Future of Power Systems

Innovation Playbook for Future Power Systems

In collaboration with Accenture

A practical resource for decision-makers as power systems evolve under increasing demand.
Scroll to explore innovative solutions and read the report for global insights. 

Explore the solutions

Discover 30+ new case studies added to our growing library of 130+ real-world examples, showcasing how 110 innovative solutions are delivering value across power systems today.  

A nuclear-powered data center combines stable nuclear electricity, high-efficiency computing infrastructure, and recovered thermal energy to support artificial intelligence workloads with lower carbon intensity and higher power reliability. The model connects a data center to nearby nuclear generation so that servers can run on continuous low-emissions power rather than relying only on grid electricity exposed to fossil fuel mix, congestion, or renewable intermittency. It also uses surplus heat or steam from nuclear operations to drive absorption cooling, district cooling, or other thermal systems that reduce the electricity normally consumed by conventional chillers. This matters because artificial intelligence training, cloud computing, and large-scale data storage require dense, uninterrupted power and significant cooling capacity. By pairing nuclear power with intelligent energy management, waste-heat utilization, advanced cooling, and digital operations control, the solution can lower power usage effectiveness, reduce grid stress, improve uptime, and create a scalable pathway for clean, resilient digital infrastructure in regions with existing or planned nuclear assets.Main outcomes of the solutionReliability & Flexibility: The solution gives power systems a stable anchor load supplied by dispatchable, low-emissions nuclear generation. This reduces exposure to intermittent supply, grid congestion, and power quality fluctuations while supporting the continuous electricity profile required by artificial intelligence computing. System Resilience: By locating high-demand computing capacity close to firm generation and using integrated cooling systems, the solution reduces dependence on long-distance grid delivery and backup diesel generation. It strengthens continuity for critical digital infrastructure while easing pressure on transmission and distribution networks. Sustainability: The solution cuts power system emissions by shifting data center demand toward low-emissions electricity and reducing cooling-related electricity consumption through recovered heat or steam. It improves resource efficiency by turning surplus thermal energy into a useful input for data center temperature control.Key deployment and scaling factorsSite the data center close to nuclear generation with sufficient grid interconnection capacity and land-use approvals to reduce transmission constraints and improve supply continuity. Design cooling systems around recovered heat, steam-driven cooling, liquid cooling, or hybrid approaches from the start rather than retrofitting conventional electric chillers after construction. Align nuclear safety, cybersecurity, data protection, emergency planning, and critical infrastructure rules before scaling operations. Use long-term power and heat offtake contracts between the nuclear operator, data center owner, cloud customers, and grid operator to reduce investment risk. Phase new computing capacity with available power, cooling capacity, and local grid reinforcement to avoid creating new system bottlenecks.Case studyQinshan Nuclear Power Plant Co, China National Nuclear Research Institute and China National Nuclear Corporation (2023), ChinaThe Qinshan Nuclear Industry Data Center in Haiyan County, Zhejiang Province, was developed by China National Nuclear Corporation and Qinshan Nuclear Power as a nuclear-powered digital infrastructure platform for China’s nuclear sector. Located inside the Qinshan nuclear power base, the project links high-reliability computing demand with one of China’s established nuclear generation sites, using the operating characteristics of nuclear power - stable output, high availability, and low operational carbon emissions - to support cloud services, artificial intelligence, big data, and nuclear production management systems. The facility was built to national Class A data center standards and planned as a secure, centralized platform for sector-wide digital operations.Public project information states that the center occupies 15,000 square meters of land and follows a one-off planning and phased commissioning model. Its first phase brought four information technology module rooms and 506 standard cabinets into operation, with rapid expansion capacity for 300 additional cabinets and long-term reserved space for 1,200 more. The project demonstrates how nuclear operators can extend the value of nuclear assets beyond electricity generation by using firm clean power to host energy-intensive intelligent computing close to the source. Project-reported outcomes indicate annual clean electricity consumption of more than 175 million kilowatt-hours, annual carbon emissions reduction of 10,675 tonnes, and annual power cost savings of more than USD 3 million. The case shows how nuclear power can underpin resilient, low-carbon data center operations while supporting digital modernization across safety-critical industrial systems.References:The Path to a New Era for Nuclear Energy – Analysis - IEAEnergy supply for AI – Energy and AI – Analysis - IEAExecutive summary – Nuclear Power and Secure Energy Transitions – Analysis - IEAInformation provided to World Economic Forum by Qinshan Nuclear Power Plant Co (May 2026)
Nuclear-powered data center
Qinshan Nuclear Power Co., China National Nuclear Research Institute, CNNC
Load optimization in distribution grids is a software-based operating solution for low- and medium-voltage networks that helps utilities manage growing pressure from distributed energy resources, electric vehicles, heat pumps and more variable demand. It shifts distribution-grid operation away from a model based on limited real-time visibility, conservative planning margins and network reinforcement as the default response to local constraints.  That earlier approach was suited to more predictable one-way power flows and largely passive consumers, but it is less effective in networks with rapidly changing load and voltage conditions driven by distributed and flexible assets. Load optimization software responds by combining grid visibility, power-flow analytics, short-term forecasting and control of flexible assets to show where the network is approaching technical limits and to act before those limits are breached.  In practice, the software ingests data from grid sensors, smart meters, distributed energy resource management systems and operational platforms to detect congestion risks, voltage issues and capacity constraints across feeders and substations. It then uses tools such as demand response, distributed energy resource coordination and dynamic operating limits to shift or shape loads, improve power quality and keep more demand and generation within the safe operating range of the existing network.  This matters because distribution grids are under increasing strain, while conventional reinforcement remains capital-intensive and slow to deliver. By improving the utilization of existing assets and enabling more targeted flexibility, load optimization software can defer avoidable network upgrades, support faster integration of distributed resources and strengthen reliability in a more electrified and decentralized power system. Main Outcomes of the Solution Reliability & Flexibility: Improves distribution-grid reliability by giving operators real-time visibility of emerging congestion, voltage deviations and local capacity constraints, then using forecasting and control of flexible assets to respond before these develop into service or power-quality problems. It also increases system flexibility by coordinating distributed energy resources, demand response and dynamic operating limits in networks with more variable two-way power flows.  Affordability: Lowers system costs by improving utilization of existing grid assets and reducing avoidable reinforcement. By managing peak demand and local bottlenecks through software and flexibility, utilities can defer capital-intensive upgrades and operate the network more efficiently.  Sustainability: Supports faster integration of distributed renewable generation and electrified end uses by creating more usable hosting capacity on existing networks, reducing curtailment risk and enabling cleaner load growth without waiting solely for physical grid expansion. Key implementation and scaling factors Deploy granular visibility at feeder and substation level through network monitoring, smart meters and distributed energy resource telemetry so operators can detect local constraints early enough to intervene effectively. Scale digital systems that can aggregate and remotely coordinate large numbers of distributed devices through interoperable smart meters, management systems and distributed energy resource platforms. Align market rules and incentives so distributed resources can participate in flexibility services and customer behavior can support grid needs. Build planning, investment and operational capability so utilities can embed these tools into routine grid management rather than treating them as stand-alone pilots. Establish secure digital architecture and data governance, as greater connectivity and automation increase cyber and data-management risks. Case study Plexigrid (2022), Sweden In 2022, Plexigrid launched a partnership with distribution system operator Falbygdens Energi Nät in Falköping, Sweden, to use digital grid planning, forecasting and flexibility management to address local connection constraints. The project focused on improving visibility of the distribution network using data from smart meters and other grid data, helping identify where customers could connect earlier through flexibility and better use of existing capacity.  A subsequent Plexigrid case study reported that the approach was used to unlock 20 MW of new photovoltaic capacity, integrated with a utility-scale battery at medium-voltage level and supported by a flexible connection agreement.  The case demonstrates how active distribution-grid management can increase renewable hosting capacity and support new connections without relying solely on conventional network reinforcement. By combining grid visibility, forecasting and flexible connection arrangements, the approach helps utilities make better use of existing infrastructure while maintaining safe operating limits on the local network. References: The Value of Demand Flexibility – Analysis - IEA Smart grids - IEA Falbygden's Case Study: Flexibility in Practice - Plexigrid Falbygdens Energi Nät and Plexigrid cut the waiting time to connect to the grid - Plexigrid Flexibility in Practice: Increasing Renewable Hosting Capacity - Plexigrid 
Load optimisation in distribution grids
Plexigrid, Falbygdens Energi Nät
Geospatial AI platforms combine spatial and engineering data – including geospatial, drone, sensor, CAD and BIM datasets – into unified 2D/3D visual environments for infrastructure planning. In power systems, these platforms can support route selection, substation planning and transmission development by allowing utilities to visualize assets and terrain conditions, test design options and identify potential constraints earlier in the project lifecycle.  By creating a shared digital view across planning, engineering and delivery teams, geospatial AI can improve coordination between stakeholders, reduce reliance on fragmented project data and support faster decision-making during infrastructure development. The platforms can also help utilities identify constructability risks, reduce surveying requirements and improve consistency between planning and execution phase.  Their value increases when deployed across an infrastructure portfolio rather than used on a project-by-project basis. Portfolio-wide access can standardize digital planning capabilities across major grid projects, reduce reliance on bespoke processes and support more consistent decision-making. This is particularly relevant for grid expansion, where utilities need to deliver new connections, substations and transmission upgrades while managing cost, delivery risk and stakeholder complexity. Main Outcomes of the Solution Affordability: Standardized digital planning platforms can reduce engineering and development costs by improving route selection, reducing rework and avoiding late-stage project changes. Institutional Efficiency & Transparency: Structured pilot-to-scale models can improve coordination across procurement, engineering and technology partners, accelerating deployment of tested digital tools across utility organizations. Reliability & Flexibility: Faster and better-informed infrastructure planning can reduce project delays, support grid expansion and improve delivery of substations, transmission upgrades and new connections. Key Deployment and Scaling Factors Define scalability requirements at the pilot stage, including integration needs, governance processes and criteria for enterprise deployment Establish dedicated coordination between innovation, procurement, engineering and operational teams to maintain continuity from pilot to full deployment Standardize digital tool access and procurement across infrastructure portfolios to reduce duplication and improve consistency between projects Integrate geospatial AI tools with existing GIS, CAD and engineering workflows to lower adoption barriers and support operational usability Ensure access to high-quality and interoperable spatial, engineering and asset data to support accurate visualization, modelling and planning decisions Case study National Grid and Sensat (2022–2025), United Kingdom National Grid Partners (NGP) – the corporate venture and innovation arm of National Grid – supported deployment of Sensat’s geospatial AI platform across major infrastructure projects in the United Kingdom. The platform was used within National Grid’s Strategic Infrastructure program to support transmission planning, substation upgrades and grid connections for new data centers. Reported early deployments identified operational benefits including reduced surveying timelines, improved coordination across planning, engineering and delivery teams, and earlier identification of planning and routing constraints. The platform was also integrated with existing GIS, CAD and engineering workflows, supporting adoption within established utility planning processes In December 2025, National Grid’s Strategic Infrastructure team formalized a portfolio-wide agreement extending access to the platform across major electricity infrastructure projects in England and Wales, replacing the previous project-by-project deployment approach. Based on earlier deployments, Sensat reported that the arrangement could reduce development timelines by up to 25% through earlier decision-making and faster stakeholder coordination.  The case illustrates how portfolio-wide deployment models, combined with workflow integration and internal coordination, can support broader adoption of digital planning technologies within regulated utility environments. References:Positioning Pilots to Scale – National Grid Partners National Grid Expands Use of Sensat’s Digital Platform to Support Infrastructure Planning – Sensat National Grid Partners Invests $20M in Startups to Transform the Grid – National Grid US The NextGrid Alliance – National Grid Partners National Grid Partners Commits $100 Million to Invest in AI Startups Advancing the Future of Energy – National Grid US 
Geospatial AI for grid infrastructure planning
National Grid, Sensat
Real-time grid hazard detection via sensors gives utilities earlier visibility into physical, electrical and environmental risks across the distribution network. Instead of relying only on traditional electrical monitoring, customer outage calls or field inspections, utilities can install sensor devices on grid assets such as poles and feeders to detect conditions such as vegetation contact, conductor movement, damaged poles, abnormal vibrations and other emerging hazards. The sensor data is transmitted into operational systems, where it can be validated, prioritized and routed to dispatch teams through existing outage management workflows. This matters because many grid risks begin outside conventional electrical parameters: a tree branch, storm impact or mechanical asset failure may develop before a fault or outage is visible in legacy systems. By reducing the delay between a hazard occurring and operator awareness, utilities can improve safety, target field crews more accurately, reduce manual triage and prevent minor incidents from escalating into outages, equipment damage or wildfire risk. As grids face higher electricity demand, more climate-driven stress and greater reliability expectations, real-time sensor intelligence helps shift grid operations from reactive outage response to proactive hazard management.Main Outcomes of the SolutionReliability & Flexibility: Earlier visibility into faults and emerging risks across distribution networks, improving fault location, reducing manual triage and enabling faster response before issues escalate into wider outages. System Resilience: Stronger ability to identify and respond to hazards such as vegetation contact, damaged poles and storm-related asset stress before they trigger wider failures or outages. Affordability: Lower operating and restoration costs through more targeted maintenance, fewer avoidable truck rolls and better use of existing grid infrastructure.Key Deployment and Scaling FactorsPrioritize deployment on high-risk feeders and assets, especially areas exposed to wildfire risk, storm damage, vegetation contact and recurring outages, where earlier hazard detection delivers the greatest operational value.Integrate sensor intelligence into existing outage management, dispatch and utility operational workflows, because standalone monitoring systems are less likely to support timely operational response.Establish interoperable communications, data standards and cybersecurity protocols, because large-scale deployment depends on secure and reliable integration across utility systems and field devices.Develop alert prioritization and event-classification frameworks that allow operators to manage high volumes of sensor data during storms and other major grid events without overwhelming control-room operations.Ensure communications coverage, power supply resilience and utility data governance frameworks are in place, because real-time hazard detection depends on reliable field connectivity, secure data transmission and integration into utility operational processesAlign deployment with utility regulatory and wildfire-risk management frameworks, because cost recovery, operational approval and data governance requirements can influence large-scale rollout.Case studyGridware and PG&E (2025), USAGridware integrated its Active Grid Response platform with Pacific Gas and Electric Company’s (PG&E) Continuous Monitoring systems to reduce hazard awareness delay across the distribution grid. Gridware worked with PG&E’s information technology and continuous monitoring teams to integrate real-time multi-sensor data into PG&E’s system through application programming interface connectivity.The system processes data from distributed sensor devices through a unified interface and feeds hazard intelligence into PG&E’s Grid Data Analytics & Technology (GDAT) platform.. The Gridware sensors monitor physical, electrical and environmental conditions and formats the resulting data for operational use. Alerts are prioritized according to PG&E safety protocols and delivered to GDAT for use within the utility. GDAT evaluates current grid conditions and creates trouble tickets as appropriate in PG&E’s outage system with hazard type and location details included. Gridware states that the integration reduced time to dispatch by removing manual data entry, validation steps and communication handoffs. The company also reports that the system supported continuous monitoring during storm and fire-weather conditions, helping operators identify and respond to issues earlier. References:Unlocking Smart Grid Opportunities in Emerging Markets and Developing Economies - IEASmart grids - IEAGridware - How PG&E Addresses Hazard Awareness Delay with Gridware AGR Integration with OMS 
Real-time grid hazard detection
PG&E, Gridware
AI for fault detection in high-voltage transmission networks uses machine learning models to analyze operational measurements, protection-system records, sensor data and inspection imagery to identify abnormal patterns associated with emerging equipment faults. The approach supports earlier detection of issues on transmission lines and substations, helping operators prioritize maintenance and respond before faults develop into outages or equipment failures.  Unlike traditional approaches based primarily on scheduled inspections, periodic testing and manual review after alarms or incidents, AI-enabled fault detection supports more continuous and condition-based monitoring. Models can assess large volumes of operational and inspection data, including infrared imagery, to identify deviations from expected asset behavior and improve fault localization and classification.  This is particularly relevant for large transmission systems and ultra-high-voltage corridors, where assets are geographically dispersed and manual inspection is resource-intensive. By improving visibility of asset condition and targeting maintenance more effectively, AI-based fault detection can reduce avoidable outages, lower inspection effort and support more resilient grid operation. Main outcomes of the solution Reliability & Flexibility: Improves security of supply by detecting abnormal conditions early and helping operators locate and classify faults faster, reducing the likelihood and duration of outages and supporting stable operation as the grid becomes more dynamic. System Resilience: Strengthens the ability to withstand and recover from disruptions by enabling faster situational awareness and response, including real-time analytics that can run close to field devices when communications or central systems are constrained. Affordability: Lowers operating cost by shifting from routine, schedule-based inspections to targeted, condition-based work, reducing unnecessary field visits and avoiding expensive unplanned failures through earlier intervention and better asset life management. Key implementation and scaling factors Establish consistent, high-quality operational data streams and asset inspection inputs so models can learn “normal” behavior and detect abnormal patterns reliably. Integrate AI outputs into existing grid operations and maintenance workflows so alerts translate into clear actions, not parallel dashboards. Automate inspection and monitoring programs able to handle very large data volumes – especially images – supported by adequate compute capacity and disciplined data management. Build workforce capability to validate model outputs, maintain performance over time and retain asset knowledge. Embed cybersecurity, access control and system assurance from the start, given the ingestion of sensitive operational data. Case study State Grid’s Electric Power Space Technology Company and North China Electric Power University (2023), China In China, State Grid’s Electric Power Space Technology Company, working with North China Electric Power University, developed an AI-powered infrared defect-identification system for ultra-high-voltage transmission lines. The solution analyzes infrared video and images to detect abnormal heat patterns that can indicate emerging faults, helping operators identify likely problem locations sooner and prioritize field intervention.  The implementation was positioned for operational support during summer peak-demand periods, when system stress and the cost of outages are higher. Reported results include monitoring accuracy above 90%, alongside reduced manual review effort through automated frame extraction and defect identification from inspection videos. In one operational example, reviewing infrared video shifted from several hours of manual screening to a shorter automated workflow from upload to analysis completion, reducing dependence on individual inspector attention and experience.  The case shows how AI-assisted inspection can support condition-based maintenance on long-distance transmission networks. By combining infrared sensing, machine learning and automated image review, the approach can help operators detect emerging defects earlier and focus field resources on higher-risk assets. References:Why edge AI is now crucial for powering the global grid | World Economic Forum IEA Activities on Energy and AI.pdf State Grid develops AI-assisted system to monitor UHV transmission lines - Global Times News - AI technology is used in large-scale heat detection of transmission lines 
AI for fault detection
State Grid Corporation of China, National Institute for Energy Development Strategy, North China Electric Power University
AI-driven operational load forecasting for near-real-time flexibility procurement enables distribution network operators to shift from annual, assumptions-based flexibility planning to daily, data-driven procurement. As distributed energy resources, electric vehicles and heat pumps increasingly connect to low and medium voltage networks, distribution network operators face a growing challenge in transitioning from passive network managers to active system operators.  Flexibility services – in which asset operators shift demand as a paid service – can be a lower-cost alternative to physical network reinforcement. However, because flexibility has traditionally been procured many months in advance, asset operators lack the near-term certainty needed to commit, which limits both participation and the practical value of flexibility for the network. AI-based load forecasting addresses this by ingesting historical load data from network substations, weather forecasts and other input variables to produce day-ahead and week-ahead forecasts at the substation level, including a quantified uncertainty range. This allows operators to define flexibility needs with greater precision and closer to the time of need, when asset operators can more reliably assess and stack their capabilities. It can broaden the pool of eligible participants and make flexibility procurement a more effective operational tool.  The approach also reduces the need for large teams of engineering staff to manually validate data and produce load forecasts, helping automate and scale a process that must expand substantially as low-carbon technologies accelerate connection to low-voltage systems. Main Outcomes of the Solution Affordability: Lower reinforcement and operating costs by enabling distribution network operators to use market-based flexibility as an alternative to physical network upgrades. More accurate forecasting and procurement can improve asset utilization and reduce unnecessary network investment. Reliability & Flexibility: Improved visibility of short-term local network constraints, allowing operators to procure and dispatch flexibility where and when it is needed while supporting the integration of low-carbon technologies such as electric vehicles and heat pumps. Institutional Efficiency & Transparency: Reduced dependency on labor-intensive manual forecasting processes, enabling distribution network operators to scale forecasting capabilities across increasingly distributed and complex networks. Key Deployment and Scaling Factors Build forecasting models on network-based substation load data rather than individual device-level data feeds, reducing privacy concerns, lowering data acquisition costs and limiting dependency on asset owners sharing granular operational data Ensure sufficient data quality, telemetry coverage and weather-data integration, because short-term forecasting depends on reliable historical load data, timely network measurements and relevant external variables Design models to produce both a point forecast and an associated uncertainty range, giving flexibility procurement teams the confidence intervals needed to support operational decision-making Integrate forecasting outputs directly into flexibility tender, dispatch and settlement processes so that asset operators receive clearer, shorter-horizon signals about when and where services are needed Align forecasting-led procurement with regulatory flexibility-market rules, cost-recovery frameworks and system-operator responsibilities, because near-real-time flexibility procurement requires clear rules for when flexibility can substitute or defer reinforcement Share model methodologies and learnings across distribution network operators through industry innovation programs to support replication and avoid duplication of development effort Case study Northern Powergrid and Faculty (Part of Accenture), United Kingdom Northern Powergrid, the electricity distribution company for Yorkshire, the North East and northern Lincolnshire, developed the Artificial Forecasting project with Faculty Science (now part of Accenture), Oak Tree Power and ev.energy, with UK Power Networks participating as a collaborating network. The project was funded under the Ofgem Strategic Innovation Fund Round 2 Discovery phase with a £160,000 project budget and ran for three months from April 2023.  The project developed machine-learning models to produce day-ahead and week-ahead load forecasts at extra-high-voltage to high-voltage and high-voltage to low-voltage substation levels. The models used historical network load data, weather forecasts and associated uncertainty outputs to support operational flexibility procurement closer to real time.  The project aimed to replace an annual, labor-intensive forecasting process with a semi-automated capability that could be run more frequently without requiring engineering staff to manually validate data at every substation. Project documentation stated that forecasting coverage would need to expand by up to 50 times compared with traditional approaches to capture the transformation points where low-carbon technology connections are creating new flexibility and constraint-management requirements.  According to project assessments, the approach could support approximately £51 million in net present value reinforcement cost avoidance through 2033 and up to £60 million in operating cost savings over the following decade by improving the targeting and timing of flexibility procurement. The project documentation also estimated approximately 700 tons of CO2-equivalent emissions reductions associated with peak demand reduction enabled by improved forecasting and flexibility dispatch.  The project further explored how near-real-time forecasting could improve flexibility procurement by allowing distribution network operators to define flexibility requirements closer to the time of need, when asset operators can more reliably assess available capacity and participate in flexibility markets. References:Enabling Northern Powergrid to Optimise Flexible Procurement – Faculty AI Artificial Forecasting Project (NPG_SIF_003) – ENA Innovation Portal New Projects Represent Further £4 Million Investment in Net Zero – UKRI Distribution Flexibility Services Procurement Report 2024–25 – Northern Powergrid 
AI driven operational load forecasting for short-term flexibility procurement
Northern Powergrid, Faculty
AI-enabled nuclear overhaul management uses generative artificial intelligence, industrial knowledge graphs, multi-agent coordination and integrated operational data to manage the full overhaul lifecycle of a nuclear power unit, including planned maintenance shutdowns, inspections and equipment replacement activities. The solution converts fragmented planning, work order, resource, safety and performance data into a coordinated digital workflow covering overhaul planning, preparation, execution and post-overhaul review. These systems can generate optimized schedules, identify work order risks through multi-modal models, forecast workforce and material needs, correct progress deviations during execution and produce post-overhaul analysis reports. Nuclear overhaul performance directly affects unit availability, safety assurance, maintenance cost and power generation output. By turning overhaul management into a data-driven and continuously adaptive process, the solution improves coordination, reduces schedule risk, strengthens operational learning and creates a scalable model for future nuclear maintenance cycles.Main outcomes of the solutionReliability & Flexibility: Improves nuclear unit availability by using AI to optimize overhaul schedules, identify work order risks, forecast resources and correct execution deviations in real time. This supports stable power supply by reducing overhaul uncertainty and improving coordination across maintenance, operations, safety, engineering and procurement teams. System Resilience: Strengthens resilience by turning overhaul management into a continuously monitored, data-driven process. Integrated data lakes, knowledge graphs, secure connectivity and cloud-edge infrastructure help operators detect risks earlier, coordinate responses faster and preserve safety controls during complex maintenance periods. Business Value: Reduces operational risk and improves asset productivity by shortening overhaul cycles, lowering avoidable delay costs and standardizing post-overhaul learning. This creates a repeatable digital management model that can be scaled across nuclear units and future maintenance cycles.Key deployment and scaling factorsBuild a unified industrial data lake that connects overhaul schedules, work orders, safety records, materials, workforce plans, equipment status and historical maintenance data across departments.Validate generative AI and multi-modal models for accuracy, traceability, explainability, human oversight and alignment with nuclear safety requirements before they support operational decision-making.Use industrial knowledge graphs to standardize relationships between assets, work packages, risks, materials and expert rules, allowing the model to be reused across overhaul scenarios and nuclear units.Ensure secure connectivity, cloud-edge infrastructure and mobile field interfaces so progress deviations, resource gaps and safety risks can be detected and corrected during the overhaul rather than after completion.Integrate the system with existing plant maintenance, enterprise asset management, safety, procurement and operational technology systems.Establish cybersecurity, data governance, access control and model governance arrangements appropriate for safety-critical infrastructure.Support adoption across maintenance, operations, safety, engineering, procurement and management teams through clear workflows, training and accountability for AI-assisted decisions.Case studyQinshan Nuclear Power Plant Co; China National Nuclear Research Institute (2023), ChinaIn 2023, Qinshan Nuclear Power Plant Co. deployed an intelligent overhaul management system to improve major overhaul performance across planning, preparation, execution and post-overhaul review. The deployment built on a maintenance digitalization program that began in 2017, during which Qinshan developed a standardized and replicable digital management method for nuclear maintenance. The system integrates generative AI, industrial knowledge graphs, multi-agent collaboration, an industrial data lake, 5G connectivity and cloud-edge-end computing. It uses 18 specialized virtual digital managers across more than 50 maintenance control scenarios, including schedule optimization, work order risk identification, workforce and materials forecasting, deviation correction during execution and automatic generation of post-overhaul reports.The implementation followed a staged digitalization method covering benchmarking, problem diagnosis, integration of management, supervision and execution, business data governance, digital scenario development and iterative innovation. This approach helped replace fragmented, department-based coordination with a closed-loop operating model connecting management oversight, supervisory control and field execution. According to Qinshan’s own estimates, the system reduced overhaul duration by 51.1%, decreased cost by 43% and reduced risk by 82.2%. These figures should be treated as company-reported rather than independently verified. The case illustrates how AI-assisted planning, risk review, deviation correction and post-overhaul analysis can support more coordinated maintenance management in high-complexity nuclear operations. References:Considerations for Deploying Artificial Intelligence Applications in the Nuclear Power Industry | IAEAExecutive Summary – The Path to a New Era for Nuclear Energy – Analysis - IEAInformation provided to World Economic Forum by Qinshan Nuclear Power Plant Co (May 2026)
AI-enabled nuclear overhaul management
Qinshan Nuclear Power Co., China National Nuclear Research Institute
A long-term delivery model for firm, usable clean energy contracts for an agreed output profile – clean electricity delivered when the buyer needs it – rather than paying only for generated megawatt-hours. In practice, the buyer defines delivery expectations such as hourly coverage, ramping needs, seasonal firmness, and performance during critical net-load hours. The supplier then designs and operates a portfolio – typically solar photovoltaics paired with battery energy storage, and sometimes complemented by additional resources – to meet those obligations. This shifts value from “energy produced” to “service delivered,” so the developer must optimise forecasting, dispatch, storage cycling, curtailment strategy, and maintenance to deliver a reliability characteristic, not just annual volume. The contract must include explicit firmness and delivery obligations – with measurable performance metrics, settlement rules, and remedies for under-delivery – otherwise it collapses back into a standard long-term power purchase agreement that can leave the buyer exposed during the hours that matter most. Strategically, this model aligns private procurement with power-system needs by rewarding flexibility and dependable clean capacity, and by creating bankable revenue for configurations that improve system adequacy, not only renewable energy build.Main outcomes of the solutionReliability & Flexibility: By contracting for an hourly delivery profile, the solution rewards dispatchable behaviour (forecasting, scheduled charging and discharging, and fast response) so PV plus battery energy storage is operated to manage variability and meet demand-critical hours rather than maximise annual megawatt-hours.Affordability: Firm-delivery obligations encourage asset optimisation - reducing peak stress, curtailment, and inefficient backup dispatch - which lowers total system costs by shifting value to least-cost flexibility and better utilisation of existing generation and network capacity.Institutional Efficiency & Transparency: A contract built on measurable delivery metrics and settlement rules makes performance auditable and comparable across suppliers, supporting clearer investment signals and procurement discipline that align private contracts with evolving adequacy and flexibility needs.Key implementation and scaling factorsHourly, location-based measurement and attribute tracking must be in place so delivery performance can be verified and settled with low dispute risk.Contracts must define firmness obligations as auditable metrics (coverage hours, tolerance bands, penalty logic and settlement timing) to keep the product distinct from a volumetric power purchase agreement.Market and regulatory frameworks must let storage monetize system value - including stacking revenues where allowed - so firm-delivery offers are investable at scale.Grid connection and operational integration, including forecasting, dispatch coordination and access to flexibility markets, must be secured to reliably deliver the contracted profile under real system constraints.Case studyMorocco’s Agency for Sustainable Energy (MASEN) and Acwa (2025) MoroccoIn 2025, Morocco’s Agency for Sustainable Energy (MASEN) awarded Acwa the Noor Midelt II and Noor Midelt III projects near Midelt. The implementation pairs utility-scale solar photovoltaics generation with co-located battery energy storage systems enabling the delivery of clean electricity beyond daylight hours. Together, the projects comprise approximately 800 MWp of solar PV capacity and around 1.2 GWh of battery storage, with batteries sized to provide several hundred megawatts of discharge capacity for over two hours to enhance grid stability and enable effective management of net-load fluctuations. Contracting is structured to support long-term performance: the projects are developed under a build–own–operate framework with Acwa in partnership with NAREVA Holding (as Moroccan local player). MASEN will act as the offtaker under a 30-year power purchase agreement. Earlier tender requirements for the Noor Midelt solar-plus-storage programme also specified delivery-point performance expectations for net capacity during off-peak hours, signalling a shift toward procuring usable output characteristics, not only energy volumes. References:Supply – Electricity 2025 – Analysis - IEAIEA-PVPS-T16-07-2026-EXEC-SUMM-Firm-Power-2026.pdfThe Value of Demand Flexibility Contracting-Principles-for-Moving-Towards-Location-and-Hourly-Clean-Energy-Matching.pdf24/7 Hourly Matching of Electricity | US EPAhttps://www.pv-magazine.com/2025/08/06/acwa-power-secures-two-solar-plus-storage-projects-in-morocco/https://www.enerdata.net/publications/daily-energy-news/acwa-power-secures-noor-midelt-ii-iii-solarbess-projects-morocco.html
Long-term delivery model for firm/usable clean energy
Moroccan Agency for Solar Energy (MASEN), Acwa
A strategic utility–supplier alliance for scalable grid modernization is a coordinated partnership model in which utilities, grid operators, energy providers and technology suppliers align network modernization needs, standardized equipment deployment and supporting service ecosystems to accelerate the grid upgrades required. Instead of treating equipment procurement, technical specification, installation and maintenance as separate workstreams, the alliance coordinates them through shared planning, standardized technical approaches and longer-term deployment visibility. This gives suppliers clearer signals on demand volume, location and timing, while giving utilities and grid operators more predictable technology availability, standardized designs and delivery capacity for network reinforcement and modernization programs. Historically, grid modernization often progressed as isolated projects with fragmented procurement, limited standardization and limited evidence to de-risk newer technologies. These conditions can slow investment decisions and increase costs. By creating repeatable deployment models across multiple assets, feeders, substations or service territories, the alliance allows suppliers to plan capacity expansion, strengthens supply chains and accelerates learning through shared specifications and installation standards. In parallel, long-term contracts, innovation financing and coordinated vendor ecosystems can support supplier scale-up, while standardized deployment reduces performance and delivery risks during equipment rollout.Main outcomes of the solutionAffordability: Reduces the delivered cost of electrification-enabling infrastructure by shifting from bespoke projects to more predictable deployment pipelines. Standardized designs, higher equipment utilization and coordinated installation schedules lower transaction, engineering and equipment costs. More predictable supplier demand can also reduce procurement risk and support faster, lower-cost rollout of grid components needed to connect new electric loads.Reliability & Flexibility: Supports system reliability by strengthening the distribution and transmission infrastructure needed to integrate new demand and distributed energy resources. Standardized, resilient and safer grid components can reduce outage risk, improve network performance and help utilities accommodate electrification without relying only on bespoke network upgrades.Sustainability: Accelerates emissions reductions by enabling the grid infrastructure required for electrification across industry, buildings and transport. Modernized, safer and more environmentally compatible grid assets can support renewable integration, reduce barriers to network expansion and improve the social and environmental acceptability of overhead-line upgrades.Key implementation and scaling factorsSecure grid connection capacity and permitting early, as grid constraints can delay both new clean electricity supply and new electrification-related load.Develop a coordinated vendor and service ecosystem with clear demand volume, location and timing so equipment manufacturing, installation and maintenance capacity can scale alongside network modernization schedules.Standardize contracting structures and investment-assessment assumptions across projects to reduce transaction costs and shorten the timeline to final investment decisions.Use long-term procurement, framework agreements or innovation-financing mechanisms to give suppliers the demand visibility needed to invest in manufacturing capacity, certification, installation capability and after-sales service.Coordinate grid component deployment with broader electrification plans, including industrial load growth, distributed generation, electric mobility and building electrification, so network upgrades are sequenced ahead of demand growth rather than becoming a bottleneck.Case studyIberdrola i-DE and Birka (2023), SpainIberdrola’s Spanish distribution company, i-DE, collaborated with Birka on the development and deployment of insulating crossarms for overhead power lines in Spain. The alliance combined utility-side technical validation, product development and supplier scale-up, supported by a three-year purchase contract and financing through Iberdrola’s PERSEO innovation program. Birka’s lightweight composite crossarms combine structural strength with electrical insulation and are designed to reduce bird electrocution risk and improve resiliency on distribution networks. Public deployment evidence shows i-DE testing the solution in Burgos in 2023 and later installing Birka crossarms in Olmedo, Valladolid, and Murcia. The case shows how a utility can use long-term procurement, innovation financing and standardized component deployment to strengthen supplier capacity while modernizing overhead lines for safer, more scalable electrification. Qualitative outcomes include improved biodiversity protection, repeatable installation models and reduced environmental risk in grid reinforcement programs.References:Building the Future Transmission GridIberdrola continues to install insulating crossarms manufactured by Birka – Birka CompositesInformation provided to World Economic Forum by Iberdrola (June 2026)
Strategic utility–supplier alliance for scalable grid modernization
Iberdrola, Birka Composites
A collective corporate contribution model for clean energy finance is a business model innovation that mobilizes private sector capital for the energy transition by standardizing a small, voluntary revenue commitment across companies and pooling it into a managed fund directed at clean energy deployment.  The model responds to a practical barrier to corporate participation in clean energy finance: many companies, particularly small and medium-sized enterprises, lack the structures, expertise or scale to deploy climate capital individually. By standardizing the contribution as a fixed share of annual revenue, the model reduces participation to a simple, low-friction commitment that is modest for any individual company but potentially significant at scale.  Contributions are aggregated through a platform that routes pooled funds to delivery partners with a track record in clean energy deployment, including in emerging and developing economies where energy access deficits are greatest. The model is designed to use catalytic capital to attract additional public, philanthropic and private investment, amplifying the total financing mobilized beyond the sum of individual corporate contributions. Main Outcomes of the Solution Sustainability: Channels private sector capital into clean energy deployment, grid modernization and community resilience projects, helping accelerate decarbonization and reduce dependence on fossil-based energy. Business Value: Creates a simple participation pathway for companies seeking to support clean energy finance without developing proprietary investment programs, lowering transaction costs and strengthening the case for collective corporate action. Socioeconomic Impact: Links corporate climate participation to energy access, job creation and community resilience by directing pooled capital toward clean energy projects in emerging and developing economies. Key Deployment and Scaling Factors Set the contribution threshold at a level that is financially manageable for businesses of different sizes, while remaining meaningful when aggregated across many participants. Partner with delivery organizations that have a demonstrated track record in clean energy finance, project deployment, blended finance and co-investment mobilization. Simplify participation through a single platform sign-up and annual contribution, reducing the need for companies to manage project selection, due diligence or deployment directly. Establish transparent fund governance, impact reporting and assurance mechanisms so participating businesses can understand how funds are allocated and what outcomes are delivered. Build visible networks of participating businesses to create social proof and peer momentum, particularly among SMEs where collective action norms can support adoption. Direct pooled funds toward markets where catalytic capital can address financing gaps and support both decarbonization and socioeconomic outcomes. Ensure claims made by participating companies are aligned with credible climate disclosure and anti-greenwashing guidance, especially where contributions are used in sustainability reporting. Case study Point One and Global Energy Alliance for People and Planet (2026), Global Point One launched in March 2026 as a global platform mobilizing private sector finance for clean energy by inviting businesses to commit 0.1% of annual revenue through a single platform sign-up. The initiative launched with more than 30 participating businesses and a target of raising at least $200 million by 2030, with the aim of attracting up to $3 billion in additional institutional investment.  The platform’s inaugural delivery partner is the Global Energy Alliance for People and Planet (GEAPP), founded in 2021 by The Rockefeller Foundation, IKEA Foundation and Bezos Earth Fund. GEAPP’s 2025 impact report states that its partnership model has unlocked $7.8 billion across 137 projects in more than 30 countries and is on track to improve energy access for 91 million people, support or create 3.1 million jobs and livelihoods and prevent 296 million tons of carbon emissions. The Alliance’s blended finance model has demonstrated the ability to unlock up to $15 in additional co-investment for every $1 contributed through first-loss structures and public-private-philanthropic partnerships designed to reduce risk for institutional investors. Point One’s model is built around a leverage mechanism. According to Point One, analysis by Global Nation suggests that allocating 0.1% of global business revenue could help finance a clean power transition by 2040, assuming each business dollar attracts at least $1.50 in additional public and private funding. Point One also cites GEAPP’s blended finance track record as evidence that catalytic capital can mobilize additional investment, though these projections should be treated as model-based estimates rather than realized outcomes.  A Point One survey of SME decision-makers in the UK and US, conducted by Survation in January 2026, found that 69% were comfortable with a contribution level equivalent to 0.1% of revenue or higher, while 79% supported a simple collective clean energy and climate effort. These findings indicate potential demand for a standardized corporate contribution model, while actual scale will depend on sustained business participation, transparent reporting and the ability of delivery partners to convert pooled contributions into bankable clean energy projects. References:Point One – Almost Nothing Changes Everything Point One Launches with Businesses to Help Close the Clean Energy Funding Gap – Global Energy Alliance for People and Planet Point One Launch Press Release – PR Newswire Global Energy Alliance for People and Planet: 2025 Impact Report 
Collective corporate revenue contribution model for clean energy finance
Point One, Global Energy Alliance for People and Planet
Modular direct air capture is an engineered carbon removal approach that uses repeatable, factory-built units to extract carbon dioxide directly from ambient air, rather than from a concentrated industrial exhaust stream. In a typical solid-sorbent system, fans move air through materials that selectively bind carbon dioxide at lower temperatures; the captured carbon dioxide is then released when the material is heated, producing a concentrated stream that can be permanently stored underground or used in products such as low-carbon concrete, synthetic fuels or industrial materials.  The modular design matters because direct air capture remains early in commercial deployment and needs faster manufacturing, lower project execution risk and easier technology upgrades to reach meaningful scale. By using standardized modules that can be connected in arrays, developers can add capacity incrementally, replicate project designs across sites and replace key components as capture materials improve. The approach also enables co-location with renewable generation, energy storage, industrial facilities and carbon transport or storage infrastructure, helping developers integrate direct air capture into broader low-carbon industrial and energy systems.  The model’s commercial importance lies in turning a capital-intensive climate technology into a repeatable infrastructure platform. Scale can come from manufacturing learning, portfolio deployment, technology upgrades and long-term offtake contracts rather than one-off project delivery.  Main Outcomes of the Solution Business Value: Creates a more bankable pathway for carbon removal by shifting project delivery from bespoke infrastructure builds to repeatable, manufactured assets. Long-term offtake contracts can improve investment certainty for projects located near low-carbon power, heat, carbon transport, storage infrastructure or industrial users. Sustainability: Supports decarbonization by creating a route to remove residual emissions that are difficult or costly to eliminate directly. Its climate value depends on using low-carbon electricity and heat, minimizing lifecycle emissions and pairing captured carbon dioxide with verified durable storage or use pathways. Key Deployment and Scaling Factors Locate DAC projects where low-carbon electricity, clean heat, or waste heat can support operations without increasing system emissions.  Standardize modular units to reduce delivery risk by enabling repeatable installation, phased expansion, and factory-based production.  Allow component and sorbent upgrades so that projects can improve performance without replacing the full asset.  Capture capacity paired with verified storage or utilization pathways to ensure that removed carbon dioxide delivers durable climate value.  Long-term offtake contracts, public procurement, tax credits, and hub-based infrastructure to improve bankability and attract capital for scale-up. Case study CarbonCapture and True North Carbon (2025), Canada In 2025, CarbonCapture, through its Canadian subsidiary True North Carbon, deployed the Tamarack direct air capture project at Deep Sky Alpha near Innisfail, Alberta. The project reached first capture on 22 October 2025 and is described by the company as Canada’s largest single-technology direct air capture deployment. Tamarack is designed to remove up to 2,000 tons of carbon dioxide from the atmosphere each year using modular direct air capture equipment powered by solar energy.  The implementation demonstrates a phased deployment model in which standardized capture modules are installed at a shared carbon removal facility, rather than developed as a stand-alone bespoke plant. The project is expected to generate the company’s first carbon removal credits and includes local supplier participation, community engagement and education programs with local and Indigenous communities. These project benefits should be treated as company-stated expectations until independently verified outcomes are available. References:Direct Air Capture - Energy System - IEA CarbonCapture | World Economic Forum DOE Explains...Direct Air Capture | Department of Energy Newsroom | Carbon Capture Inc. True North Carbon's Tamarack Project, Canada's Largest Single-Technology Direct Air Capture Deployment, Achieves First Capture in Innisfail, Alberta 
Modular direct air capture (DAC)
CarbonCapture
A shared storage capacity leasing model turns battery storage from a project-specific compliance asset into a tradable, system-facing flexibility resource. In this structure, an independent storage operator can buy electricity when market prices are low, sell electricity when prices are high, and receive compensation for maintaining dependable discharge capacity. At the same time, charging electricity can be exempted from selected grid-related charges, including transmission, distribution, and certain government surcharges, which improves operating economics and makes market-based dispatch more viable.  The leasing element allows storage capacity to be registered centrally and contracted across an entire province, enabling renewable generators or other eligible counterparties to lease capacity instead of building dedicated batteries at each site. This matters because it stacks multiple value streams into a single business model – energy arbitrage, capacity compensation, and leasing revenue – while widening access to storage services beyond the owner of the asset.  Strategically, the model supports a more efficient build-out of flexibility: storage can be deployed where it is most valuable to the power system and  then shared through market arrangements rather than duplicated project by project. As electricity systems absorb more variable renewable generation, this kind of market design helps align storage investment with both commercial incentives and system reliability needs. Main outcomes of the solution Affordability: Shared storage lowers total system cost by allowing multiple users to access a single storage asset instead of building separate project-specific systems. Exempting certain charging-related grid costs and combining leasing, market trading, and capacity payments also improves asset utilization and reduces the cost of deploying flexibility at scale.  Reliability & Flexibility: The model increases dispatchable flexibility by enabling storage to respond to spot market conditions and by compensating available capacity. This supports balancing of variable wind and solar generation, peak demand management, and overall system operability.  Institutional Efficiency & Transparency: Central registration and province-wide leasing through the electricity trading center create a structured framework for sharing storage. Defined participation and transaction rules support a more transparent and organized market for storage services. Key implementation and scaling factors Allow storage to participate directly in electricity markets as an independent asset class. Combine spot market revenues, capacity payments, and leasing income to improve commercial viability. Establish central registration and province-wide leasing mechanisms to enable efficient sharing of storage capacity. Exempt charging electricity from selected transmission, distribution, and surcharge components to improve project economics. Align storage deployment with renewable expansion and system flexibility needs to support efficient scaling. Case study Shandong Provincial Development and Reform Commission, Shandong Provincial Energy Administration, and the National Energy Administration’s Shandong Regulatory Office (2022), China In Shandong Province, China, the shared storage capacity leasing model was introduced through a provincial policy framework issued by the Shandong Provincial Development and Reform Commission, the Shandong Provincial Energy Administration, and the National Energy Administration’s Shandong Regulatory Office.  The framework allows independent energy storage to participate directly in electricity markets by purchasing electricity to charge and selling electricity back to the grid when discharging. It introduced a two-part compensation structure combining energy market revenues with capacity payments and requires storage capacity to be centrally registered through the provincial electricity trading center for leasing across the province by renewable energy companies.  For designated demonstration projects, monthly available-capacity compensation was set at up to twice the standard level applied under prevailing market rules. The policy also exempts eligible charging electricity from selected transmission, distribution, and government surcharge costs. In practice, this framework establishes a structured approach for transitioning storage from a project-specific asset to a shared provincial flexibility resource with multiple revenue streams and formal market participation.   Solution and insights developed with contributions from CNESA. References: Shandong Introduced China's First Energy Storage Support Policy in Electricity Spot Market — China Energy Storage Alliance  
Shared storage capacity leasing model
Shandong Provincial Development and Reform Commission (Shandong DRC), Shandong Provincial Energy Administration, National Energy Administration (NEA) Shandong Regulatory Office
Load flexibility frameworks define how electricity consumers can adjust demand in response to grid needs, price signals or operational constraints. Rather than treating demand as fixed, these frameworks classify flexible load according to practical performance characteristics such as response speed, duration, availability, frequency of activation, ramping capability and depth of demand reduction or shifting. This gives system operators, utilities, aggregators and customers a common basis for assessing how load can contribute to reliability, balancing, congestion management and peak reduction. The framework is particularly useful for large new loads, such as industrial facilities, data centers, hydrogen production, EV charging hubs or electrified heating systems, where grid connection queues can be delayed by local capacity constraints or uncertainty over reinforcement needs. By defining when and how a customer can reduce or shift demand, utilities can evaluate flexible connection options, staged energization or non-firm service arrangements more consistently. This can help identify existing grid headroom, reduce repeated bespoke studies and support more consistent connection decisions while maintaining system reliability. By translating customer-side flexibility into standardized operating terms, the framework helps integrate demand-side resources into grid planning and operations. When properly measured and governed, flexible loads can reduce stress on the grid, improve use of existing network capacity and support higher renewable integration without relying only on generation-side capacity or network reinforcement.  Main Outcomes of the Solution Reliability & Flexibility: Enables demand-side resources to support grid needs such as peak reduction, congestion management, balancing and frequency response. Affordability: Improves utilization of existing grid assets by using flexible demand to defer or reduce the need for immediate network reinforcement or generation capacity. Institutional Efficiency & Transparency: Creates common definitions, performance expectations and participation rules for utilities, system operators, aggregators and customers. Key Deployment and Scaling Factors Standardize flexibility definitions covering response speed, duration, availability, frequency of activation, ramping capability and depth of demand adjustment. Establish measurement and verification methods to confirm that flexible load performs as expected during grid events or market participation. Integrate flexibility into planning, connection, market and operational processes so demand-side resources can be evaluated alongside supply-side and network options. Develop customer participation models that reflect different load types, including industrial facilities, commercial buildings, data centers, EV charging and thermal loads. Define clear commercial, regulatory and operational arrangements covering incentives, obligations, dispatch rights, data sharing and reliability requirements. Case study Electric Power Research Institute DCFlex Flex MOSAIC Framework (2026), United States EPRI’s DCFlex Flex MOSAIC™ Framework provides a standardized, technology-neutral way to describe and evaluate flexibility from large loads, particularly data centers. It defines flexibility through common performance characteristics such as notification time, duration, frequency of use, load adjustment depth, ramp behavior and availability. This gives utilities, system operators, regulators and developers a shared basis for assessing whether flexible demand can support grid connection and operation. The framework addresses a common interconnection challenge: large loads are often studied as bespoke projects, which can increase uncertainty, study time and cost. By classifying flexibility against grid needs such as congestion relief, peak reduction, balancing and frequency response, the framework helps identify where existing grid headroom can be used more effectively without compromising reliability. The case demonstrates how standardized flexibility definitions can support more consistent connection decisions for large electricity users. It also provides a foundation for voluntary flexible-load programs, allowing data centers and other large loads to participate in grid support while preserving a pathway toward firmer service over time. References:Flex MOSAIC™ | DCFlex EPRI Launches Flex MOSAIC to Reduce ‘Time to Power’ for Data Centers | FinancialContent 
Load flexibility framework for grid planning, operations and large-load connection
Electric Power Research Institute (EPRI)
Interconnection queue reform changes how grid operators study and approve new generation or storage projects seeking grid connection. Instead of processing projects one by one under a serial, first-come, first-served queue, projects are grouped into study cycles and assessed together based on network impact, readiness, site control, technical completeness and financial commitments. This helps system operators focus engineering resources on projects that are more likely to proceed, while reducing the risk that speculative or incomplete projects occupy queue capacity and delay more viable resources.  The model typically introduces defined application windows, validation phases, phased impact studies and decision points where developers must either post additional security, meet readiness requirements or withdraw before costs and commitments escalate. By moving toward a first-ready, first-served approach, interconnection reform can improve queue discipline, shorten review timelines, increase transparency for developers and support faster integration of new resources needed for reliability, electrification and renewable growth.  Main Outcomes of the Solution Reliability & Flexibility: Accelerates connection of generation and storage resources needed to meet demand growth, improve resource adequacy and support changing system needs. Affordability: Reduces time and cost spent studying speculative or less-ready projects, while improving visibility on network upgrade needs and cost allocation. Institutional Efficiency & Transparency: Creates clearer queue rules, readiness requirements, study phases and developer decision points, improving predictability for system operators, regulators and project developers. Key Deployment and Scaling Factors Clustered study process that groups projects into defined application cycles rather than processing each project individually. Readiness requirements covering site control, technical data, deposits and other evidence that projects are capable of advancing. Phased study design, including early system-impact screening, detailed impact analysis and facilities-level assessment. Digital tools and standardized application review processes to manage high volumes of interconnection data and reduce manual review bottlenecks. Clear rules for withdrawal, restudies and network-upgrade cost allocation to reduce late-stage uncertainty for developers and system operators. Case study PJM Interconnection (2026), United States PJM’s first full reformed interconnection queue cycle demonstrates a cluster-based, first-ready, first-served model for processing new generation and storage projects. The reform replaces PJM’s earlier serial, first-come, first-served queue approach with a clustered cycle process designed to screen project readiness earlier and prevent less-viable projects from clogging the queue. The validation phase confirms whether applicants have submitted required technical and financial information, including site control and readiness commitments, before qualified projects move into clustered system-impact studies. In April 2026, PJM announced that 811 new generation projects, representing around 220 GW of capacity, had applied to connect through the first cycle of its reformed process. PJM described this as the first major intake under a redesigned approach focused on improving the certainty, speed and discipline of project review. PJM’s Cycle 1 applications include a diverse mix of resources, including storage, gas, nuclear, solar, wind, hybrid, hydro and other projects.  The case shows how interconnection queue reform can create a more structured pathway for new resources to connect to the grid. PJM expects projects in the cycle to be reviewed through a multi-stage process, with phased studies and decision points running into 2028. The reform is particularly relevant as PJM faces rising demand from data centers, advanced manufacturing and electrification, making timely interconnection of new supply critical for reliability. References:  PJM’s First Reformed Queue Cycle Draws 811 Projects, 220 GW FERC Conditionally Accepts PJM Interconnection Reforms for a First-Ready, First-Served Clustered Cycle Approach | Washington Energy Report 
Interconnection queue reform
PJM Interconnection
Cap-and-floor investment frameworks support long-duration electricity storage by reducing revenue uncertainty while preserving market-based operation. Under this model, storage assets continue earning revenues from electricity market activities, but a regulated floor provides minimum revenue protection if market earnings fall below an agreed threshold, while a regulated cap limits excess revenues and can return upside value to consumers. This creates a clearer investment case for storage assets that have high upfront costs, long development timelines and uncertain future market revenues. Long-duration electricity storage can provide flexibility over extended periods, helping shift surplus renewable generation, manage periods of low renewable output, support security of supply and reduce reliance on fossil-fuel-based backup or balancing resources. However, many long-duration technologies face financing barriers because their full system value is not always captured through short-term wholesale or ancillary service markets. A cap-and-floor framework helps address this gap by creating a bankable revenue structure, while still incentivizing projects to operate efficiently in the market. Main Outcomes of the Solution Business value: Improves investor confidence and project bankability by creating minimum revenue protection while allowing upside from market operation. Reliability and flexibility: Supports investment in long-duration storage assets that can provide extended balancing, system flexibility and security-of-supply benefits. Affordability: Protects consumers through a revenue cap and helps unlock storage that can reduce wider system costs by improving renewable integration and reducing reliance on more expensive balancing options. Key Deployment and Scaling Factors Clear eligibility criteria defining qualifying long-duration storage technologies, minimum duration requirements and project readiness conditions. Transparent cap-and-floor revenue design, including how the cap, floor, rates of return and revenue assessment periods are calculated. Robust project assessment process covering eligibility, project cost assessment, cost-benefit analysis and consumer value. Consumer protection mechanisms ensuring that excess revenues above the cap are returned and that floor payments are justified. Defined institutional roles for government, regulator, system operator and project developers, including licensing, cost recovery, monitoring and reporting arrangements. Case study Ofgem Long Duration Electricity Storage Cap-and-floor Scheme (2025), United Kingdom Ofgem is implementing a cap-and-floor scheme to enable investment in long-duration electricity storage while protecting consumers. Under the model, Ofgem sets a minimum and maximum level of revenue for an LDES project: when revenues fall below the floor, consumers top up the difference through energy bills; when revenues exceed the cap, the extra money is returned to consumers through reductions in network charges. Ofgem states that this model encourages investment by guaranteeing revenues that can offset high upfront costs and market uncertainty for LDES projects.  The first application window is structured around two streams. Stream 1 applies to more mature technologies, requiring at least 100 MW of capacity and TRL 9. Stream 2 supports less mature technologies, requiring at least 50 MW of capacity and TRL 8. Both streams must be able to discharge continuously at full power for at least eight hours. The scheme responds to a clear system need: Ofgem identifies an indicative capacity range of 2.7–7.7 GW of additional LDES required by 2035, based on NESO advice.  The case shows how a regulated investment framework can improve bankability for long-duration storage, while using eligibility thresholds, cost-benefit analysis and consumer-protection mechanisms to support system flexibility and security of supply. Reference: Long duration electricity storage | Ofgem Long Duration Electricity Storage: Technical Decision 
Cap-and-floor investment framework for long-duration electricity storage
Office of the Gas and Electricity Markets (OFGEM)
Green electricity market platforms create dedicated trading channels for buyers and sellers of renewable electricity. Instead of relying only on bilateral contracts or long-term procurement, these platforms allow renewable energy to be traded through organized power exchanges across short-term and forward time horizons. Buyers can procure green electricity to meet sustainability targets or renewable purchase obligations, while renewable generators gain an additional route to market. These platforms improve price discovery for renewable electricity by allowing participants to submit bids based on quantity and price, and by clearing green electricity separately or alongside conventional market segments. They can also support flexible procurement across day-ahead, intraday, weekly or monthly products, helping buyers manage demand variation while enabling renewable generators to sell surplus output. When linked to renewable purchase obligations or certificate systems, green electricity market platforms can increase transparency, improve liquidity and create clearer investment signals for renewable generation. Main Outcomes of the Solution Sustainability: Accelerates renewable electricity consumption by creating dedicated market channels for green power procurement. Affordability: Improves price discovery and procurement flexibility, helping buyers access renewable electricity through competitive market mechanisms. Institutional efficiency and transparency: Strengthens renewable power markets through clear trading rules, exchange-based procurement and linkage with renewable compliance mechanisms. Key Deployment and Scaling Factors Dedicated green electricity trading segments within existing power exchanges. Clear eligibility rules defining which renewable resources and buyers can participate. Integration with renewable purchase obligations, renewable energy certificates or other compliance frameworks. Multiple contract horizons, including day-ahead, intraday, weekly and monthly products, to support buyer flexibility. Transparent market clearing, settlement and reporting processes to build confidence among generators, buyers and regulators. Case study Green Term Ahead Market and Green Day Ahead Market (2020), India India introduced dedicated green electricity trading products to deepen the market for renewable electricity and provide competitive price signals. The Green Term Ahead Market has operated since August 2020, while the Green Day Ahead Market has been available since October 2021 as an additional market segment for renewable electricity trading. The Ministry of Power states that GTAM and GDAM enable procurement of green energy through power exchanges, which can be counted toward renewable purchase obligations. The GDAM allows participants to submit quantity and price bids for renewable electricity, with price discovery carried out before the conventional day-ahead market. Buyers can claim renewable purchase obligation credit for power purchased through GDAM, while renewable energy sellers cleared in the conventional day-ahead market may claim renewable energy certificates. The Ministry reported that total GDAM volume from October 2021 to January 2023 reached 4,186.64 million units. The Green Term Ahead Market provides additional flexibility by allowing renewable electricity trading across intraday, day-ahead contingency, daily, weekly, monthly and any-day single-sided contracts, with procurement available up to 90 days. The Ministry reported that GTAM volume from August 2020 to January 2023 reached 8,509.49 million units. The case shows how dedicated green power exchange products can improve renewable electricity procurement, create clearer market signals and support compliance with renewable purchase obligations. References: Press Release:Press Information Bureau IEX | Indian Energy Exchange 
Green electricity market platform for renewable power procurement
Indian Energy Exchange
Revenue underwriting schemes support investment in renewable generation and clean dispatchable capacity by reducing downside revenue risk for project developers. Under this model, eligible projects compete through tenders for long-term underwriting agreements that provide a revenue safety net if market revenues fall below an agreed level, while allowing government or consumers to share in upside returns when project revenues exceed agreed thresholds. This improves bankability for capital-intensive clean energy assets while retaining exposure to market signals. The mechanism is particularly useful where electricity systems need rapid build-out of renewables, storage and other dispatchable clean capacity, but investment is slowed by merchant price uncertainty, financing constraints, grid connection risk or policy uncertainty. By using competitive tenders, governments can target specific capacity volumes, technologies or locations, while selecting projects based on price, deliverability, system value and broader community or economic criteria. [1],[2] Main Outcomes of the Solution Business value: Improves investor confidence and project bankability by providing long-term revenue certainty for renewable generation and clean dispatchable capacity. Sustainability: Accelerates deployment of low-carbon electricity resources needed to replace fossil-fuel generation and support clean power targets. Affordability: Uses competitive tenders and revenue-sharing design to reduce investment risk while limiting public exposure and returning upside value when projects perform strongly. Key Deployment and Scaling Factors Design competitive tenders with clear eligibility rules, bid evaluation criteria and capacity targets for renewable generation and clean dispatchable resources. Define long-term underwriting contracts with revenue floors, upside-sharing arrangements, payment triggers, settlement methods and payment caps. Integrate procurement with system planning so contracted projects support reliability, renewable integration and grid needs. Include deliverability requirements covering grid connection, land access, permitting, financing, community engagement and project timelines. Maintain exposure to market signals, including operational incentives around dispatch, availability and negative pricing. Establish transparent governance and cost-recovery arrangements to balance investor confidence, consumer protection and public accountability. Case Study Australian Government Capacity Investment Scheme (2023), Australia Australia’s Capacity Investment Scheme is a revenue underwriting scheme for renewable generation and clean dispatchable capacity. Successful projects receive long-term agreements with an agreed revenue floor and ceiling, reducing downside market risk while preserving exposure to wholesale market signals. If project revenue falls below the floor, government support covers part of the shortfall; if revenue exceeds the ceiling, the project shares upside revenue with the government.  The underwriting design also accounts for negative price exposure. Tender guidance describes a zero-price floor approach for net revenue calculations, where payments linked to negative prices are excluded from the calculation. This helps avoid treating negative-price periods as lowering annual revenue for underwriting purposes, while retaining incentives for projects to manage dispatch and contracting exposure. The scheme was expanded on 29 July 2025 from 32 GW to 40 GW of new capacity nationally. The target increased from 23 GW to 26 GW of renewable generation capacity and from 9 GW to 14 GW of clean dispatchable capacity, with the expanded scheme to be rolled out from 2024 to 2027.  The case shows how revenue underwriting can reduce investment risk while maintaining competitive discipline. By combining a revenue floor, ceiling and tender-based allocation, the scheme improves bankability for clean energy projects while helping Australia accelerate renewable generation and dispatchable capacity deployment. [1],[2] References: https://www.dcceew.gov.au/energy/renewable/capacity-investment-scheme  The Capacity Investment Scheme 
Revenue underwriting scheme for clean energy investment
Department of Climate Change, Energy, the Environment and Water of Australia
Balancing market platforms enable system operators to procure reserve and ancillary services through transparent, competitive market mechanisms. Instead of relying only on administratively procured reserves or bilateral arrangements, qualified resources bid into standardized products that reflect system needs such as frequency control, supply-demand balancing, renewable forecast-error management and contingency response. These resources can include generation, pumped hydro, battery storage, self-generation and demand response, provided they meet technical performance requirements. As renewable penetration increases, balancing needs become more dynamic because system operators must manage larger forecast errors, shorter-term variability and unexpected changes in supply or demand. A dedicated balancing market helps create clearer price signals for flexibility, expands participation beyond conventional generators and can improve the efficiency of reserve procurement. By defining products according to response time, duration and operational purpose, the platform allows different technologies to compete to provide the flexibility services most suited to their capabilities. Main Outcomes of the Solution Reliability and flexibility: Supports frequency control, supply-demand balancing and contingency response by procuring reserve products with defined technical requirements. Affordability: Can improve procurement efficiency by allowing multiple resource types to compete to provide balancing services, helping reduce reserve procurement costs where markets are liquid and well designed. Institutional efficiency and transparency: Creates standardized products, market rules and published trading information that improve transparency and neutrality in reserve procurement. Key Deployment and Scaling Factors Define balancing products clearly, including response time, duration, activation conditions, locational requirements and performance standards. Establish resource-neutral participation rules that allow generation, storage, self-generation and demand response to compete if they meet technical requirements. Develop market systems for bidding, clearing, dispatch, settlement, performance assessment and publication of trading results. Coordinate balancing market operation with transmission system operation, renewable forecasting, congestion management and imbalance settlement. Set transparent governance, monitoring and penalty arrangements to maintain market confidence and reliable delivery of contracted reserves. Ensure telemetry, metering, communications and cybersecurity standards are sufficient for near-real-time activation and verification. Align market rules with broader system planning and investment signals so flexibility providers can build credible revenue stacks.  Case study Electric Power Reserve eXchange Balancing Market (2024), Japan Japan’s balancing market is used by general transmission and distribution operators to procure reserves for frequency control and supply-demand balancing. The market was established in April 2021 as part of broader electricity market reform to support cross-regional procurement of balancing capacity, improve transparency and reduce adjustment costs through competition.  The market was introduced in phases. Tertiary reserve 2 began in fiscal year 2021, tertiary reserve 1 in fiscal year 2022 and, from fiscal year 2024, primary reserve, secondary reserve 1 and secondary reserve 2 were introduced, enabling market trading across all five product categories. Participation can include thermal generation, pumped hydro, battery storage, self-generation and demand-response providers that meet technical requirements.  EPRX began operating the balancing market from April 2024, taking over from the earlier structure operated by Japan’s general transmission and distribution operators. Its role is to support stable, transparent and neutral market operation as the importance of balancing procurement grows with renewable integration and power-market reform.  The case shows how a national balancing market can create a structured route for multiple flexibility resources to provide reserve services. By fiscal year 2024, Japan’s balancing market had introduced all five reserve product categories, covering response times from fast primary reserves to slower tertiary reserves. The market framework expanded participation beyond conventional thermal generation to include batteries, pumped hydro and demand-response providers, supporting more flexible system operation as renewable penetration and short-term balancing needs increase. References: 一般社団法人 電力需給調整力取引所 Organization Overview | About the Electricity Supply and Demand Adjustment Exchange | Electricity Supply and Demand Adjustment Exchange 
Balancing market platform for reserve and ancillary services
Electric Power Reserve eXchange (EPRX)
Energy savings insurance and standardized investment models make it easier to finance energy efficiency upgrades by addressing a long-standing concern – uncertainty about whether predicted savings will be delivered. In the past, many projects relied mainly on contractual performance guarantees from contractors, which could still leave building owners and lenders exposed if results were disputed, difficult to verify, or dependent on a single company’s financial strength. This solution improves on that approach by adding two practical elements. First, an insurance policy can protect the customer if agreed savings are not achieved, shifting performance risk to an insurer that is structured to manage it. Second, a standardized package – including shared contract terms, a consistent method to estimate and verify savings, and independent validation – makes projects easier to compare and assess. Together, these elements reduce uncertainty and administrative complexity, which can lower financing barriers and help smaller projects attract capital. A key benefit is repeatability: once the model is established, similar upgrades can be deployed across multiple sites and organizations with faster approvals and more predictable outcomes. Main outcomes of the solution Affordability: By making energy efficiency upgrades easier to finance and replicate, the solution accelerates demand reduction and peak load relief. This can lower system operating costs and help moderate consumer bills through reduced energy consumption and deferred network investment. Business Value: By shifting performance risk from customers and lenders to an insurance-backed structure, the solution improves bankability and enables portfolio financing. This can increase private capital participation and make returns more predictable for efficiency investments. Institutional Efficiency & Transparency: By using standardized contracts and consistent validation of savings – typically based on recognized measurement and verification protocols – the solution reduces negotiation and due diligence time, strengthens trust in reported results, and supports faster and more consistent approvals across projects and markets. Key implementation and scaling factors Standardize documentation and calculation methods so projects use repeatable contracts and consistent savings estimation and verification, reducing transaction costs and enabling replication. Require independent technical validation to confirm baselines, assumptions, and expected savings before financing and insurance attach, reducing disputes and increasing lender confidence. Develop clearly defined energy savings insurance products with transparent triggers, roles, and remedies, improving financeability and reducing risk premiums. Build bank and investor readiness through consistent project data packs and underwriting guidance so financiers can evaluate projects without bespoke engineering reviews. Enable pipeline aggregation through common eligibility criteria and reporting standards so smaller projects can be bundled into portfolios and financed at scale. Case study Inter-American Development Bank (IDB) and partners including BASE (2013), Global In 2013, the Inter-American Development Bank (IDB), together with partners including BASE – the Basel Agency for Sustainable Energy – developed the Energy Savings Insurance (ESI) model to unlock investment in energy efficiency by reducing the risk that projected savings are not delivered. In Europe, the model has been implemented in Italy, Portugal, and Spain through initiatives including the GoSafe with ESI program. These deployments combine standardized contracts adapted to national legal frameworks, independent validation of expected savings, and participation from local insurers and financial institutions. European implementation has also incorporated digital project management and reporting tools to improve traceability of project performance and reported savings. Building on early implementation experience, the approach has expanded to additional markets, supporting wider replication of the same financing structure. Reported results from early deployments indicate that the model can support measurable reductions in energy use for participating enterprises while helping build a growing pipeline of financed projects and mobilized investment. This helps shift energy efficiency upgrades from isolated transactions toward repeatable programs capable of scaling across multiple sectors. References: WEF_Industrial_Electrification_2024.pdf Financing Energy Efficiency – Energy Efficiency Policy Toolkit 2025 – Analysis - IEA Energy Savings Insurance (ESI) Europe | BASE Energy Savings Insurance: Advances and Opportunities for Funding Small- and Medium-Sized Energy Efficiency and Distributed Generation Projects in Chile Energy Savings Insurance: Building Trust in Energy Efficiency Investments | BASE 
Energy savings insurance and standardized investment models
Inter-American Development Bank, BASE
A regulated innovation funding framework for energy network transformation is a structured, publicly administered mechanism that channels ring-fenced funding from network price controls into the development and demonstration of new technologies, services and business models needed to decarbonize electricity and gas networks.  Rather than relying on utilities to self-fund innovation or on commercial markets to develop early-stage solutions, the framework creates a dedicated pathway through which energy network companies partner with innovators and apply for staged funding against published challenge areas. Projects typically progress through phases such as feasibility, proof of concept and large-scale demonstration, with funding increasing at each stage and fewer projects advancing.  The framework is designed to address a persistent market failure: transformational energy infrastructure innovation is often too risky and too long-cycle for private capital alone, while the changes required for net zero are too urgent to leave to incremental network investment. By embedding innovation funding within the regulatory price control, the mechanism strengthens accountability to consumers, requires measurable net benefit and creates a replicable program structure that can be extended, scaled and refined across successive price control periods. Main Outcomes of the Solution Sustainability: Accelerates the development and deployment of technologies needed to decarbonize electricity and gas networks, including whole-system integration, network flexibility, electrification and low-carbon energy infrastructure. Affordability: Limits consumer cost exposure through competitive allocation, net-benefit requirements and phased funding gates that concentrate resources on the most promising projects. Institutional Efficiency & Transparency: Embeds innovation within the regulatory framework, creating clear governance, challenge-based direction, published project information and accountability for consumer-funded innovation. Key Deployment and Scaling Factors Embed the innovation fund within the regulatory price control so consumer funding is subject to clear governance, net-benefit requirements and regulatory accountability. Publish challenge areas co-developed with industry, consumers, regulators and government to focus innovation on priority system needs. Use a phased funding structure – such as feasibility, proof of concept and demonstration – to manage risk and create a clear progression pathway from early concept to deployment. Require network license holders to partner with innovators, technology companies and research institutions so funded ideas are connected to operational network needs. Add deployment support in successive price control periods to bridge the gap between successful demonstration and widespread operational adoption. Require transparent monitoring, reporting and knowledge-sharing so lessons from funded projects can inform wider industry practice. Align funding with data governance, cybersecurity, interoperability and safety requirements, particularly where projects involve digital systems or operational network assets Case study Ofgem and Innovate UK, Strategic Innovation Fund (2021–2026), United Kingdom The Ofgem Strategic Innovation Fund is delivered in partnership between Ofgem and Innovate UK as part of the RIIO network price control. Launched in 2021 as a five-year, £450 million program under RIIO-2, the fund supports innovation in gas and electricity networks for a low-carbon future. Ofgem and Innovate UK now describe SIF as a £1 billion program, including a further £500 million for the RIIO-3 period.  By the end of 2024, the program reviewed more than 270 new ideas and approved more than £104 million of funding, including 12 large-scale Beta demonstration projects. The 2024 annual report also identified 234 funded projects involving 339 organizations, with 191 completed and 43 live. These figures indicate the scale of participation and the staged progression model used to filter ideas toward larger demonstrations.  For RIIO-3, the program introduces five innovation challenges for 2026 to 2033, covering industrial and business connection acceleration, network build speed and cost, plug-and-play domestic devices, outage reduction and decentralized system balancing. Ofgem’s governance documentation also confirms a multi-phase project approach designed to mitigate innovation risk.  Projected outcomes from funded projects include more than £5.9 billion in potential cost savings and over 12.2 million tons of CO₂-equivalent in potential emissions reductions by 2035, based on illustrative projections assuming projects progress to full rollout. These projected benefits are associated with innovations in areas such as network flexibility, decentralized system balancing, electrification and whole-system integration, and should be treated as projected benefits rather than realized outcomes. References:Apply to the Strategic Innovation Fund (SIF) – Ofgem Ofgem Strategic Innovation Fund Programme Page – Innovate UK Business Connect Strategic Innovation Fund Annual Report 2024 – Innovate UK / UKRI £84 Million of SIF Funding to Accelerate Race to Net Zero – UKRI  
Regulated innovation funding framework for energy network transformation
Innovate UK, Ofgem

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