made from abundant elements offer a scalable solution. High-temperature molten salt devices such as sodium-nickel-chloride (Na-NiCl2) batteries are commercial, but their cost remains a barrier. Replacing nickel with cheaper zinc could enable the needed savings, but the techno-economic potential of sodium-zinc (Na-Zn) batteries remains untested. We present the first cost and performance assessment of a solid-electrolyte Na-ZnCl2 battery. Using a bottom-up engineering model and Monte Carlo uncertainty analysis, we find a baseline 240-cell module achieves project-level capital costs of 246-273 USD per kWh capacity, and a levelised cost of storage (LCOS) of 164-195 USD per MWh delivered, if 2030 performance targets are met. These values undercut 2024 prices for stationary lithium-ion systems, and increasing cell capacity to 1.1 kWh or enlarging modules to 15,360 cells lowers LCOS by up to 30%, competitive with leading 4-hour lithium-ion forecasts. Sensitivity analysis shows manufacturing scale and inactive-material reduction outweigh rawmaterial price in driving cost, challenging the view that cheap zinc alone guarantees competitiveness. Our results provide a clear R&D roadmap and position sodium-zinc batteries as a mineral-lean complement to lithiumion for reliable, long-duration, grid-scale energy storage.

High costs of capital are a major barrier to renewables deployment in low- and middle-income countries (LMICs), impeding decarbonisation efforts despite strong renewable resources. Using grid carbon intensity trajectories, we show that renewable deployment in LMICs has mitigation potentials over 20 times larger per project than high-income countries, even before accounting for planned fossil projects. High costs of capital increase renewable levelised costs of electricity (LCOEs) in LMICs substantially, adding an average of US$26/MWh (solar) and US$24/MWh (onshore wind) relative to high-income financing terms. Financing costs account for up to 78% of the LCOE in LMICs, compared with 37-39% in high-income countries, restricting a large portion of global renewable potential. Under a scenario consistent with the target of tripling global renewable capacity by 2030, this corresponds to additional annual costs of US$245bn (solar) and US$329bn (wind). Our results highlight the importance of reducing financing costs in LMICs for a more equitable and efficient energy transition.

The rapid rise in solar photovoltaic (PV) installations globally drives demand for tools to optimize system operations and maintenance. Machine learning models can identify and classify faults (such as cracks or physical damage) or obstructions (such as dust or snow) that reduce performance. However, existing models lack the scalability, accuracy, and generalizability required to handle large, noisy, and diverse datasets of real-world solar panel images. This paper presents SolarSynthNet (SSN), a novel deep learning framework for classifying faulty or obstructed solar panels. SSN is built upon a base feature extraction block supported by an Enhanced Feature Mixing Block and Contextual Focus module. These improve feature extraction by combining information from multiple layers to capture complex patterns, and increase classification accuracy by prioritizing the most relevant regions to focus on discriminative features. Experiments across multiple datasets demonstrate SSN's accuracy, correctly identifying 91.48 % of solar panels in 6-class, 95.83 % in 3-class, and 97.42 % in binary classification. SSN outperforms all existing models in both binary and 6-class tasks. It offers strong potential for optimizing operational performance and maintenance strategies in real-world solar energy systems, thus improving project economics. Future work will improve sample balancing, image quality, and expand SSN to real-time applications.

This briefing examines how artificial intelligence is entering national energy planning and why its use requires explicit governance, assurance and capacity-building. Its scope covers AI applications in forecasting, scenario exploration, optimisation acceleration, digital twins, generative-AI-enabled workflows, stress testing and decision support for increasingly complex, climate-exposed and cross-sectoral energy systems. It argues that AI can improve planning speed, probabilistic analysis and exploration of deep uncertainty, but should complement—not replace—physics-based models, institutional judgement or public accountability. The key finding is that the principal risks of AI in energy planning are institutional rather than purely technical. Poorly governed AI can create opaque model authority, vendor dependence, loss of public-sector capability, weak traceability, data-governance failures, and erosion of national sovereignty over models, data and decisions. The paper therefore proposes the Shared AI Governance for Energy Systems (SAGES) framework as a non-binding, principles-based assurance architecture for governments, regulators and international partners. SAGES centres on five commitments: AI as an empowering partner rather than a default solution; human and institutional accountability for final decisions; proportional, purposeful and testable trust; respect for national sovereignty and institutional maturity; and collective progress towards just energy futures. It recommends practical implementation through a Secretariat and Working Task Groups covering vendor engagement, demonstration tools, sovereignty and IP safeguards, capacity building and model literacy. The briefing concludes that responsible AI adoption requires traceability from data to decision, stress testing, independent challenge, public-sector capability, and international cooperation that supports national ownership without transferring decision rights away from states.

The war in Iran and Russia’s invasion of Ukraine show that energy and geopolitics are locked in a powerful 2-way relationship: fossil fuel supply and demand influence geopolitics, while geopolitics shapes national energy strategies. The energy transition shifts attention to the geopolitics of renewable energy (RE), raising concerns over critical minerals, technological know-how, and manufacturing. However, academic debate largely focuses on how RE impacts geopolitics, neglecting how geopolitics affects RE deployment. This study uses 10 in-depth expert interviews from 2022 to investigate both directions of this relationship. Interviewees identified mechanisms by which solar photovoltaics, wind and battery production increase China-US tensions (particularly regarding manufacturing and critical minerals) and that these tensions already reduce RE deployment (primarily in the US). Recent developments, including the US Inflation Reduction Act (IRA) and its partial retrenchment, tariff escalation and Chinese export controls, are broadly consistent with those channels. We develop and apply a framework around ‘pressures’ and ‘tensions’ that distinguishes structural supply-chain vulnerabilities from the observable geopolitical responses they trigger, and traces how these map onto technology-specific deployment risks. Using this framework, the interviews challenge claims that diversification, substitution or recycling will mitigate critical mineral pressures in the near term. They also reveal differentiated vulnerabilities between technologies: batteries face the greatest geopolitical risks, and wind power is most resilient. Both directions of the RE-geopolitics relationship require further research, particularly with continued changes to the US IRA and new trade tariffs. Decarbonisation may intensify certain geopolitical tensions, and the risks identified here pose notable challenges to the technology roll-out required for the energy transition.

Energy policy is often guided by a small set of least-cost pathways to net-zero emissions, despite wide uncertainty in technology performance, fuel prices, demand and weather. To avoid overstating confidence in any single pathway, we quantify the likelihood of alternative technology pathways and identify the assumptions driving divergence, including the conditions under which technologies reach critical tipping points in competitiveness. We couple a sector-linked national optimisation model with Monte Carlo sampling (10,000 runs) across two European power systems (Germany and Great Britain) to generate probability distributions of capacity expansion and robust cost thresholds for key technologies. Results reveal substantial ambiguity in the future roles of wind versus solar, gas with carbon capture, and negative-emissions options. Tipping points vary widely with system conditions, while cross-country differences highlight the role of institutional constraints and resource endowments. Britain exhibits an either-or decision around nuclear power, investing if costs in 2035 fall below EUR 4700/kW, otherwise favouring offshore wind. Germany's uncertainty centres on dispatchable low-carbon options: gas with carbon capture (below EUR 2100/kW), biomass with carbon capture (below EUR 4200/kW), or hydrogen if electrolysis is below EUR 560/kW. We reframe scenario analysis as risk management by linking uncertainty to cost targets and minimum deployment requirements for robust net-zero strategies.

Battery energy storage systems (BESS) are increasingly needed to provide flexibility and balancing services to accommodate rising shares of variable wind and solar generation, and rapid electrification with electric vehicles, heat pumps, and AI data centres. BESS deployment depends on whether market revenues are high enough and reliable enough to justify their capital-intensive investment. Revenue stacking across multiple markets is essential for a viable business model. However, most valuation studies neglect market-based operational constraints, including batteries being 'skipped': not being dispatched even when they offer the lowest-cost action. We quantify the value of revenue stacking across Great Britain's day-ahead wholesale market and the balancing mechanism, and the cost of battery bids being skipped by the system operator. Using three years of half-hourly price data and a co-optimisation framework, we show that participation in the balancing mechanism increases BESS revenues by up to 250% relative to providing wholesale arbitrage alone. Stacking reduces revenue volatility, as diversifying income streams offsets the Balancing Mechanism's higher price volatility. These gains are highly sensitive though. We show that each 10% increase in battery bids skipped reduces profits by 7%, or around 12,000 pound per MW per year. Similarly, pay-as-bid settlement means imperfect price capture proportionally erodes returns. These results highlight the need for policy changes that enable the better integration of BESS, as market design and dispatch integration can be as important as technology costs in determining whether batteries will deliver low-carbon flexibility at scale.

Solar photovoltaic systems are central to the future of electricity production worldwide. However, their performance degrades with age. The rate of decline in real-world usage critically affects the financial viability and carbon mitigation potential of photovoltaic installations. Earlier studies are typically limited by small sample sizes or short observation periods, and limited treatment of a potential non-linear relationship to environmental factors. This study uses high-dimensional fixed-effects panel regression encompassing up to 16 years of data from over 1 million solar installations in Germany (34 GW of capacity). Key robustness checks, including separate regressions for a self-consumption subsample and sensitivity analysis for air pollution, confirm the reliability of the estimates. The Findings show that power production falls by an average of 0.59% per year. Degradation rates decrease with age, with system output declining between 7% and 13% slower at age 10 than when new, and are one-third higher for larger installations ( >30 kW(p)). Output is significantly affected by environmental variables. Each day of extreme heat or cold and each microgram of particulate matter reduce annual output by 0.038-0.101%. Heat-related degradation intensifies over time, while cold and pollution have stronger effects on newer installations. By providing robust evidence from a population several orders of magnitude larger than previous studies, this study supports improved economic and environmental forecasts and strategic planning for global solar energy expansion. Back of the envelope, the estimated cost of degradation would compared to average literature results decrease by about & euro;638 million p.a. to maintain installed capacity in 2040.

Linear and mixed-integer optimisation models are widely used across economics and engineering to study resource allocation, infrastructure planning, and energy-system transitions. Algebraic modelling languages such as GNU MathProg (GMPL), AMPL, and GAMS let researchers write these models close to their mathematical form, keeping them transparent and reviewable even without extensive coding experience. However, as models grow in scale, translating algebraic formulations into solver-ready sparse matrices becomes a major computational bottleneck. This paper introduces MOSOX, a Rust-based command-line tool and library that compiles a targeted subset of GMPL model and data files into sparse matrices, covering the constructs required by the OSeMOSYS energy-system model family. It expands sets, parameters, variables, objectives, and constraints into matrices, exports them in standard MPS format, and can solve models directly via the HiGHS solver. On OSeMOSYS benchmarks, MOSOX compiles matrices up to 6.5 times faster than GLPK's glpsol while also reducing peak memory use on the largest tested model. By combining fast, low-memory compilation with the readability of GMPL and solver-independent output, MOSOX - developed within the Climate Compatible Growth Program - supports reproducible, auditable, and automatable optimisation workflows for large-scale energy-system modelling.

Mitigating climate change requires broad societal buy-in. Integrated assessment models (IAMs) produce cost-optimal pathways, but these are complex and not easily customized to reflect individuals' preferences. Twenty years ago, the stabilization wedge framework introduced a simpler way to discuss decarbonization. Here, we modernized this framework, identifying 36 strategies, each with the potential to mitigate 4% of global emissions by 2050, and quantified their required scale of deployment. People can build personalized decarbonization pathways by choosing a portfolio of these strategies, with more than 6 trillion combinations that are able to limit global warming to 1.5°C. We assessed which strategies IAMs favor and found that they prioritize technological over behavioral and nature-based solutions, with limited agreement. This framework empowers a general audience to construct and debate pathways, by making informed choices that reflect objectives beyond cost-optimization.

Reanalysis datasets have become indispensable tools for wind resource assessment and wind power simulation, offering long-term and spatially continuous wind fields across large regions. However, they inherently contain systematic wind speed biases arising from various factors, including simplified physical parameterizations, observational uncertainties, and limited spatial resolution. Among these, low spatial resolution poses a particular challenge for capturing local variability accurately. Whereas prevailing industry practice generally relies on either no bias correction or coarse, nationally uniform adjustments, we extend and thoroughly analyse a recently proposed spatially resolved, cluster-based bias correction framework. This approach is designed to better account for local heterogeneity and is applied to 319 wind farms across the United Kingdom to evaluate its effectiveness. Results show that this method reduced monthly wind power simulation errors by more than 32% compared to the uncorrected ERA5 reanalysis dataset. The method is further applied to the MERRA-2 dataset for comparative evaluation, demonstrating its effectiveness and robustness for different reanalysis products. In contrast to prior studies, which rarely quantify the influence of topography on reanalysis biases, this research presents a detailed spatial mapping of bias correction factors across the UK. The analysis reveals that for wind energy applications, ERA5 wind speed errors exhibit strong spatial variability, with the most significant underestimations in the Scottish Highlands and mountainous areas of Wales. These findings highlight the importance of explicitly accounting for geographic variability when correcting reanalysis wind speeds, and provide new insights into region-specific bias patterns relevant for high-resolution wind energy modelling.

As power systems add more wind and solar, electricity supply becomes less controllable and market prices become more volatile. A growing challenge is that renewable generators increasingly earn below-average market prices because their production is highly correlated, an effect known as "revenue cannibalisation" or declining "capture rates". Co-locating battery storage with renewables is widely proposed to shift output to higher-value hours, reduce curtailment, and share grid-connection infrastructure, but the most profitable configurations remain unclear across markets and regulatory designs. This study asks when renewable-storage hybrid projects are economically superior to stand-alone renewables or storage, and which designs and operating strategies best protect revenues. We develop a revenue-stacking optimisation model with explicit efficiency losses and battery degradation to show that profitability depends more on market access and operating constraints than on location alone. Considering a UK case study, we find storage only becomes strongly profitable when it can both charge from the grid and stack revenues across markets. Four-hour discharge duration was most cost-effective, and stand-alone storage tends to be more profitable than co-located or hybridised systems. Extending the analysis across several world regions shows wide geographic variation. Stand-alone storage is favourable in Australian, Nordic and most US markets, while renewable-storage hybrids are superior in Europe, Texas, and parts of Japan. These results provide a practical map from market design and regulation to profitable hybrid architectures, helping investors and policymakers target storage where it most effectively stabilises renewable revenues and supports reliable decarbonisation.

Aquifer thermal energy storage (ATES) is a promising technology for sustainable and climate-friendly space heating and cooling. Compared to conventional heating and cooling techniques, ATES-based systems offer several benefits such as lower greenhouse gas emissions and reduced primary energy consumption. Despite these benefits and the availability of suitable aquifers in many places around the world, ATES has yet to see a widespread global utilization. Currently the vast majority of installed systems is located in the Netherlands, Belgium, Sweden and Denmark. Besides technical and hydrogeological feasibility, appropriate national policies driving ATES deployment are therefore of high importance. Hence, this study provides an international comparison of ATES policies, highlighting best practice examples and revealing where appropriate policy measures are missing. To this end, multi-disciplinary views from experts in geothermal energy and ATES from academia, companies, government authorities, national geological surveys and industrial associations in 30 countries were obtained through an online survey. Subsequent semi-structured interviews with a smaller selection of experts revealed further insights. The online survey results show significant differences regarding the existence and the strength of supporting policy elements between countries of different ATES market maturity. Going beyond these descriptive findings, the interviews provided more country-specific details on how favorable conditions came into effect and what obstacles have still to be overcome for an increased ATES deployment. Based on the lessons learned from the online survey and the expert interviews, recommendations for sophisticated ATES policies are derived which address the following areas: legislative and regulatory issues, raising awareness and expertise, the role of ATES in local energy transitions, and social engagement. This work aims at steering energy policy towards a wider international ATES deployment and better harnessing the potential of ATES to decarbonize buildings.

Access to electricity is the lifeblood of modern society. Electricity grids are undergoing transformative changes, driven mainly by decarbonization, decentralization and the integration of variable renewables. Transmission System Operators (TSOs) face increasing pressures from these changes, yet the scale and pace of innovation required to overcome them remain largely unexplored. This study uses interviews with innovation leaders at 11 European TSOs to investigate their approaches to managing the energy transition, focusing on innovation strategies, technology adoption, and future visions. We find that many TSOs adopt decentralized innovation strategies, involving business lines in defining innovation needs and exploring digital and grid technologies to improve efficiency and flexibility. TSOs are using cross-sector collaborations with various decision-making and benchmarking tools to improve performance and better manage emerging technologies like non-wires alternatives. While TSOs seek to adopt new technologies, regulatory constraints, excessive bureaucracy, technology immaturity, skills gaps, and organizational cultural inertia are raised as key barriers. We argue that proactive engagement in innovation, supported by collaboration and regulatory changes could improve TSO resilience and agility. We highlight the importance of integrating innovation into the core strategy of TSOs and the critical need to modernize regulatory frameworks, originally designed for a very different historical context, to eliminate outdated constraints and enable TSOs to more effectively future-proof their organizations.

Hydrogen has been promoted as a revolutionary fuel for 50 years, yet usage is confined to oil refining and fertilizer production. For hydrogen to advance global decarbonization, many barriers must be overcome. In this Perspective, we examine the challenges hydrogen faces from production to usage, assessing its environmental and economic credentials, controversies and uncertainties. We provide the evidence base for companies and governments to assess clean hydrogen’s current and potential future competitiveness. Fuel cell cars and space heating are among the least promising applications owing to rapid advances in direct electric alternatives. Hydrogen holds potential in industry, long-duration energy storage and long-haul transport, but its competitiveness depends on large-scale deployment yielding substantial cost reductions. Current production cost estimates range by a factor of five and suggest that targets for 2030 will be difficult to achieve, especially once costs for transport and storage are included. The climate impacts of hydrogen production are also uncertain, with production from electrolysis or methane gas with carbon capture potentially increasing system-wide or upstream emissions, alongside water scarcity and persistent organic pollution. Future research must resolve these uncertainties, with strategic focus on deploying hydrogen in priority areas where it is most competitive. Hydrogen has been proposed as a fuel for widespread use since the 1970s, but uptake has repeatedly fallen below projections, primarily due to high costs. This Perspective considers hydrogen’s potential in relation to rapidly improving competitor technologies, and outlines steps for prioritising roles for clean hydrogen within the energy transition.

International and national air pollution policies depend on air quality modelling to establish future emissions ceilings and targets for improving pollutant exposure. Historically, this modelling has typically relied on singular reference energy scenarios, with air pollutant abatement measures added. Greenhouse gas and air pollutant emissions are intrinsically linked through fuel combustion. Decarbonisation and net zero strategies will, therefore, yield significant co-benefits for air pollution. Consequently, the speed and pathway to net zero should be prominently considered within national emissions reduction targets and international renegotiations of air pollution protocols. We illustrate this by modelling the air pollution impacts of four UK 'Future Energy Scenarios' from the National Energy System Operator. Spatially apportioned impact factors are used from the UK Integrated Assessment Model (UKIAM) to provide rapid and replicable air pollution exposure assessments for UK energy system decarbonisation scenarios. We found that net zero scenarios relying on electrification exhibited 40 % lower nitrogen oxide emissions in 2050 than in a hydrogen-based scenario across energy production and use sectors. This led to 36 % and 15 % lower population exposure to nitrogen oxides and fine particulate matter, respectively, which could result in notable health benefits. The choice of energy system decarbonisation pathway has crucial implications for determining which additional air pollution abatement measures would be suitable. Therefore, a holistic approach should be adopted. Policymakers need to understand the net zero strategies being implemented to establish the relevant air pollution abatement options during this era of energy system transition.

Transitioning away from fossil fuels presents substantial challenges, given the growing mismatch between pledges submitted to international climate negotiations and the mitigation strategies that limit warming to below 1.5 °C or 2 °C presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report. The scientific case for phasing out coal-fired electricity is clear, and many countries are progressing towards this. However, despite widespread concerns about risks and trade-offs, natural gas is often considered a bridge fuel, and there is currently no progress towards phasing down its capacity. Previous work on the political feasibility of coal phase-out only considered limited socio-political factors, missing the importance of governance quality and policies supporting the energy transition. There is even more limited understanding of factors associated with gas phase-down, while Europe and North America fall behind trajectories required to limit warming below 1.5 °C. We use multivariate regression and clustering analyses on over four decades of data to investigate the drivers and synergies of coal and gas transitions. This reveals opportunities to overcome fossil fuel lock-in through renewable energy expansion, energy policy reforms, and power market restructuring. Countries with greater reliance on fossil fuel infrastructure and workforce face additional difficulties in phase out. Social factors such as higher belief in climate change are positively linked with more ambitious coal phase-out efforts. However, disentangling these links for gas remains difficult given the limited historical evidence of phase-down progress. We identify four archetypes (Coal Reliance, Gas Reliance, Limited Policy, and Transition Underway) that illustrate different ways countries have transitioned from coal and gas over time. These provide blueprints for potential future transitions in other countries. Recognizing the diverse social, political, and institutional factors that shape transitions can inform the design of politically relevant future scenarios.

Aquifer Thermal Energy Storage (ATES) is an underground thermal energy storage technology that provides large capacity (of order MWth to 10s MWth), low carbon heating and cooling to large buildings or complexes of buildings, or district heating/cooling networks. The technology operates through seasonal capture, storage and re-use of thermal energy in shallow aquifers, reducing carbon emissions and electricity demand for heating and cooling compared to direct ground- or air-sourced heat pump systems.We demonstrate that ATES could make a significant contribution to decarbonising UK heating and cooling, but uptake is currently very low. We identify eleven low temperature (LT-ATES) systems operating in the UK, with the first having been installed in 2006. These systems currently meet