Long-term decarbonized capacity auction
AI power systems for industrial park
Framework for competitive dispatchable storage
![Digital twin grids create a virtual representation of power-system assets and near real-time or periodically updated operational conditions to support improved grid planning, asset management and operational decision-making. By replicating the behavior of transmission and distribution networks under a wide range of scenarios, these models allow operators to visualize system performance, simulate future states and coordinate actions more proactively. Modern digital-twin platforms integrate data from grid-management systems, sensors, geospatial models and asset registries into a unified digital environment. Combined with advanced simulation tools, AI-assisted analytics and automated data validation, they provide deeper situational awareness and help predict emerging equipment issues, identify constraints and assess energy-flow patterns. In addition to operational simulations, digital twins can represent physical system constraints such as thermal limits, voltage stability, protection settings and congestion, and can model planned and unplanned outages using N-1 and N-k contingency analysis under varying weather and demand conditions. When connected to planning or cost-assessment tools, they also support evaluation of reinforcement options, project sequencing and investment impacts. As systems become more complex – due to distributed energy resources, electrification and extreme-weather risks – digital twins help utilities transition from reactive maintenance to predictive, data-driven operations. Main outcomes of the solution Reliability & Flexibility: Improves operational reliability by enabling operators to test outages, switching actions, congestion events and extreme-weather conditions in a virtual environment before they occur; supports faster operational decisions, more accurate contingency planning and system stabilization during rapidly changing conditions. System Resilience: Identifies weak points in the network – such as overloaded corridors, ageing assets, protection-setting issues or DER-driven congestion – through continuous simulation and model validation. This enables more targeted reinforcements, predictive maintenance and reduced unplanned downtime, improving readiness for climate-driven stresses. Institutional Efficiency & Transparency: Provides a single, authoritative digital model that aligns planning, operations, protection engineering and regulatory reporting; eliminates duplicate modelling efforts, reduces human error, ensures consistent model updates across departments, and supports transparent decision-making and compliance processes. Key deployment and scaling factors Consolidate grid data into a single digital model by integrating SCADA/telemetry streams, GIS network topology, protection and switching schemes, asset registries and historical operational records into a standardized and validated network model. Build high-resolution simulation and scenario-analysis capabilities to model power flows, contingency events, DER integration, switching actions, extreme-weather impacts and planned infrastructure upgrades under both real-time and hypothetical conditions. Deploy advanced analytics and AI/ML tools to detect abnormal operating patterns, predict asset-health issues, prioritize maintenance and support risk-based planning across transmission and distribution networks. Implement strong data governance, cybersecurity and interoperability frameworks so the digital twin connects seamlessly with existing utility systems (EMS, DMS, OMS, GIS, ADMS, asset-management systems) while ensuring secure handling of operational data. Set up continuous model-maintenance processes that reconcile the digital model with field changes, asset updates and regulatory requirements. Establish rigorous model-calibration and parameter-validation procedures to ensure the digital twin remains accurate as system conditions evolve. Case Study AcegasApsAmga and Siemens (2025), Italy In 2025, the Italian distribution system operator (DSO) AcegasApsAmga began deploying Siemens’ Gridscale X platform to build a full digital twin of Trieste’s medium- and low-voltage electricity grid. The platform integrates network data, smart-meter information, outage insights and distributed-energy resource (DER) visibility to provide enhanced situational awareness of distribution-grid conditions. Through its simulation and analytics capabilities, the digital twin supports the assessment of emerging congestion, load patterns and the energy required to maintain grid stability during periods of increased demand. These capabilities are particularly relevant for the electrification of Trieste’s port, which handles more than 70 million tonnes of cargo annually and requires reliable grid capacity for expanding shore-side power and other electrification initiatives. The deployment forms part of the city’s broader strategy to prepare its urban and port infrastructure for increased electrification and distributed-energy integration, demonstrating how advanced modelling and operational analytics can support more proactive and coordinated distribution-grid management.[1,2]eRoots Analytics (2025), Spain eRoots Analytics is developing The Map, a planning-oriented digital grid twin that integrates network topology, market data and power-system analytics to support energy developers, power producers and market players in assessing hosting capacity, congestion risks and grid-connection options earlier in the project cycle. The platform simulates the Iberian market, identifies likely grid constraints, forecasts potential curtailment and evaluates the technical viability of connection points. According to internal analysis provided by the company, the underlying simulation engine has been applied across Iberia in support of developers representing more than 3 GW of renewable capacity. Internal validation exercises report alignment between model outputs and TSO data for selected locations, and the tool has been used in studies commissioned by developers relating to national-scale HVDC and interconnection planning tasks. The Map is currently in a beta testing phase with industry partners, who are providing feedback to refine analytical depth and usability. Early internal testing suggests that automated grid-screening processes may reduce initial feasibility assessments from several weeks to under 24 hours, potentially accelerating project pipelines where sufficient organizational capacity exists. Its modular architecture allows adaptation to different regulatory and grid-data environments.[3]American Electric Power (AEP), Siemens Advanta and Xtensible (2022), USAAmerican Electric Power (AEP) is implementing the T-Nexus program to modernize and centralize transmission system model management across its 11-state service territory. Working with Siemens Advanta and Xtensible, AEP is building a unified digital representation of its transmission network that consolidates planning, operations and asset information into a single high-quality system model. T-Nexus establishes an authoritative “source of truth” for network models by integrating data from multiple legacy systems, standardizing model formats, and improving validation processes. This harmonized digital model supports more accurate planning studies, reduces manual inconsistencies and streamlines regulatory reporting. Siemens’ grid-software platform provides advanced model management and validation capabilities, while Xtensible delivers enterprise data-architecture and utility information-modeling expertise. By improving data quality, automating updates, and enabling cross-functional collaboration, T-Nexus enhances outage analysis, compliance activities, and system-planning workflows. It demonstrates how large utilities can move from fragmented, manual model maintenance to integrated, digital environments that support more proactive and coordinated grid management.[4,,5,6,7]Energy Market Authority (EMA) Singapore and SP Group (2021), Singapore SP Group, Singapore’s national grid operator, is developing a comprehensive Digital Grid Twin under the oversight of the Energy Market Authority (EMA) and in collaboration with research partners – the Institute of High Performance Computing (IHPC), TUMCREATE and Nanyang Technological University (NTU). The initiative comprises two complementary components: an Asset Twin, which represents the condition and performance of grid assets such as substations, transformers and cables, and a Network Twin, which simulates electrical-network behavior under evolving demand, new grid connections and evolving grid conditions. The prototype launched in 2021 integrates historical and near–real-time operational data from multiple systems. It is intended to support remote assessment of asset health, reduce reliance on physical inspections and provide network-impact analysis when integrating new loads or distributed energy resources (DER) such as EV chargers and rooftop solar. This supports more informed maintenance planning and infrastructure-upgrade decisions as electrification and DER adoption increase. The system models the national grid serving 1.7 million electricity consumers in Singapore and is designed to support long-term grid resilience. By simulating new demand patterns, infrastructure stress points and DER hosting capacity, the Network Twin enables planners to identify reinforcement needs earlier and better assess operational risks. Public communications from EMA and SP Group indicate that ongoing development will focus on improving predictive analytics, situational awareness and coordination across planning and operations functions.[8,9,10]National Energy System Operator (NESO) (2021-ongoing), United Kingdom The UK’s National Energy System Operator (NESO) is developing the Virtual Energy System as part of its wider digitalization program. The Virtual Energy System is intended to support analysis of Great Britain’s energy system by enabling coordinated modelling of electricity, gas, storage, demand and flexibility assets across planning and operational time horizons. Rather than operating as a single central model, the Virtual Energy System is conceived as a federated digital-twin approach, in which different organizations retain responsibility for their own models while aligning them through common data standards, interfaces and governance arrangements. This supports greater consistency across system-planning, operational and market analyses and helps reduce duplication of modelling effort across the energy sector. The Virtual Energy System is supported by NESO’s Data Sharing Infrastructure (DSI), which provides the underlying mechanisms for secure data access and exchange between participants. The DSI enables standardized data interfaces, access controls and governance arrangements that allow energy-system data to be shared and reused across organizations, including in system-modelling and analytical tools developed by NESO and third parties.[11,12,13]FlexOps (2023), NorwayFalkor (formerly Kongsberg Digital) worked with Norwegian distribution system operators Elvia, Linja and Tensio, alongside SINTEF Energy, the Norwegian Smart Grid Center and technology provider Pixii, to develop and test a digital twin for integrating flexibility into grid operations. The resulting Falkor Grid module combines grid models, switch settings, historical load and generation measurements, and sensor data in a virtual representation of the network. It provides an always-on grid simulation showing current load flows and forecasts for the next 48 hours, allowing operators to identify emerging bottlenecks and simulate whether flexible resources could resolve them. In Lierne, Norway, the platform was also connected via APIs to a 1 MW/1 MWh battery supplied by Pixii and operated through Peak Shaper, enabling battery data to be viewed alongside grid capacity and load-flow information. Separate testing of the battery found a 22.7% reduction in active power losses during reactive-power support, while Norway's energy regulator estimates that the installation can increase local grid capacity by approximately 30%. The case illustrates how digital grid modelling can help operators identify constraints and assess the use of flexible assets as an alternative or complement to conventional grid reinforcement. [14,15,16]References:AcegasApsAmga leverages Siemens’ Gridscale X to build digital twin of Trieste’s energy grid I Siemens (June 2025) Trieste port electrification advances with Siemens technology I PV Europe (July 2025) eRoots Company WebsiteSiemens grid protection software, expertise enable unified digital model of AEP network I Siemens (September 2022) Electrical Digital Twin I Siemens Accelerating Digital Transformation at American Electric Power I Siemens Xtensible and Siemens Advanta Join Forces to Support AEP’s Next Generation T-Nexus Program (Network Model Management) I Xtensible Singapore’s First Digital Twin for National Power Grid I SP Group & Energy Market Authority (October 2021) Grid Digital Twin I Energy Market Authority (October 2021) Singapore's First Digital Twin for National Power Grid I SP Group (October 2021) Virtual Energy System I NESO Electricity System Operator Innovation Strategy 2024/2025 I ESO Virtual Energy System. Common framework – data & technology priority factors I NESO Shaping the future of electricity grids through flexibility | FalkorWhitepaper Practical experiences of battery operation in radial norwegian rural gridNVE Fakta 4/2026: Batterier i kraftnettet](https://fws.weforum.org/images/casestudies/aJYTG00000010pB4AQ/standard)
Digital twin grid
![AI-enabled interconnection platforms accelerate access to grid capacity for large electricity consumers by analyzing network constraints, identifying usable headroom and automating parts of the planning workflow. These systems typically combine physics-based grid models – such as load-flow analysis, voltage and thermal screening and N-1 contingency assessment – with machine learning tools that can pre-screen thousands of operating scenarios and surface feasible hosting capacity options. By augmenting traditional planning methods with AI-assisted forecasting and scenario exploration, the platforms help reveal when and where additional load may be accommodated within existing infrastructure, reducing reliance on new construction and shortening conventional interconnection timelines. As electrification, digital infrastructure and industrial activity drive rapid load growth, conventional planning processes often struggle to keep pace due to limited visibility, fragmented datasets and manual coordination across stakeholders. AI-assisted approaches address these gaps by translating complex engineering constraints into feasible, commercially aligned pathways that can help utilities and customers progress more quickly and with greater certainty. By enabling the activation of existing capacity rather than depending solely on network expansion, these platforms support more efficient use of existing assets, improve cost-effectiveness in system planning and contribute to flexibility and reliability across the power system. Main outcomes of the solution Affordability: Helps avoid unnecessary or premature network upgrades by identifying usable capacity within existing assets, reducing interconnection costs and improving long-term asset utilization. Reliability & Flexibility: Supports safe activation of latent grid headroom through physics-based modelling, contingency screening and AI-assisted forecasting, allowing faster interconnections without compromising system stability or regulatory requirements. Business Value: Helps reduce interconnection bottlenecks and shorten timelines, enabling utilities and customers to advance projects more efficiently. Key deployment and scaling factors Integrate grid datasets – including topology, load, weather, and operational constraints – into a unified AI-enabled modelling environment capable of assessing hosting capacity and identifying feasible headroom. Collaborate closely with utility planning teams to validate findings, align with reliability criteria and ensure regulatory compliance. Establish multi-stakeholder coordination processes where utilities, developers and solution providers share visibility into constraints, timelines and feasible interconnection pathways. Adapt modelling approaches to local regulatory frameworks, planning methodologies and transparency requirements. Build scalable data pipelines to support replication of the methodology across regions with varying grid architectures and market designs. Case Study GridCARE and Portland General Electric (PGE) (2025), USA In Hillsboro, Oregon - an area seeing rapid growth in data center demand - hyperscale AI developers were facing 5-7 year interconnection queues. GridCARE partnered with Portland General Electric (PGE) to accelerate access to capacity by combining physics-based grid modeling with AI-assisted forecasting and scenario exploration to identify parts of the network where additional load could be safely accommodated without near-term transmission or generation expansion. GridCARE acted as a neutral partner between utility planners and data-center developers, translating engineering constraints into actionable commercial pathways and building shared trust in the process. The methodology increases asset utilization, raising system use from below 40% toward its designed potential. Higher utilization spreads fixed costs across more kilowatt-hours, creating downward pressure on customer rates. Through detailed engineering validation with PGE, the approach supported plans to enable approximately 80 MW of new capacity by 2026 and a further 400 MW by 2029, subject to ongoing utility and regulatory review. These load additions, enabled without major infrastructure upgrades, allow PGE to reduce rates by up to 5% or invest up to $1.35B in grid modernization without raising rates. This project demonstrates how utilities and large-load customers can use AI-enabled analysis to navigate constraints more efficiently, identify opportunities to use existing infrastructure and reduce delays in connecting high-demand facilities while maintaining reliability standards.[1,2,3]References:Portland General Electric and GridCARE Accelerate Hundreds of Megawatts of Data Center Power in Leading U.S. Market I GridCARE (October 2025) AI Data Centers as Engines of Affordability and Capital Investment I GridCARE Information provided to World Economic Forum I GridCARE (November 2025)](https://fws.weforum.org/images/casestudies/aJYTG000000126D4AQ/standard)
AI-enabled interconnection platforms
![Enhanced Geothermal Systems (EGS) create engineered geothermal reservoirs in hot dry rock where natural permeability is insufficient for conventional geothermal development. Water is injected at depth to open or enhance fractures, circulates through the heated rock, and returns to the surface for electricity generation or direct heat use. Unlike hydrothermal geothermal, which is limited to locations with naturally occurring heat, fluid and permeability, EGS can be deployed across a far wider range of geologies, significantly expanding geothermal potential beyond volcanic regions.[1,2,3]According to the International Energy Agency (IEA), next-generation geothermal technologies such as EGS could broaden the geographical scope of geothermal deployment and provide firm, low-carbon renewable energy in regions without natural geothermal reservoirs.[4]Main outcomes of the solutionSustainability: Delivers low-carbon renewable heat and power by creating engineered geothermal reservoirs in locations lacking natural hydrothermal resources, supporting deep decarbonization while minimizing land requirements. Lifecycle impacts remain low relative to fossil alternatives. Business value: Opens new opportunities for baseload renewable investment as drilling technologies improve, while enabling knowledge transfer from oil and gas. Early-stage capital requirements and subsurface uncertainty still pose investment risks, but successful pilots can catalyze specialized financing and local supply-chain capability. Reliability & Flexibility: Provides continuous, dispatchable renewable power that complements variable solar and wind. Reservoir performance depends on subsurface conditions, flow rates and thermal sustainability, requiring ongoing monitoring to maintain reliable output. Key deployment and scaling factorsAdvanced drilling technology and cost-effective hydraulic stimulation to create permeability in deep hot rock. Accurate subsurface characterization (geology, heat flow, stress fields) to optimize reservoir creation and minimize induced seismic risk. High upfront capital investment with financing models that absorb early-stage risk and enable scaling. Regulatory frameworks for geothermal licensing, monitoring induced seismicity, and permitting deep reservoirs. Skilled workforce and supply chain development for deep drilling, well stimulation, reservoir management and long-term operation. Continuous reservoir monitoring to manage thermal drawdown, flow rates and induced seismicity. Community engagement and transparent communication to address seismic-risk concerns and support social acceptance. Case Studies Geo-Energie Suisse (2024), SwitzerlandGeo-Energie Suisse is developing a deep Enhanced Geothermal System in Jura Canton, targeting a depth of approximately 4,000 meters. Planned activities include hydraulic stimulation in 2025 to enhance permeability in hot dry rock, followed by drilling of a second well in 2026 to enable circulation testing. Subject to reservoir performance and regulatory approvals, the project has an indicative target of producing around 5 MWe later in the decade. The project is intended to demonstrate the technical feasibility of EGS in non-volcanic geological settings and to provide a reference model for expanding firm, low-carbon geothermal energy beyond regions with naturally occurring hydrothermal resources.[5] Fervo Energy and Google (2023), USAFervo drilled two horizontal wells and deployed fiber-optic monitoring to track temperature, flow and reservoir performance in real time. In 2023, the project successfully completed a 30-day flow test, demonstrating stable flow rates and sustained heat extraction, confirming the technical viability of this next-generation geothermal approach in heat resources without naturally occurring flow. Following this milestone, the system began supplying carbon-free electricity to the Nevada grid, contributing to Google's aim of achieving 24/7 carbon-free energy by 2030.The project illustrates how EGS can provide continuous, dispatchable clean power with a relatively small land footprint, support knowledge transfer from oil and gas drilling and monitoring practices, and expand geothermal deployment in resources previously considered unsuitable for conventional geothermal development.[6] References:The Future of Geothermal Energy I IEA (December 2024) Enhanced Geothermal Systems I US Department of Energy (updated January 2025) Why enhanced geothermal systems could be a game-changer for clean energy I StanfordReport (February 2025)IEA publishes special report on the Future of Geothermal Energy I World-Energy (December 2024)Deep Geothermal Pilot Project in Haute-Sorne (JU) I Geo-Energie Suisse AG A first-of-its-kind geothermal project is now operational I Google (November 2023)](https://fws.weforum.org/images/casestudies/aJYTG000000127p4AA/standard)
Enhanced geothermal systems
![Advanced cooling technologies enable data centers to reduce energy and water consumption while supporting higher compute densities associated with AI and cloud workloads. Instead of depending primarily on conventional air conditioning systems, approaches such as free-air cooling, liquid cooling and immersion cooling transfer heat directly from chips or racks, improving thermal efficiency: Free air cooling uses filtered outdoor air in suitable climates to reduce mechanical cooling loads. Liquid cooling circulates coolant directly to servers or chips, improving heat transfer and lowering energy use. Immersion cooling submerges IT hardware in thermally stable dielectric fluids to remove heat with very high efficiency and minimal water use. The International Energy Agency (IEA) reports that cooling accounts for about 7% of electricity use in very efficient hyperscale sites, compared with over 30% in conventional data centres.[1] The International Renewable Energy Agency (IRENA) similarly highlights opportunities to reduce environmental impact by adopting high-efficiency cooling and capturing waste heat for productive use.[2] Together, these technologies support the expansion of digital infrastructure in a more resource-efficient way. Main outcomes of the solution Sustainability: Reduces energy and water consumption associated with thermal management and enables productive reuse of data-center waste heat where appropriate infrastructure exists. Affordability: Lowers cooling overheads and reduces the operational costs of high-density compute environments, helping delay or avoid expensive expansions of conventional cooling systems. Business Value: Supports higher compute densities and more thermally stable operation of AI and cloud workloads, improving uptime and enabling more efficient use of data-center space. Key implementation and scaling factors Pilot deployment in selected data-center areas to validate thermal performance, safety and integration with existing infrastructure. Use modular cooling units and standardized interfaces to support replication across facilities with varying rack densities and computing environments. Assess power, water, airflow and climate conditions—including humidity thresholds—to ensure technology suitability and avoid retrofit challenges. Align cooling systems with heat-recovery infrastructure to maximize the value of captured heat for potential district-heating or industrial applications. Collaborate closely with equipment vendors and operators to maintain compatibility with evolving server architectures, high-density AI workloads and sustainability targets. Evaluate material compatibility, maintenance needs and lifecycle performance for liquid or immersion-based systems. Integrate monitoring and data-analytics tools to track thermal performance and support predictive maintenance. Case Study Polar Data Centres and Vertiv (2024), NorwayPolar Data Centers partnered with Vertiv to design and deliver DRA01, a high-density data center in Norway optimized for AI and high-performance computing workloads. The project’s design phase was completed in Q4 2024, with modular infrastructure shipped and installed in Q1 2025 and commissioning planned for completion later in 2025. The facility is designed in line with Tier III–type principles, including N+1 redundancy and concurrent maintainability. Powered by local hydropower, the site integrates liquid cooling and free-air cooling approaches to support high-density compute while reducing energy and water use. The cooling architecture includes glycol-free free-cooling chillers and a chilled-water network with thermal storage, alongside direct liquid cooling designed to support advanced GPU-based systems. The modular setup is designed to support rack densities of up to 110 kW, enabling a total initial IT capacity of approximately 12 MW, with provision for future expansion. Based on design targets and commissioning plans, the facility is expected to achieve high energy efficiency, with a projected power usage effectiveness (PUE) as low as 1.15, benefiting from Norway’s cold climate and advanced cooling design. The project illustrates how advanced, scalable cooling technologies can enable energy-efficient, high-density data-center development in regions with favorable climatic conditions and low-carbon electricity.[3] References: Energy demand from AI I IEA (April 2025) Waste heat recovery from data centres I IRENA (June 2023)Polar and Vertiv drive innovation with modular AI solution for their DRA01 data center in Norway I Vertiv (2025)](https://fws.weforum.org/images/casestudies/aJYTG000000129R4AQ/standard)
Advanced sustainable cooling technologies
![AI for grid operating optimization applies advanced data analytics and machine learning techniques to large volumes of operational, asset and system data to support more accurate forecasting, faster decision making and improved operational efficiency. AI models are increasingly used to enhance short-term load and renewable generation forecasting, support predictive maintenance and anomaly detection in grid assets, and assist operators in managing more dynamic and complex power flows. As power systems integrate higher shares of variable renewable energy, electric vehicles and distributed energy resources, grid behavior becomes less predictable and more sensitive to local constraints. Traditional rule-based or deterministic methods often struggle to capture these interactions in real time. AI tools can learn from historical and live datasets to identify patterns, anticipate stress conditions and highlight emerging risks earlier than manual analysis alone, improving situational awareness for system operators. In operational practice, AI is most commonly applied to prioritize alerts, improve forecast usability and narrow the set of actions operators must evaluate under time pressure. For asset-intensive systems, AI-based condition monitoring supports a shift from reactive or time-based maintenance toward condition-based approaches, reducing forced outages and improving asset availability. In distribution networks, AI-enabled analytics increasingly support visibility of DER behavior, local congestion risks and load growth driven by electrification and EV charging. Together, these applications help utilities operate existing infrastructure more efficiently while maintaining reliability in increasingly complex system conditions.[1]Main outcomes of the solution Reliability & Flexibility: Improves grid stability by enhancing forecast accuracy, enabling earlier detection of abnormal conditions and supporting more timely operator response to system variability. Institutional Efficiency & Transparency: Strengthens data-driven decision making by converting large operational datasets into actionable insights, reducing manual workload and improving visibility for operators and planners. Affordability: Supports more efficient use of existing assets by reducing unplanned outages, optimizing maintenance activities and limiting avoidable operational costs. Key deployment and scaling factors Ensure availability of high-quality historical and real-time datasets, including load, renewable output, asset condition and environmental data. Perform integration of AI models with existing SCADA, EMS and asset management platforms to ensure operational relevance and safe deployment. Develop robust cybersecurity and data governance frameworks to protect operational data and maintain institutional trust. Develop workforce capabilities that combine grid engineering expertise with data engineering and AI model development skills. Case Study Camus Energy (2019), USACamus Energy has deployed an AI-enabled Distributed Energy Resource Management System (DERMS) designed to support real-time situational awareness and grid-aware coordination of distributed energy resources on distribution networks. The platform integrates data from advanced metering infrastructure, IoT sensors and network topology models with machine learning–based forecasting to provide operators with visibility of load, voltage conditions and DER behavior at feeder and substation level. By combining short-term load forecasting with modeled behavior of EV charging, rooftop solar and flexible demand, the system supports identification of emerging congestion risks and local hosting-capacity constraints. Utilities can use this information to assess where managed charging, demand-side flexibility or other operational measures may be applied before technical limits are reached, within established grid-security and operating rules. In collaboration with utilities including AES Indiana, the platform has been applied to analyze EV charging impacts and evaluate managed-charging strategies as part of broader distribution planning and operational processes. The case illustrates how AI-enabled analytics and DERMS platforms can support more informed operational decision-making, improve utilization of existing distribution assets and help utilities manage growing DER penetration under accelerating electrification.[2,3]References: Energy and AI I IEA (April 2025) The Smartest DER Control, Rooted in Grid Awareness I Camus Technology Convergence Report 2025 I World Economic Forum (June 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012B34AI/standard)
AI for grid operating optimization
![As power grids and energy infrastructure become increasingly digitized, connected devices, ranging from smart meters and sensors to grid-edge IoT and operational technology systems, expose new attack surfaces. Cybersecurity solutions for digital grids and power infrastructure are critical to safeguarding these assets from cyber threats, ensuring that smart-grid operations remain secure, reliable and resilient amid growing system complexity and IT/OT convergence. Modern security platforms combine automated asset discovery, continuous vulnerability and threat monitoring, behavioral analytics and unified IT/OT threat management to protect both legacy and new infrastructure. By providing enhanced visibility over grid-edge devices and near real-time detection of anomalous activity, they help utilities manage cyber risk proactively, reduce the likelihood and impact of cyber-induced outages and support resilient operations in increasingly distributed and digitalized energy systems. Main outcomes of the solution Reliability & Flexibility: Improves operational continuity by reducing cyber-induced outages and enabling secure integration of digital and smart-grid technologies; supports stable grid and plant operations under evolving threat conditions. System Resilience: Strengthens grid resilience by continuously monitoring IoT/OT devices, identifying vulnerabilities early and enabling faster response to cyber threats — reducing the risk of system disruptions and cascading failures. Institutional Efficiency & Transparency: Enhances governance and compliance by consolidating IT and OT security processes into a unified framework, standardizing reporting and enabling consistent oversight across connected devices and grid assets. Key deployment and scaling factors Deploy agentless, network-layer monitoring sensors to automatically discover and map IoT/OT devices across grid infrastructure, including unmanaged or legacy (“brownfield”) assets. Implement continuous passive monitoring and behavioral-analytics engines to identify anomalies, unusual traffic patterns or protocol misuse without interrupting operational performance. Integrate OT and IoT threat-detection systems with existing IT security platforms (SIEM/SOAR) to enable unified incident-response workflows across IT/OT domains. Maintain comprehensive vulnerability and risk-management processes – including regular asset-inventory updates, patching, network segmentation and access control policies – tailored to the unique constraints of critical infrastructure environments. Ensure scalability so the solution supports diverse device types (smart meters, sensors, control systems) and deployment contexts (on-premises, hybrid, cloud-connected), across multiple network segments and regulatory jurisdictions. Conduct regular cyber-incident-response exercises (e.g., tabletop drills, OT emergency simulations) to validate preparedness and coordination across teams Case Study Microsoft (2020), Global In 2020, Microsoft acquired CyberX, a company specialized in agentless IoT/OT security and integrated its technology into Azure Defender for IoT (now Microsoft Defender for IoT). CyberX’s platform uses passive, agentless deep-packet inspection to analyze traffic across industrial and operational-technology networks, covering a wide range of OT and critical-infrastructure protocols. This enables automated discovery of connected assets, baseline development of normal operational behavior and identification of vulnerabilities, misconfigurations and anomalous activity without requiring software installation on devices. The solution provides continuous monitoring of IoT/OT environments using behavior and protocol analysis to detect suspicious patterns, unsafe configurations and policy violations in a way that is compatible with safety-critical equipment. When connected to Microsoft Sentinel, the company’s cloud-native SIEM/SOAR (Security Information and Event Management / Security Orchestration, Automation and Response) platform, organizations can correlate OT and IT security events, build coordinated response workflows and centralize alert management. This combined architecture gives utilities, manufacturers and other operators of critical infrastructure enhanced visibility into cyber risks across both digital-grid and industrial-control-system environments, supporting more consistent and coordinated risk management.[1,2]References: Azure Defender for IoT including CyberX I Microsoft (November 2020) Microsoft acquires CyberX to accelerate and secure customers’ IoT deployments I Microsoft (June 2020)](https://fws.weforum.org/images/casestudies/aJYTG00000012RB4AY/standard)
Cybersecurity solutions for digital grids and power infrastructure
![Digital Resilient Energy Systems (DRES) use integrated digital technologies, predictive analytics and real-time monitoring to strengthen the adaptability and reliability of power networks. By combining operational data with climate intelligence, automated decision support and cybersecure control systems, they improve outage prevention, optimize distributed-asset coordination and enhance long-term planning. DRES help distribution operators manage growing electrification, rising climate volatility and increasing distributed energy resources (DER) penetration by providing continuous situational awareness and risk forecasting down to the low-voltage level. Predictive models can identify abnormal operating patterns, estimate fault probabilities and highlight assets at elevated climate risk. Through digital workflows that connect vegetation and weather intelligence, operational analytics, cost–benefit assessment and resilience-based investment planning, DRES creates a data-driven foundation for more sustainable, reliable and climate-ready electricity networks. Main outcomes of the solution Sustainability: Supports decarbonization by reducing technical losses, avoiding fossil-fuel backup generation during outages, and ensuring renewable resources remain connected even under stress conditions. System Resilience: Improves the ability of electricity networks to anticipate, withstand and recover from extreme weather and other disruptive events by integrating predictive risk models, climate forecasting and continuous monitoring. Institutional Efficiency & Transparency: Embeds data-driven decision-making into grid planning by integrating ESG criteria, quantifying resilience benefits and providing clear justification for investment prioritization and regulatory processes. Key deployment and scaling factors Integrate operational, meteorological, climate and vegetation datasets into a unified platform to provide consistent risk indicators, early-warning capabilities and near real-time situational awareness. Develop predictive-analytics and scenario-modelling tools to forecast faults, evaluate stress conditions, simulate DER interactions and assess climate impacts on grid assets. Implement interoperable modules (vegetation management, weather analysis, advanced grid operation, cost-benefit modelling and resilience planning) to support coordinated operational and investment decisions. Define resilience and ESG metrics for long-term planning, enabling prioritization of vulnerable assets and optimization of total expenditure across technical, environmental and social dimensions. Ensure scalability and repeatability through modular system design and interoperability with existing utility systems. Strengthen data governance and cybersecurity measures to safeguard sensitive operational data and maintain confidence in digital decision-support tools. Case Study Enel (2025), Brazil Enel’s DRES4BR initiative applies digital resilience tools to support distribution-network planning and operations in areas experiencing more frequent climate-related disruptions. The project, led by Enel Distribuição São Paulo together with AiDash, PSR, Tempo OK and academic partners, aims to ensure stable and continuous electricity service for communities and critical infrastructure. The system integrates five digital modules combining vegetation monitoring, high-resolution weather forecasting, advanced grid-operation analytics, cost–benefit modelling and long-term resilience planning. These tools allow operators to identify vulnerable assets, predict faults and evaluate which interventions will deliver the greatest technical, environmental and economic benefits. The platform uses satellite and drone imagery, predictive models and ESG criteria to prioritize maintenance and investment where climate risks are highest. By reducing outages – tracked through DEC/FEC reliability indicators2 – and improving the planning of network upgrades, DRES4BR helps lower operational costs and strengthens grid performance. It supports Brazil’s wider decarbonization efforts by reducing technical losses, limiting reliance on fossil-fuel backup during disruptions and enabling more stable renewable integration. The approach is designed for scalability to other regions subject to local regulatory requirements.[1]National Grid and Rhizome (2025), USA National Grid is deploying Rhizome’s gridFIRM platform to strengthen wildfire-risk assessment and resilience planning across its transmission and distribution networks in the Northeastern United States. While the region has historically been considered lower risk for wildfires, New York and Massachusetts recorded over 2,600 wildfire incidents in 2024, more than double the number reported in the previous year, prompting the utility to adopt more proactive, data-driven planning tools. gridFIRM integrates climate data, geospatial analysis and long-term risk modelling to help identify assets exposed to elevated wildfire risk, quantify potential ignition pathways and prioritize mitigation options. The platform supports National Grid’s ability to assess vulnerabilities across its network, compare risk-reduction strategies and inform longer-term resilience planning decisions.[2]References: Information provided to World Economic Forum I Enel (November 2025)As wildfire risks grow, predictive tech is powering grid resilience I National Grid (December 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012Sn4AI/standard)
Digital resilient energy systems
![Dynamic Line Rating (DLR) is a grid-optimization technology that calculates the real-time thermal capacity of overhead transmission lines based on actual operating conditions. Unlike traditional static ratings – defined using conservative “worst-case” environmental assumptions – DLR adjusts capacity using live measurements of wind speed, ambient temperature, solar radiation and conductor behavior. Because conductor temperature determines safe ampacity, real-time ratings allow system operators to utilize transmission assets more efficiently and, in favorable conditions, increase transfer capability without physical upgrades. Modern DLR systems combine direct measurements (e.g., conductor sag, tension, clearance or temperature) with indirect weather-based estimation models. Field-deployed IoT sensors and nearby weather stations provide continuous data streams that feed secure analytics platforms, where thermal models compute dynamic capacities, alert operators to overheating risks and identify anomalous conductor behavior. These insights support congestion management, dispatch optimization, outage preparedness and predictive maintenance, while helping utilities comply with regulatory requirements for improved transmission-line visibility and ambient-adjusted rating practices. Main outcomes of the solutionSustainability: Helps integrate more variable renewable generation and reduces the need for new build infrastructure, lowering environmental impact – by increasing the usable capacity of existing lines. Reliability & Flexibility: Improves situational awareness and allows operators to respond dynamically to changing conditions, strengthening operational reliability and managing congestion more effectively. Affordability: Can defer or avoid costly grid-upgrade investments by unlocking additional capacity from existing assets at comparatively low cost Key deployment and scaling factors Pilot validation to demonstrate technical performance, operational impact, and regulatory compliance. Modular sensor and analytics architecture for scalable deployment across diverse grid topologies. Cybersecurity and data integrity embedded from design to ensure safe and compliant operations. Real-time data integration into EMS, SCADA, ADMS and planning tools to ensure that dynamic ratings are operationally usable. Data quality and fallback mechanisms to manage sensor redundancy, calibration, error detection and confidence intervals. Regulatory alignment mechanisms to ensure acceptance of dynamic ratings within system-operator procedures, congestion management and interconnection studies. Collaborative implementation frameworks involving utilities, TSOs/ISOs, regulators and technology providers to update operating practices and workforce capabilities. Case StudyAES Corporation (announced in 2023), USABetween 2023 and 2024, AES deployed 42 non-contact LiDAR-based IoT sensors across five transmission corridors in Indiana and Ohio as part of its grid modernization strategy.[1] These field-mounted sensors continuously monitored conductor sag, position, and local weather conditions, generating real-time and planning-grade DLR that reflected actual thermal capacity rather than conservative static assumptions. The deployment increased transfer capacity by up to a 61% on a 345 kV line and 25% on a 69 kV line, compared to static ratings. By leveraging DLR instead of reconductoring, AES achieved the required capacity improvements at an estimated cost of USD 0.39 million, compared to approximately USD 1.63 million for full line replacement. The system also reduced renewable energy curtailment and supported additional solar integration across the monitored corridors. Real-time data streams and analytics enabled dynamic dispatch and helped AES align with requirements of FERC Order 881, which mandates enhanced visibility of transmission line ratings. Embedded cybersecurity and data integrity safeguards ensured secure operation of the sensor network and analytics platform. References: Lessons from first deployment of Dynamic Line Ratings I AES & LineVision Case Study (April 2024)](https://fws.weforum.org/images/casestudies/aJYTG00000012UP4AY/standard)
Dynamic line capacity rating
![Eco-efficient switchgear refers to high-voltage electrical equipment – most commonly gas-insulated switchgear (GIS) – that replaces sulfur hexafluoride (SF₆), SF₆ has a global warming potential more than 20,000 times higher than CO₂ and remains in the atmosphere for more than a thousand years if released. As transmission systems expand and more substations are built in space-constrained environments, reducing dependence on SF₆ has become an important component of grid decarbonization efforts, especially as GIS deployment grows in urban, space-constrained and underground substations. Alternative approaches typically use low-GWP insulating gases or redesigned insulation systems that maintain the required electrical performance without relying on SF₆. These developments allow network operators to modernize and expand grid infrastructure while reducing lifecycle emissions and aligning with emerging regulatory requirements that target high-GWP gases. [1]Main outcomes of the solutionSustainability: Reduces or removes greenhouse-gas emissions associated with insulating gases in high-voltage equipment, supporting utilities in meeting climate objectives and complying with evolving SF₆-reduction policies. Key deployment and scaling factorsEnsure availability and supply chain readiness of low-GWP insulating materials and technologies that meet technical requirements across voltage classes. Validate performance at high-voltage levels (e.g., 420 kV and 550 kV) through type-testing, system validation and early operational experience. Assess compatibility with retrofit or replacement strategies, noting that retrofill solutions are more mature at medium voltage, while high-voltage GIS typically requires new equipment. Build industry acceptance through third-party testing, certification and the accumulation of operational field data across different climates and use cases. Align with regulatory frameworks that incentivize or mandate reductions in SF₆ use in transmission and distribution networks. Case Study Hitachi Energy and State Grid Corporation of China (SGCC) (2025), ChinaIn 2025, Hitachi Energy announced delivery of a 550 kV SF₆-free gas-insulated switchgear (GIS) installation for SGCC’s Central China branch. High-voltage GIS at this rating is used for long-distance transmission and is traditionally reliant on SF₆ because of its strong technical properties. Replacing SF₆ at 550 kV therefore represents a significant technical milestone, demonstrating that alternative insulating gases and adapted insulation designs can meet the performance and reliability requirements associated with the highest voltage classes in transmission networks. The installation replaces conventional SF₆-insulated GIS with equipment using an alternative insulating gas mixture and modified insulation geometry. It provides early evidence of the feasibility of SF₆-free GIS at one of the most demanding voltage levels and contributes to wider efforts in China and globally to lower the environmental footprint of high-voltage transmission assets.[2] References:EconiQ Technology Eliminating SF6 in High-voltage Switchgear and Circuit-breakers I Hitachi Review (2023) Hitachi Energy to deliver the world's first SF6-free 550 kV gas-insulated switchgear I Hitachi (May 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012W14AI/standard)
Eco-efficient switchgear
![Advanced market trading dynamics introduce shorter bidding and scheduling intervals within wholesale electricity markets, typically moving from hourly blocks to 15-minute or even 5-minute products. By increasing temporal granularity, markets reflect system conditions more accurately, capturing rapid changes in renewable generation, demand patterns and flexibility availability. This enables traders, generators, storage operators and demand-response providers to submit bids aligned with real operational behavior rather than broad hourly assumptions. Traditional hourly auctions were designed for systems dominated by large, inflexible thermal plants. As variable renewables and distributed flexibility resources grow, the mismatch between real-time system needs and hourly price signals increases. Shorter intervals address this challenge by improving the precision of price formation and reducing imbalance risks. They also expand value opportunities for fast-responding assets such as batteries, electrolyzers, virtual power plants (VPPs) and flexible industrial loads. In doing so, advanced trading dynamics improve system balancing, reduce reliance on costly reserve actions and strengthen the economic foundations of a high-renewables grid. Main outcomes of the solution Reliability & Flexibility: Finer time resolution improves forecasting accuracy and operational responsiveness, strengthening balancing capability and supporting higher levels of system flexibility. Sustainability: Granular price signals better match the short-term output patterns of variable renewables, reducing curtailment and lowering reliance on fossil-fuel balancing resources. Affordability: Reduced imbalance costs and more efficient use of storage and demand-side flexibility improve overall system efficiency and support lower wholesale-market costs over time. Key deployment and scaling factors Advanced market-clearing infrastructure capable of processing higher data volumes and executing more frequent auctions reliably. Harmonized regulatory frameworks with consistent rules on gate closure, product definitions, imbalance settlement and cross-border capacity allocation. Participant digital readiness in forecasting, automation and bidding systems compatible with sub-hourly markets. Coordinated TSO–DSO procedures for scheduling, balancing and data exchange to support granular trading in real-time operations. Widespread deployment of flexibility assets (storage, demand response, hybrid plants, VPPs) able to respond to granular price signals. Robust cybersecurity and data-integrity measures to support increased digitalization and automated dispatch. Case Studies European Commission, ACER, ENTSO-E, and Nominated Electricity Market Operators (NEMOs) (2025), Europe On 1 October 2025, Europe implemented a major market-design reform by shifting the Single Day-Ahead Coupling (SDAC) from hourly products to 15-minute trading intervals. This transition was executed on 30 September 2025 for delivery beginning 1 October, as confirmed by the European Commission, ACER, ENTSO-E and all Nominated Electricity Market Operators (NEMOs). The reform harmonizes the day-ahead market with the 15-minute granularity already present in intraday trading and reflects the increasingly dynamic conditions of a system with growing share of renewables. Under this structure, market participants submit 96 bids per day instead of 24, enabling trading decisions that reflect short-term fluctuations in wind output, solar ramps and demand variations. Cross-border capacity is also allocated at 15-minute resolution, enabling neighboring countries to exchange power more efficiently during periods of volatility. Flexible assets such as batteries, demand-response portfolios and hybrid renewable-storage plants can now respond more precisely to system needs and capture value from intra-day price spreads. Early market observations indicate sharper price signals and improved balancing performance, particularly during periods of high renewable variability. The reform aims to strengthen Europe’s ability to operate a high-renewables grid and enhance the value of flexibility investments.[1, 2, 3]Federal Energy Regulatory Commission and regional wholesale market operators (2015), USAFor more than a decade, the United States ISO/RTO markets have operated on 5-minute dispatch intervals.[4] Operators including CAISO, PJM, MISO, ERCOT, NYISO, and SPP recalculate locational marginal prices every five minutes, aligning market signals with real-time operating conditions in systems with high shares of wind, solar and distributed flexibility resources. These frameworks were introduced to improve operational efficiency, reduce balancing costs and enable fast-responding technologies to participate more effectively. Advanced dispatch algorithms and real-time telemetry support continuous price formation, while settlement rules – updated in several markets following Federal Energy Regulatory Commission (FERC) Order 825[6] – improve alignment between dispatch and compensation. Regional coordination optimizes flows across large networks and enhances congestion management. Documented outcomes from U.S. ISO/RTO markets include lower uplift and balancing costs, improved price convergence between day-ahead and real-time markets, and enhanced reliability through continuous recalculation of operating limits. In regions such as CAISO[5] and ERCOT[7], granular 5-minute pricing has also strengthened the business case for fast-responding assets, with batteries and responsive demand able to react immediately to changing system conditions and help manage variability from wind and solar generation. References: EU electricity trading in the day-ahead markets becomes more dynamic I European Commission (October 2025)15-minute products in Market Coupling I epexspot Single Day-Ahead Coupling (SDAC) I All NEMO Committee FERC Releases Metrics Report on RTO and ISO Markets I American Public Power Association (February 2024) Wind and Solar Curtailment Report I California ISO (May 2025) Settlement Intervals and Shortage Pricing in Markets Operated by Regional Transmission Organizations and Independent System Operators (June 2016) 2024 State of the Market Report for the ERCOT Electricity Markets I Potomac Economics (May 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012Xd4AI/standard)
Advanced market trading dynamics
![All-in-one V2G bundles integrate electric vehicle leasing, bidirectional charging, smart tariffs and digital optimization into a single turnkey offer. As the global electric vehicle fleet is expected to grow twelve-fold by 2035 under stated policies according to the International Energy Agency (IEA), the aggregate storage potential of vehicle batteries represents a significant source of short-duration flexibility for power systems.[1] These bundled models coordinate charging and discharging to reduce peak demand, absorb surplus renewable generation and, in suitable regulatory contexts, defer local network upgrades. Historically, EVs, charging equipment, tariffs and flexibility services were procured separately, limiting customer uptake and reducing the system value of electrified transport. Charging was often unmanaged, and most consumers were not rewarded for contributing flexibility. All-in-one V2G bundles streamline this landscape by combining vehicle access, interoperable hardware, digital control and optional market participation in a single offer. Bidirectional EVs operate as controllable distributed assets that can modulate charging or export electricity according to price signals, network conditions and customer preferences. Customers receive a simplified service model and the potential for financial benefits, while providers gain a scalable platform for orchestrating flexible demand. Main outcomes of the solution Affordability: Lowers system and consumer costs by shifting charging away from peak periods, reducing local grid stress and deferring reinforcement. Using existing EV batteries as flexible capacity avoids investment in separate storage assets and can reduce household charging expenses through optimized tariffs. Reliability & Flexibility: Improves system balancing by coordinating aggregated bidirectional charging to respond to local constraints, renewable variability and short-term operational needs. When permitted by grid and market rules, V2G fleets can support voltage and frequency management and enhance flexibility provision. Sustainability: Increases renewable utilization by aligning charging with renewable-rich periods and discharging during fossil-intensive peaks, reducing curtailment and supporting lower-carbon system operation. Key deployment and scaling factors Design integrated bundles that combine vehicle access, bidirectional hardware, tariffs and optimization into a single customer offer to reduce adoption barriers. Establish smart tariff and market integration frameworks to monetize flexibility where regulation allows and provide transparent, predictable value to participants. Deploy digital orchestration platforms capable of coordinating large EV fleets, optimizing charge-discharge cycles and integrating with grid-operator or aggregator systems. Secure OEM–utility–installer partnerships to ensure availability of V2G-capable vehicles, interoperable chargers and reliable installation and maintenance workflows. Target deployment to priority network areas where dwell times, congestion patterns and flexibility needs deliver the strongest system and economic benefits. Ensure compliance with grid codes and metering requirements, including export authorization, smart-meter capabilities and bidirectional-charger certification. Case Study Octopus Energy (2024), United Kingdom In 2024, Octopus launched the Power Pack, a V2G offer that consists of a bidirectional tariff, compatible charging hardware and an optional EV lease. The model automates charge and discharge cycles through the Kraken platform, aligning vehicle operation with low-cost, low-carbon periods or high-demand hours. Participation requires a V2G-capable vehicle, a smart meter, an approved bidirectional charge, and export authorization from the distribution network operator. A bundled option combines a BYD Dolphin lease, a Zaptec Pro bidirectional charger and access to the tariff under a single monthly payment, reducing the need for customers to assemble compatible components. Kraken schedules charging and export based on wholesale-price signals and local network conditions, with potential savings reflecting avoided peak charging and export revenues where applicable.[2] References: Global EV Outlook 2024 I IEA (April 2024) Octopus Power Pack: the UK’s first Vehicle-to-Grid tariff I Octopus Energy](https://fws.weforum.org/images/casestudies/aJYTG00000012ZF4AY/standard)
All-in-one vehicle-to-grid (V2G) bundles
![Battery-as-a-Service (BaaS) and EV battery-swapping models separate battery ownership from the vehicle, allowing fleet operators or consumers to lease or exchange batteries instead of purchasing them outright. This reduces upfront capital expenditure, eases financing constraints for commercial fleets and accelerates vehicle turnover by treating the battery as a service asset rather than a long-lived capital component. Swapping systems rely on modular, standardized battery packs and automated stations where depleted batteries can be exchanged for charged units within minutes, reducing charging-related downtime and enabling high daily mileage or tightly scheduled logistics operations. BaaS platforms also centralize battery charging, health monitoring and lifecycle management. By charging batteries at controlled rates, operators can improve asset longevity, reduce load peaks on the grid and shift electricity demand to off-peak periods. This supports more predictable grid interactions compared to unmanaged plug-in charging and creates opportunities for coordinated demand response as fleets scale. Centralized oversight enables consistent assessment of battery state-of-health, facilitates second-life allocation into stationary storage and ensures recycling pathways are integrated from the outset. For power systems facing rapid growth in commercial EV fleets, BaaS and battery-swapping models provide a structured mechanism for electrification without requiring extensive upgrades to depot infrastructure or large scale fast-charging hubs. The model offers an alternative pathway for high-utilization sectors such as delivery services, urban logistics, ride-hailing and municipal fleets, where operational continuity and predictable energy availability are central to business performance.Main outcomes of the solution Affordability: Reduces total cost of ownership by avoiding upfront battery procurement, spreading expenditure through leasing or service fees and reducing operational losses from charging-related downtime. Reliability & Flexibility: Provides rapid, automated battery swaps that enable continuous vehicle operations and predictable energy availability, supporting sectors with tight scheduling requirements such as logistics and commercial transport. Business Value: Enhances fleet performance through shared battery assets, more stable operating costs and service models that scale with fleet growth and long-term electrification strategies. Key deployment and scaling factors: Establish interoperability and standardization across OEMs and battery suppliers to enable safe, compatible swapping architectures. Deploy modular, automated swap stations at volume to achieve geographic coverage and operational efficiency for commercial fleets. Align with regional electrification and transport policies to integrate BaaS into city or national zero-emission mobility strategies. Develop viable service pricing structures that balance incentives across fleet operators, infrastructure providers and battery owners. Implement robust safety, data and battery health monitoring systems to ensure asset reliability and manage lifecycle performance. Case StudiesMitsubishi Fuso, Mitsubishi Motors, Ample, and Yamato Transport (2025), Japan In 2025, Mitsubishi Fuso Truck and Bus Corporation, Mitsubishi Motors Corporation, Ample and Yamato Transport announced a pilot program to deploy a battery-swapping network for commercial EV fleets across Tokyo. The multi-year initiative plans to introduce more than 150 battery-swappable electric vehicles, including light-duty trucks and compact commercial models, supported by approximately 14 modular, automated swap stations. The stations are designed to complete fully automated battery exchanges in roughly five minutes, reducing charging-related downtime and enabling continuous operations for last-mile logistics and delivery services. The Tokyo deployment builds on earlier pilot work in Kyoto and is supported by Tokyo Metropolitan environmental programs. The initiative illustrates how BaaS and battery-swapping models can enable higher-utilization fleets, support rapid electrification of urban logistics and test scalable operational frameworks for commercial transport applications in dense metropolitan areas.[1] Bandera Electric Cooperative and Base Power (2025), USAIn 2025, Bandera Electric Cooperative in Texas partnered with Base Power on a residential battery storage program that provides homeowners with affordable whole-home battery backup systems. Under this partnership, Base Power supplies and manages the battery systems while members pay a monthly fee as part of a subscription structure, with no upfront cost required for installation. The distributed network of home batteries can act as an aggregated resource, allowing the cooperative to operate the systems in real time to support grid performance and enhance resiliency. The program is structured so that the cooperative can balance demand and grid operations by drawing on the distributed battery capacity across participating homes, while members benefit from backup power during outages and potential savings through time-of-use optimization. This initiative reflects a utility-driven approach to deploying distributed energy storage through regulated partnership arrangements.[2]References: International Consortium Rolls Out Major EV Battery Swapping Initiative in Tokyo I Mitsubishi Motors (June 2025) Base Power and Bandera Electric Cooperative Announce Industry-First Distributed Battery Storage Partnership | AltEnergyMag (July 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012ar4AA/standard)
Battery-as-a-Service (BaaS) and EV battery swapping
![Cable-pooling enables multiple renewable generation assets, such as solar and wind, to share a single grid connection point rather than each constructing a separate one. Through digital controls and metering, participating plants coordinate their output to make better use of existing capacity. This smooths the combined generation profile and reduces the need for costly grid reinforcements or delays caused by connection constraints. As power systems face rapid electrification and renewable expansion, grid bottlenecks have become a major barrier. The International Energy Agency highlights that nearly 80 million kilometers of new or upgraded power lines will be needed globally by 2040 if traditional connection models persist, even though much existing capacity remains underused.[1] Cable-pooling directly addresses this inefficiency by maximizing the use of current infrastructure. It allows developers to co-locate complementary resources – for example, daytime solar and nighttime wind – to deliver more stable output and support system flexibility. Main outcomes of the solution Sustainability: Co-locating renewables assets and sharing infrastructure enables higher renewable penetration without expanding the grid, while reducing emissions and land use intensity. Affordability: Shared access to transmission or distribution capacity lowers capital cost by avoiding duplicate lines and reduces pressure on network upgrades, improving project- and system-level affordability. System Resilience: With diversified generation profiles and coordinated digital control, system operators gain enhanced ability to manage variable conditions and maintain operational stability. Key deployment and scaling factors Complementary asset design: co-location of renewable assets (e.g., wind and solar) to achieve balanced generation profiles and maximize shared connection utilization. Standardized technical and metering requirements with consistent rules for measurement, data-sharing and cost allocation to enable transparency among participating assets. Clear operational governance and protocols for dispatch coordination, maintenance responsibilities and compliance with grid-code requirements. Regulatory frameworks with explicit provisions for shared grid connections to remove regulatory barriers to multi-asset interconnection. Digital grid management tools (e.g. real-time monitoring, forecasting and predictive analytics) to coordinate pooled assets and maintain system stability. Incentive and tariff mechanisms that reward shared-infrastructure use and help reduce project connection queues. Case Study Polish Government and Energy Regulatory Office (2023), Poland In Poland, an amendment to the Renewable Energy Sources (RES) Act came into force on 1 October 2023, establishing a legal basis for cable-pooling.[2] The legislation enables multiple renewable installations to share a single grid connection point and related infrastructure, including use of pre-existing secured transmission capacity – for example, a photovoltaic plant operating via the connection of a previously developed wind farm, even if owned by different investors[3]. The reform was introduced in response to growing connection queues and capacity bottlenecks. Under the new provisions, developers can pool connections for co-located solar and wind plants, subject to metering, balancing, and operational agreements approved by the Energy Regulatory Office. Participants must sign a joint rights/obligations agreement, appoint a single entity to represent them to the grid-operator/regulator, and designate a balancing entity. Early implementation shows strong investor interest, as the analysis suggests that cable-pooling could enable up to 25 GW of additional renewable capacity in Poland without the need for new grid infrastructure, representing savings of approximately PLN 40 billion (≈USD 10 billion).[4] Poland’s initiative aligns with broader European efforts to accelerate renewable deployment through smarter grid access, positioning cable-pooling as a replicable approach for integrated and flexible energy systems. References: Electricity Grids and Secure Energy Transitions I IEA (revised November 2023) Amendment of the RES Act I Polish Energy Regulatory Office Cable-Pooling: On the Final Straight I Norton Rose Fulbright (August 2023) Potential of cable pooling in Poland I Forum Energii (May 2023)](https://fws.weforum.org/images/casestudies/aJYTG00000012cT4AQ/standard)
Cable-pooling
![A Clean Transition Tariff (CTT) is a utility rate structure that enables large electricity customers to support the procurement of new clean firm energy resources by agreeing to pay the cost associated with those technologies in exchange for rate credits based on the energy and capacity they provide. The model is intended to support system decarbonization by adding clean firm capacity that can operate during hours when variable renewable generation is insufficient. Unlike traditional green tariffs that often source energy from least cost renewable projects, a CTT is designed to enable utilities to develop or contract additive clean resources – such as enhanced geothermal – that would not typically be selected under standard least-cost planning. The participating customer receives defined energy and capacity credits from the resource, while regulators oversee the tariff to ensure that non-participating customers are not exposed to additional costs. Because the tariff provides a clear cost-allocation framework, it can facilitate commercial arrangements for early-stage clean firm technologies that require greater revenue certainty.[1,2,3,4]Main outcomes of the solution Sustainability: Enables deployment of additive clean firm capacity that displaces fossil generation during high-emission hours. By allowing a large customer to fund the incremental cost of technologies such as enhanced geothermal, CTTs support progress toward hourly clean energy matching and reduce reliance on fossil generation. Business Value: Provides a bankable pathway for large buyers to secure long-term clean firm supply with defined cost responsibilities and transparent energy and capacity crediting. This supports risk management for buyers while accelerating commercial maturity for emerging clean firm technologies. Socioeconomic Impact: Protects non-participating ratepayers by assigning all premium costs to the participating customer, while supporting local economic development and workforce transition associated with advanced clean energy projects. Key deployment and scaling factors: Establish clear regulatory authority for utilities to create tariffs. Secure large customers willing to make long-term commitments to new clean energy projects. Ensure availability of emerging clean firm energy technologies at commercial demonstration scale. Implement transparent cost-allocation mechanisms to balance customer benefits and system-wide impacts. Case Study Google, NV Energy (Berkshire Hathaway Energy) (2025), USA Nevada’s Clean Transition Tariff was approved to allow a large customer to support development of a new clean firm resource while remaining fully served by the utility. In June 2024, Google and NV Energy asked Nevada regulators for permission to enter into a power supply agreement based on the proposed Clean Transition Tariff. Under the arrangement, NV Energy procures energy from Fervo Energy’s 115 MW Corsac Station enhanced geothermal project and supplies it to Google under an Energy Supply Agreement structured within the CTT. The participating customer assumes the full cost associated with the project and receives defined energy and capacity credits on its electric bills, including credits that can offset demand charges. The Public Utilities Commission of Nevada approved the tariff and related agreements in March 2025 after determining that the design prevented cost shifts to other customers. The case has informed similar tariff discussions in other jurisdictions considering customer-funded procurement of clean firm energy resources within regulated utility frameworks.[1,2,3,4] References: Clean Transition Tariffs: An innovative way to accelerate power sector emission reductions - Center for Climate and Energy Solutions, Center for Climate and Energy Solutions (August 2024) NV Energy seeks new tariff to supply Google with 24/7 power from Fervo geothermal plant | Utility Dive (June 2024) Scale carbon free energy with clean transition tariff - The Climate Drive 24/7 Carbon-Free Electricity Transition Tariffs: A Regulatory Tool for Accelerating Decarbonization I Regulatory Assistance Project (March 2024)](https://fws.weforum.org/images/casestudies/aJYTG00000012e54AA/standard)
Clean transition tariff (CTT)
![Capital-intensive clean energy assets, such as nuclear power, large grid expansions, long-duration storage, carbon capture facilities and industrial decarbonization hubs, require substantial upfront capital, long payback periods and stable policy frameworks. Global energy investment is projected to reach about USD 3.3 trillion in 2025, with roughly USD 2.2 trillion flowing into clean energy technologies.[1] This level of spending reflects strong momentum across solar, storage, grids and electrification, yet investment remains concentrated in advanced economies, while many emerging and developing markets continue to face limited access to affordable capital. High financing costs, currency exposure and construction risk still constrain long-lived clean energy infrastructure, particularly in regions where system demand is growing fastest. These barriers slow deployment of firm low-carbon technologies that are important for system reliability. Financing models that combine public and private capital help improve bankability and reduce financing costs. Regulated Asset Base frameworks, blended finance structures, credit enhancement tools and multi-stakeholder ownership arrangements share risks across governments, utilities, investors and consumers, while creating more predictable long-term revenue streams. By improving investment certainty and mobilizing institutional capital, these structures support the scale-up of firm low-carbon technologies and the delivery of critical infrastructure for a resilient and affordable net-zero energy system. Main outcomes of the solution Affordability: Lowers the cost of capital for major clean energy assets, reducing overall system investment needs and helping manage long-term consumer cost exposure through more efficient capital deployment. Reliability and Flexibility: Accelerates investment in firm low-carbon generation, long-duration storage and modern grid infrastructure, supporting stable operation as renewable variability and electrification-driven demand increase. Robust financing frameworks ensure that reliability-enabling assets are not delayed by capital constraints. Institutional Efficiency and Transparency: Improves investment confidence through standardized contracts, predictable revenue frameworks and clearer regulatory signals, supporting sustained private-sector participation in system planning and delivery. This helps build a pipeline of bankable projects rather than one-off bespoke transactions Key deployment and scaling factors Establish revenue-stabilizing frameworks such as Regulated Asset Base models to provide predictable returns and sharing construction and demand risk for long-lived clean-energy assets. Expand blended-finance structures that use concessional capital, guarantees and risk-mitigation tools to mobilize large volumes of private and institutional investment. Create multi-stakeholder ownership and risk-sharing models that align incentives among governments, utilities, off-takers, communities and institutional investors. Standardize project preparation, due diligence and contracting to shorten development timelines and lower perceived legal and execution risk. Align regulatory and policy frameworks with long-term investment needs through clear roadmaps, stable remuneration mechanisms and predictable tariff-setting. Mobilize institutional investors through diversified investment vehicles, credit enhancement and transparent data that support scale and long-term capital allocation. Case Study Sizewell C and United Kingdom Government (2025), United Kingdom In 2025, Sizewell C secured a Final Investment Decision under a Regulated Asset Base (RAB) financing model that combines public and private capital. The structure allows regulated returns during construction and shares financing risk among the government, the developer, consumers, and private investors. The project comprises two European Pressurised Reactor (EPR) units with a combined capacity of approximately 3.2 GW, replicating the design used at Hinkley Point C. Once operational, Sizewell C is expected to provide long-term, low-carbon electricity, supplying power equivalent to that used by around six million homes over its operating life. The financing package combines government equity with private investment from La Caisse de dépôt et placement du Québec, EDF, Centrica and Amber Infrastructure, supported by long-term debt from commercial lenders and national and export-credit agencies. The project includes extensive domestic participation, with more than 70% of contract value awarded to UK businesses and a large workforce engaged during peak construction.[2,3] Solution and insights developed with contributions from the World Nuclear Association.References: World Energy Investment 2025 - 10th Edition I IEA (2025) Sizewell C gets green light with final investment decision I GOV.UK (July 2025) Sizewell C: Documents for the Sizewell C new nuclear power station I GOV.UK (July 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012nl4AA/standard)
Financing models and risk-sharing structures for capital-intensive clean energy projects
![Planning a carbon-neutral and reliable electricity system requires a full system, holistic perspective that provides the tools to understand the total system value and cost of the electricity system. However, traditional indicators such as the levelized cost of electricity (LCOE) assess technologies in isolation and offer a simplified view of costs, failing to capture system-wide interactions and the complementary roles of diverse power assets. As electricity systems decarbonize, these limitations become more pronounced, as planners should address flexibility to balance variable renewable output, adequate firm capacity to maintain resource adequacy, grid reinforcement to integrate new resources, and resilience to withstand disturbances. Focusing narrowly on clean generation rather than clean systems increasingly limits the relevance of conventional cost metrics for policy and planning. Emerging frameworks for full system cost assessment address these gaps by evaluating not only energy production but also contributions to grid infrastructure, flexibility, firm capacity and resilience. The full system cost assessment complements LCOE and other cost indicators, offering a clearer understanding of the economic implications of integrating different technologies into a modern electricity system under deep decarbonization pathways. To fully realize the value of such whole-system approaches, greater standardization of power system modelling practices is required. Aligning assumptions on scenario design, treatment of uncertainty and the system capabilities that models are expected to deliver is essential for transparent and comparable assessments. Cost-optimization modelling alone cannot capture the full picture, but a stronger emphasis on evaluating multiple scenarios and system performance outcomes can enhance policy-relevant insights. As modelling efforts converge around common definitions of system capabilities and how to measure them, this alignment can bridge the gap between technical analysis and practical policy guidance, supporting more effective market design and decision-making in the transition to a carbon-neutral electricity system.[1]Main outcomes of the solution Affordability: Supports cost-effective system planning by incorporating the full set of system costs, helping avoid misallocation of capital and reducing long-term expenditures. This enables investments in portfolios that may have higher plant-level costs but lower total system costs. Reliability & Flexibility: Captures operational requirements associated with integrating variable renewable energy, firm low-carbon technologies, storage and demand-side flexibility, enabling portfolios that can meet demand under variable conditions. Institutional Efficiency & Transparency: Provides a consistent and transparent framework for comparing technologies based on their full system value, improving decision-making and strengthening communication of trade-offs across institutions and stakeholders. Key deployment and scaling factors: Adopt whole-system cost assessment methodologies through agreement among planners, regulators and system operators. Ensure availability of robust data on system operations, grid investments, flexibility needs and resource adequacy. Develop analytical capability to model decarbonization scenarios and evaluate interactions across supply, storage, demand and grid assets. Coordinate among public institutions, research organizations and industry participants to maintain consistent application of the framework. Establish transparent assumptions, data governance and open protocols to support replicability and stakeholder trust. Case Study UNECE, LSE, NTNU, EPRI, WRI, Quantified Carbon, Newcleo, World Nuclear Association (2025), Global In 2025, UNECE began piloting the System Cost Breakdown of Electricity (SCBOE) framework to support future electricity-system planning, reflecting a broader shift in policy thinking from a focus on clean generation toward the performance of clean electricity systems as a whole. The pilot involves collaboration with the London School of Economics, NTNU, EPRI, the World Resources Institute, Quantified Carbon, Newcleo and the World Nuclear Association. The pilot examines how electricity-system costs evolve under multiple decarbonization pathways by assessing the full suite of system needs rather than evaluating technologies on generation cost alone. In doing so, the approach moves beyond cost-optimisation modelling focused on least-cost outcomes and places greater emphasis on scenario evaluation and system performance under different assumptions. UNECE notes that the framework captures dimensions such as grid infrastructure requirements, flexibility to balance variable renewable generation, system adequacy from firm low-carbon resources, and resilience across a range of operating conditions. By modelling these system-level interactions using more consistent definitions of system capabilities, the initiative highlights the importance of standardizing modelling practices across scenario design, treatment of uncertainty and performance metrics. This alignment is intended to enable more transparent and comparable power system assessments, providing planners and policymakers with a stronger basis for comparing technology portfolios, identifying long-term investment needs and designing cost-effective pathways to resilient, carbon-neutral electricity systems. This work helps mainstream full-system thinking, ensuring that energy sources are assessed based on their total contribution, not just plant-level cost. The pilot is ongoing, and results are intended to inform future national and regional planning processes across UNECE member states.[1] References: UNECE and partners launch initiative to develop a full system cost approach to guide investments towards resilient and carbon-neutral electricity systems | UNECE (December 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012pN4AQ/standard)
Full system cost frameworks
![Government-led investment platforms provide a structured mechanism to align national policy priorities with capital mobilization. They consolidate project pipelines, standardize evaluation processes and set transparent rules that build investor confidence. By integrating technical validation, risk-sharing instruments and blended finance structures, these platforms reduce transaction costs and lower barriers to private capital participation. Their core function is to replace fragmented, project-by-project financing with a coordinated, system-level architecture that aligns policy signals, capital allocation, and project preparation. Compared with traditional grant-based or bilateral approaches, this model offers greater consistency in screening and risk allocation, supporting more predictable investment decisions. The International Energy Agency (IEA) notes that well-designed public financing vehicles can support private investment by improving long-term policy visibility and providing targeted credit enhancement mechanisms.[1]Main outcomes of the solution Affordability: Reduced capital costs and shorter development timelines through aggregated pipelines and standardized financing processes, lowering system-wide investment requirements for clean power. Business Value: Improved bankability through de-risked financing structures and transparent selection criteria, expanding private-sector participation and supporting long-term value creation across the power sector. Institutional Efficiency & Transparency: Clearer governance, more consistent permitting pathways and transparent investment rules that reduce transaction complexity and align public objectives with market participation. Key deployment and scaling factors Establish clear, policy-aligned project pipelines to provide visibility on investable opportunities and support steady capital deployment. Deploy blended finance instruments, as targeted risk mitigation often determines whether private investors can participate. Standardize documentation and due diligence processes to reduce friction and enable replication across projects. Centralize technical validation to improve bankability and reduce the risk of project underperformance. Implement digital monitoring and portfolio-level data systems to enhance transparency and support scaling once the platform is operational. Case Study Brazilian Federal Government (2024), Brazil Brazil’s Climate and Ecological Transformation Investment Platform (BIP) is a government-led initiative launched in 2024 to connect transition-aligned projects with domestic and international sources of capital. The platform consolidates priority sectors such as renewable energy, low-carbon industry, nature-based solutions, and sustainable agriculture into a structured investment pipeline designed to improve project preparation and reduce transaction barriers for investors. BIP was launched during a side event of the G20 Finance Ministers and Central Bank Governors meeting in October 2024. BIP is led by Brazil’s Ministry of Finance, together with the ministries of Environment and Climate Change, Development, Industry, Commerce and Services, and Mines and Energy. The National Bank for Economic and Social Development (BNDES) acts as the Secretariat, managing day-to-day operations. In November 2025, Brazil’s Ministry of Finance, in partnership with BNDES and the Green Climate Fund, announced plans for a catalytic equity fund under the BIP framework, expected to launch in 2026. The partners indicated an initial concept under assessment, including an indicative fund size of more than $400 million, and stated an ambition to mobilize more than $1 billion from commercial investors and development finance institutions. The proposed vehicle is intended to combine concessional and commercial capital to address early-stage risk in priority sectors and support scalable investment pipelines rather than isolated projects.[2,3,4]References: World Energy Investment 2025 - 10th Edition I IEA (2025) Brazil Climate and Ecological Transformation Investment Platform I Ministério da Fazenda Brazil climate and Ecological Transformation Investment Plataform announces Plan for landmark $1 billion+ climate fund I Ministério da Fazenda (November 2025) Brazil Climate and Ecological Transformation Investment Platform Launches to Help Deliver Brazil’s Ambitious Development and Climate Goals | Glasgow Financial Alliance for Net Zero (October 2024)](https://fws.weforum.org/images/casestudies/aJYTG00000012qz4AA/standard)
Government-led investment platform