Harmonized product specifications
Grid-safe connection standards for large loads
Parallel and risk-based permitting
Integrated environmental and land-use planning
Grid infrastructure investment trusts
Long-term smart grid transformation frameworks
![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
![AI-enabled microgrids integrate onsite renewable generation, battery energy storage systems (BESS) and intelligent energy management algorithms to optimize local energy use, strengthen resilience and support flexible operation in both grid-connected and islanded modes. These systems typically combine machine learning forecasting with model-predictive or optimization-based control, using short scheduling intervals to determine when to consume, store or export electricity based on expected demand, renewable availability and tariff signals. A cloud edge architecture is often used, with forecasting and optimization performed centrally and real-time dispatch executed locally by the microgrid controller. By coordinating solar PV, batteries and backup generation with advanced supervisory controls, AI-informed microgrids support continuity of supply during grid disturbances, reduce energy costs through peak-shaving and tariff optimization, and increase utilization of onsite renewable energy. Environmental benefits depend on local grid mix characteristics, tariff design and available flexibility, but AI can help align economic and sustainability outcomes where regulatory and system conditions are supportive. AI-enabled microgrids are increasingly deployed in commercial facilities, airports, industrial sites, campuses and remote or underserved regions, where they provide cleaner and more reliable power without extensive grid upgrades. Main outcomes of the solution Reliability & Flexibility: Supports more stable operation by enabling controlled transitions between grid-connected and islanded modes, and by applying predictive or optimization-based control to maintain balanced microgrid performance under variable renewable output and demand. System Resilience: Enables real-time local balancing and supports autonomous operation during outages or extreme events, helping maintain priority loads and critical services when the main grid is compromised. Business Value: Helps reduce energy costs through peak shaving and time-of-use optimization, increases the effective use of onsite solar and storage assets and may enable participation in demand-response or export programs where permitted. Key deployment and scaling factors Integrate onsite DERs (solar PV, BESS, backup generators and flexible loads) with an energy-management system incorporating forecasting and optimization capabilities. Implement predictive or optimization-based control to schedule generation, storage and load operation in accordance with tariffs, demand patterns and resilience priorities. Ensure robust real-time control and protection, cybersecurity measures and support for controlled transitions between grid-connected and islanded modes. Use modular or pre-engineered microgrid designs to simplify deployment and enable replication across commercial, industrial or remote applications. Define operational strategies and configuration settings for resilience, including islanding procedures, renewable-first dispatch, peak-shaving logic and DER prioritization. Ensure interoperability with utility and DER communication standards to support integration with grid operations. Align tariff structures and grid mix characteristics to ensure that cost-optimized dispatch supports low-carbon operation where system conditions allow. Case study Schneider Electric (2023), Global In 2023, Schneider Electric began a two-year empirical assessment of 11 operational microgrids across Europe, North America and Australia to evaluate how AI-based optimization affects operating costs and greenhouse gas emissions. Using the EcoStruxure™ Microgrid Advisor platform, the study tested whether economic optimization alone can deliver emissions reductions under real-world tariff and grid conditions. Results show that AI can reduce both costs and emissions where price signals reflect carbon intensity, but can increase emissions where those signals diverge. AI therefore amplifies existing market incentives rather than acting as an independent decarbonization mechanism. A 24/7 agrifood cold-storage logistics facility in South Australia demonstrated the strongest alignment between cost and emissions outcomes. The site operates 2.5 MW of solar PV (14.7:1 PV-to-load ratio), supported by battery storage and high thermal inertia from refrigeration. Compared with heuristic control, AI-based dispatch reduced annual electricity costs by €124,500 (–21.6%) and emissions by 328.6 tCO₂e, yielding an abatement cost of – €379/tCO₂e. The optimization objective was purely economic, with no explicit carbon constraints. Three implementation features enabled this outcome. First, high PV penetration with export capability allowed surplus solar generation to be consumed onsite or exported, displacing higher-carbon grid generation. Second, thermo-electrical flexibility enabled pre-cooling during low-carbon midday periods and reduced grid reliance during evening peaks. Third, alignment between time-of-use prices and carbon intensity ensured that cost-minimizing dispatch avoided high-emissions periods: low-price midday electricity coincided with low carbon intensity, while high-price evening periods coincided with higher emissions. The same AI logic produced different outcomes in other regions. In California sites, where prices reflected capacity scarcity rather than emissions intensity, AI optimization reduced costs by ~€27,500 but increased emissions by 51 tCO₂e. Across the portfolio, successful replication required four conditions: tariffs correlated with carbon intensity, sufficient onsite renewables, operational flexibility (battery and/or thermal), and permission to export electricity. Where these conditions were absent, AI improved economics but did not support emissions reduction.[1,2] References: Accelerating Microgrid Projects: From Start to Finish I Schneider Electric (June 2025) AI-Powered Microgrids: The Path Toward Coupling Economic and Environmental Benefits I Schneider Electric (October 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012J74AI/standard)
AI-powered microgrids
![Extreme weather events – including large hail – are becoming more frequent and severe, creating new operational risks for utility-scale solar assets. Traditional manual risk-mitigation approaches often cannot respond quickly enough to rapidly forming convective storms. Automated, data-driven climate-resilience systems use predictive meteorology, engineering insights and machine-to-machine automation to anticipate potential impacts and initiate protective actions ahead of damaging events. These systems combine long-range weather monitoring, short-range storm prediction and automated tracker control to reduce exposure to hail-related damage and support asset availability. By embedding meteorological intelligence directly into operational workflows, operators can move from reactive, manual intervention toward more proactive, automated protection strategies that help preserve generation output and extend asset lifetimes. Main outcomes of the solution Reliability & Flexibility: Reduces the likelihood of unplanned outages and asset damage by automatically positioning PV trackers in defensive stow configurations ahead of hazardous weather events. System Resilience: Improves the ability of solar plants to withstand extreme hail conditions through predictive weather modelling, rapid automation and enhanced operational standards. Business Value: Avoids or limits costly equipment damage and replacement, reduces post-event downtime and supports continuity of energy production during extreme-weather events. Key deployment and scaling factors Integrate long-range and short-range meteorological intelligence to provide early situational awareness and precise storm-cell forecasting. Deploy automated tracker-control systems capable of receiving digital hazard signals and autonomously moving PV modules into predefined defensive stow positions. Develop geospatial visualization tools to support real-time monitoring, operator oversight and rapid event response. Embed engineering research and procurement specifications that reduce hail vulnerability at the module-design and operational-standards level. Establish operational protocols that link predictive alerts to automated actions while maintaining availability outside defined hazard windows. Case StudyLightsource bp (2021), USA Lightsource bp developed Project Whiskeyball to address increasing hail risk for utility-scale PV assets. The program combines engineering research, predictive meteorology and automated control to shift hail-response operations from manual and reactive processes toward digital, anticipatory protection. The system uses a multi-layered defense approach. Long-term storm pattern monitoring provides early awareness, enabling operations teams to proactively schedule defensive measures. Short-range automation approach where, if storms develop unexpectedly, predictive algorithms assess whether approaching cells will produce hail and calculate potential impact trajectories relative to solar assets. When credible risk is identified, PV trackers automatically move to a defensive stow position, typically 30 minutes before projected impact. Advanced geospatial visualization tools accelerate monitoring and response times, ensuring complete situational awareness during rapidly evolving weather events. Finally, ongoing collaboration with research laboratories continuously refines procurement specifications and operational standards, strengthening the system's resilience. The system’s performance was demonstrated during a severe hail event in Pueblo, Colorado in July 2023. While surrounding areas experienced damage, the automated stow procedure was executed as designed and no material damage was reported at the protected site. Lightsource bp has also indicated that the presence of a documented, automated hail-mitigation framework supports risk discussions with insurers, although outcomes remain asset- and market-specific.[1]References:Company information provided to World Economic Forum I Lightsource bp (December 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012Kj4AI/standard)
Automated climate-resilience systems for PV assets
![Behind-the-meter power systems allow energy-intensive facilities – such as data centres, industrial plants and large commercial campuses – to generate or store electricity directly on-site rather than relying exclusively on the public grid. By integrating local energy sources such as natural gas cogeneration, solar PV, battery storage or, where feasible, hydrogen-ready turbines, organizations can reduce exposure to grid congestion and improve operational reliability during peak demand. Behind-the-meter assets also help facilities align consumption with on-site renewables, cutting emissions and strengthening resilience. Analysis by the International Energy Agency (IEA) indicates that decentralized energy resources can support system reliability and improve resilience to grid disturbances.[1] The International Renewable Energy Agency (IRENA) notes that on-site generation paired with storage enhances operational flexibility and mitigates stress on regional grids.[2]Main outcomes of the solution Sustainability: Enables the use of on-site cleaner energy sources, lowering emissions and reducing reliance on carbon-intensive grid electricity; support integration of low-carbon fuels and emerging technologies into industrial and data center operations. Affordability: Reduces exposure to wholesale price volatility and provides greater long-term cost predictability for mission-critical facilities; avoids costly transmission upgrades, connection-queue delays and certain tariff components (e.g., demand charges, transmission fees), improving economics where grid congestion or high connection costs would otherwise increase total energy costs. Reliability & Flexibility: Improves resilience during peak demand, outages or grid congestion by allowing facilities to rely on on-site generation or storage; supports continuity of operations in mission-critical facilities such as data centers, advanced manufacturing sites and logistics hubs. Key implementation and scaling factors Conduct early assessments of load profiles, site constraints, permitting requirements and environmental regulations to confirm the feasibility of on-site generation or storage. Use modular, standardized designs and ensure seamless integration with existing and future facility infrastructure, including mission-critical equipment. Implement rigorous safety, compliance and maintenance frameworks that address hydrogen handling, battery safety codes, emissions standards and operational reliability. Secure resilient fuel-supply and logistics arrangements for natural gas, hydrogen or other clean fuels to ensure uninterrupted behind-the-meter operation. Deploy advanced digital control and energy-management systems to coordinate on-site generation, storage and grid interactions in real time. Address regulatory and market conditions – including tariff structures, standby charges, private-wire rights and self-generation rules – that determine project viability. Manage applicable interconnection, protection and metering requirements to remain compliant with grid-code obligations even when operating primarily behind the meter. Case Study Energy Abundance Development Corp. (under development, 2026 onwards), USAIn Texas, Energy Abundance Development Corp. (formerly Green Hydrogen International) is developing Data City – what it describes as “the world’s largest behind-the-meter data center”– designed to operate independently of the public grid and to be powered by dedicated on-site and local clean fuel generation. The planned 50,000-acre campus near Laredo will initially rely on locally sourced natural-gas generation, with a phased transition to 100% green hydrogen stored in nearby salt-dome caverns.[3]According to the developer, the project will be built in phases, beginning with 300 MW of data-center capacity and 1 million square feet of data-center space targeted for 2026, with an envisaged long-term expansion pathway of up to 5 GW and more than 15 million square feet of leasable space as demand grows. A related green energy hub near Corpus Christi is expected to produce approximately 280,000 tons of green hydrogen and 1 million tons of green ammonia per year, using wind and solar power. As a forward-looking development, the Texas project illustrates how large industrial and data center facilities may use behind-the-meter generation and dedicated clean-fuel supply to secure power availability, stabilize long-term energy costs and bypass grid-connection constraints while supporting the growth of high-density operations. References: Unlocking the Potential of Distributed Energy Resources I IEA (May 2022) Innovation Landscape for a Renewable-Powered Future I IRENA (2019)Energy Abundance Announces Data City, Texas - The World's Largest Behind-the-Meter Data Center Hub Powered by 100% 24/7 Green Energy I PR Newswire (March 2025)](https://fws.weforum.org/images/casestudies/aJYTG00000012ML4AY/standard)
Behind-the-meter
![Behind-the-meter batteries installed at homes, commercial buildings, or industrial sites store electricity on the customer side of the meter, allowing users to manage when and how they consume or export energy. By shifting load, shaving peaks, and supporting self-consumption of on-site renewables, these systems reduce strain on the grid and help smooth local demand profiles. When paired with digital controls, behind-the-meter batteries can also participate in demand-response programs, aggregation schemes and virtual power plants (VPPs). As electrification of end uses increases and renewable output becomes more variable, grid operators face rising challenges from peak loads, local congestion and system balancing. Analysis by the International Energy Agency (IEA) highlights that distributed batteries can provide fast and flexible response and support system stability without major grid upgrades.[1] When integrated into VPPs, behind-the-meter batteries can deliver services such as frequency regulation, peak reduction, reserve capacity and congestion management. Main outcomes of the solution Affordability: Lowers energy costs by reducing peak-demand charges, enabling time-shifting to lower-priced hours, and helping avoid or defer costly grid upgrades where local constraints would otherwise increase system costs. Reliability & Flexibility: Enhances operational continuity by providing fast-response backup during outages or congestion, while enabling flexible dispatch of stored energy for self-consumption, load shifting and local balancing. System Resilience: When aggregated and digitally coordinated, functions as distributed flexibility assets that support frequency stabilization, manage local constraints and reduce reliance on thermal peaker plants during extreme-weather events. Key deployment and scaling factors Analyze load profiles, tariff structures and usage patterns to size batteries correctly and maximize value streams. Use standardized metering and communication protocols for seamless integration with energy-management systems, aggregators and grid operators. Deploy advanced control systems to optimize charging and discharging in real time and coordinate safely with on-site renewables. Ensure compliance with fire-safety codes, permitting requirements and interconnection standards for distributed batteries. Create supportive regulatory frameworks enabling BTM batteries to participate in demand-response and grid-service markets. Establish modular system designs and strong vendor partnerships to streamline rollout and asset management. Address interconnection, protection and metering rules to enable safe import/export capability and VPP participation. Case Studies California Energy Commission and California Independent System Operator (CAISO) (2025), USA California’s Demand-Side Grid Support (DSGS) program compensates households and aggregators that discharge behind-the-meter home batteries during grid stress events, such as heatwaves. DSGS has become a key element of California’s demand-side reliability strategy, complementing CAISO’s resource adequacy framework.[2] In 2025, DSGS recorded its largest activation to date: over 100,000 home batteries delivered approximately 539 MW over two hours, demonstrating the scale and responsiveness of aggregated behind-the-meter resources.[3] Programme guidelines expanded available capacity to around 500 MW, enabling participation from aggregators including Sunrun, Tesla Energy, OhmConnect and others. Under the program framework, aggregators coordinate battery dispatch, CAISO validates performance, and the program administrator Olivine manages settlement and compliance. DSGS is expected to deliver net system savings of approximately USD 28 million to USD 206 million over 2025–2028, depending on market conditions.[3] These savings are driven by avoided procurement of high-cost peak-generation resources and reduced reliance on emergency capacity measures during grid-stress events. Iron Mountain Data Centers (2024), USA In 2024, Iron Mountain Data Centers, in collaboration with the Virginia Department of Energy and with funding from the U.S. Department of Energy, initiated a project to deploy behind-the-meter battery energy storage at its Prince William County data center campus in Virginia. The project is supported by an award of approximately USD 50 million under DOE’s Grid Resilience and Innovation Partnerships (GRIP) program, with additional cost-sharing from private partners. The battery system is installed on the customer side of the meter and is designed to support load management at a large, power-intensive data center site. The system will enable Iron Mountain to shift electricity consumption by charging during lower-cost periods and discharging during peak demand, reducing stress on the local distribution network and supporting more efficient use of grid infrastructure as on-site power demand grows. In addition to load shifting, the battery is intended to improve site-level resilience by supporting critical IT operations during grid disturbances and reducing reliance on diesel backup generation during outages. The project is being used to assess how digitally controlled, behind-the-meter storage at hyperscale and enterprise data centers can contribute to both operational continuity and broader grid-flexibility objectives. The initiative is structured as a replicable deployment model for other data-intensive campuses, providing insights into technical integration, regulatory coordination and the role of large behind-the-meter assets in supporting system reliability as electrification and data-center demand continue to expand.[4, 5] References: Unlocking the Potential of Distributed Energy Resources I IEA (May 2022) Demand Side Grid Support Program I California Energy Commission Calfornia's home batteries are replacing gas plants and saving hundreds of millions I PV Magazine (September 2025) Innovative transmission, energy storage projects in 18 states get USD 2.2B from DOE I Utility Dive (August 2024)IMDC gets grant of USD 50M for developing on site battery storage by VA DoE I Iron Mountain (August 2024)](https://fws.weforum.org/images/casestudies/aJYTG00000012Nx4AI/standard)
Behind-the-meter battery integration
![Carbon capture, utilization and storage (CCUS) encompasses technologies that capture CO₂ emissions from industrial facilities, gas processing plants and fossil-fuel power stations before they enter the atmosphere. Once captured, CO₂ is compressed and transported by pipelines or ships for permanent storage in deep geological formations, or used as feedstock for products such as synthetic fuels, chemicals or construction materials. The International Energy Agency (IEA) highlights CCUS as a critical lever for reducing emissions in hard-to-abate sectors such as cement, steel, chemicals and gas processing.[1] According to the International Renewable Energy Agency (IRENA), achieving global net-zero emission by 2050 would require around 6 Gt of CO₂ per year to be captured and stored by 2040 and more than 8 Gt per year by 2050, across industrial carbon capture and storage (CCS), bioenergy with CCS (BECCS), direct air capture with storage (DACCS) and other carbon-removal pathways. IRENA characterizes the role of carbon capture as “moderate but indispensable” in 1.5°C global energy transition scenario.[2]Main outcomes of the solution Sustainability: Delivers durable emissions reductions by capturing CO₂ at industrial sites and storing it permanently in suitable geological formations, while enabling utilization pathways that provide verifiable long-term climate value. System Resilience: Provides a predictable, infrastructure-based abatement route that supports industrial compliance with tightening climate policies, offering stable, weather-independent emissions mitigation where appropriate geological storage is available. Reliability & Flexibility: Enables reliable production of low-carbon hydrogen, ammonia and natural gas, and supports scalable multi-emitter cluster-based participation through shared CO₂ transport and storage systems that help distribute costs and risks. Key deployment and scaling factors Conduct reservoir characterization and injection-capacity assessments, and develop monitoring frameworks to assess long-term storage integrity, capacity and permanence. Develop shared CO₂ pipeline, compression and storage hubs to reduce costs and streamline multi-emitter participation. Develop and implement regulatory frameworks covering permitting, long-term liability, reporting requirements and cross-border CO₂ transportation. Use modular capture technologies to reduce installation timelines and standardize integration with industrial processes. Establish commercial models for long-term offtake, transport and storage services to enable cost-sharing across clusters. Coordinate closely among industrial operators, pipeline developers, technology vendors and geological agencies to accelerate project delivery. Deploy digital monitoring systems and advanced subsurface modelling to ensure safe, permanent storage performance over time. Advance credible standards for emissions accounting and reporting for CCS power generation projects. Case Studies Petronas, MMHE, NPCC, McDermott and Baker Hughes (Expected 2026), Malaysia Petronas’ Kasawari CCS project in the SK316 gas field offshore Sarawak is designed to capture and store up to 3.3 million tonnes of CO₂ per year, making it one of the largest offshore CCS developments in Asia and globally. CO₂ separated during gas production will be compressed and transported through a subsea pipeline to the depleted M1 reservoir for permanent geological storage. The project is being developed by Petronas Carigali, with engineering contributions from MMHE, NPCC, McDermott and Baker Hughes. Kasawari forms a central element of Malaysia’s strategy to build regional CO₂ storage capacity by leveraging existing offshore assets and favorable geological potential. It demonstrates how large natural-gas operations in Southeast Asia can integrate CCUS at scale, supporting emissions reduction while laying foundation for future multi-emitter transport and storage networks. [3,4,5,8]Low Carbon Infrastructure and Google (2025), USA The Broadwing Energy project in Decatur, Illinois is a natural-gas cogeneration power plant of over 400 MW and greater than 1,500,000 lbs. steam per hour under development by Low Carbon Infrastructure (LCI) and designed with integrated post-combustion carbon capture targeting around 90% CO₂ removal. The Broadwing plant will produce power for Google as well as power and steam for Archer Daniels Midland (ADM). The captured CO₂ is expected to be transported to EPA Class VI geological storage wells operated by ADM, which has already carried out nearly a decade of commercial-scale CO₂ injection, storage and monitoring at their Decatur site. Under a long-term agreement to procure clean, firm electricity, Google intends to use Broadwing as part of its strategy to secure round-the-clock decarbonized power for its data-center operations. The project will also incorporate a new standard for CCS-specific Energy Attribute Certificates (EACs), developed by industry experts to ensure CSS projects can be accurately quantified in emissions reporting. [6] The project shows how CCS can be paired with dispatchable gas generation to support reliable, lower-carbon supply in systems with rising electricity demand. Broadwing is the first project in a longer-term collaboration between Google and LCI highlighting an emerging replicable procurement model in which corporate clean power commitments help enable investment in firm low-carbon assets and early development of next-generation CCS projects in the US power sector.[7]Pertamina, J-Power, JANUS, JGC and ITB (2025), Indonesia The Gundih CCS pilot in Central Java is Indonesia’s first planned onshore carbon-capture and storage project. The design targets the capture of around 300,000 tons of CO₂ per year from the natural-gas processing stream at the Gundih Gas Field, with compression and injection into the nearby Kedungtuban geological structure for permanent storage. The project is being developed by Pertamina in collaboration with J-Power, JGC Corporation, JANUS, and the Institut Teknologi Bandung (ITB), building on feasibility work supported through Japan’s Joint Crediting Mechanism.[8,9]Technical studies to date include reservoir characterization, seismic surveying and front-end engineering design for capture, compression and monitoring systems. While injection operations have not yet begun, the feasibility work indicates an estimated total storage potential of around 3 million tons of CO₂ over a 10-year injection period, subject to reservoir performance and regulatory approvals.[8,9]Gundih is intended to demonstrate the technical and regulatory foundations for CCS deployment in Indonesia following the issuance of Presidential Regulation No. 14/2024 on CCS/CCUS.[10] The project is expected to help build domestic storage expertise, validate Indonesia’s onshore storage capacity and provide an early model for integrating CCS into natural-gas operations across the ASEAN region. References: Net Zero Roadmap: A Global Pathway to Keep the 1.5C Goal in Reach I IEA (2023) Carbon Capture I IRENA McDermott Secures Offshore Contract for the Kasawari Carbon Capture and Storage Project in Malaysia I McDermott (January 2024) Kasawari Gas Development Project, Sarawak, Malaysia I Offshore Technology (February 2023) PETRONAS Carigali Reaches Final Investment Decision for Kasawari CCS Project Offshore Sarawak I Petronas (November 2022) Carbon Capture and Storage Energy Attribute CertificatesOur first carbon capture and storage project I Google (October 2025)CCUS Activities in Indonesia (October 2020) Innovation can accelerate Southeast Asia's energy transition I Atlantic Council (October 2024) Indonesia Moves to Provide Solid Legal Basis for Carbon Capture & Storage I ABNR (April 2024)](https://fws.weforum.org/images/casestudies/aJYTG00000012PZ4AY/standard)
Carbon capture, utilization and storage (CCUS)
![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