Geospatial Data Use in Energy Network Management

"𝑬𝒏𝒉𝒂𝒏𝒄𝒊𝒏𝒈 𝑬𝒍𝒆𝒄𝒕𝒓𝒊𝒄 𝑼𝒕𝒊𝒍𝒊𝒕𝒚 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 𝒘𝒊𝒕𝒉 𝑨𝒓𝒄𝑮𝑰𝑺 𝑼𝒕𝒊𝒍𝒊𝒕𝒚 𝑵𝒆𝒕𝒘𝒐𝒓𝒌"   In modern utility management, GIS is no longer just about creating nice-looking maps. It has evolved into a core component for network modeling, asset relationship analysis, and most importantly, data quality assurance (QA/QC) — ensuring that data is accurate, connected, and usable across enterprise systems.   we carried out a comprehensive QA/QC workflow on a substation within the electric utility network using ArcGIS Pro and the ArcGIS Utility Network tools. 1: Identify Existing Issues Several users reported problems in the substation data, including: - Unexpected or incorrect equipment appearing in the system. - Missing assets that should be part of the network. - Disconnected or incomplete electrical connections between components. 2: Generate a Network Diagram - We used Network Diagrams to explore the spatial and logical relationships between all features in the substation and visually identify inconsistencies. 3: Investigate Isolated Features - We discovered a series of high-voltage connectors that appeared unlinked to any other elements. - Using the diagram, we traced these to their physical location in the network map for validation. 4: Validate and Clean Up Network Topology - Zoomed into problem areas to check feature geometry. - Used topology validation tools to clean and update connections after edits. 5: Resolve Missing Connections - A transmission breaker was found connected only on one side (to the line) but not to the distribution substation. This missing connection was manually fixed in the diagram. 6: Restore Hidden or Missing Content - Some equipment and enclosures were not showing in the Asset Management System. These were added and linked properly to the substation network. 7: Create a Custom Diagram Template - Leveraged ModelBuilder to build a reusable diagram template. - Applied custom layout rules and symbology to highlight recurring data issues. 8: Analyze Street Lighting Connections - Created a network diagram for streetlight poles and lamps. - Identified lamps without poles (and vice versa). - Used Diagram Expand by Association to automatically add related elements for better validation. 9: Correct Streetlight Network Errors - Fixed missing associations between lamps and poles to ensure accurate feature representation and integrity. 𝗙𝗶𝗻𝗮𝗹 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: - Significant improvements in data accuracy and reliability. - Faster QA/QC cycles with better visibility into errors. - A trusted network model ready for use in asset management and operational planning. 𝐋𝐢𝐧𝐤 𝑻𝒖𝒕𝒐𝒓𝒊𝒂𝒍 : https://lnkd.in/dnihRAwQ #GIS #ArcGISPro #UtilityNetwork #QAQC #DataQuality #SmartGrid #Geospatial #DigitalInfrastructure #ESRI #ModelBuilder #ElectricUtilities

😮🗺️ The intelligence lives where your data already is 🗺️ 😮 At data:unplugged 2026 Alina Krämer and I showcased something different: an AI Emergency Dashboard built on a multi-agent architecture, where specialized subagents work in concert to not just understand a crisis, but act on it. Scenario: a power outage in Berlin. The agent pulls geospatial data, overlays affected infrastructure, and then does something most AI systems can't: it tells the team what to check next, runs spatial analyses, and coordinates resource planning based on the organization's own internal standards. That's possible because the agent doesn't just know your data: it knows your organization. Internal standards, spatial context, and operational priorities flow into every response. Not as a lookup. As reasoning. And because it runs on local LLMs, all of that stays exactly where it should: inside your organization. This is what makes geospatial AI agents fundamentally different from generic LLM assistants. The domain knowledge isn't bolted on. It's baked into how the agent thinks. Two things make this work at enterprise scale. Adapted infrastructure a platform built for agentic, spatially-aware AI workloads. And maintained metadata, because agents can only leverage domain knowledge if they can find and understand the data in the first place. Clean, structured metadata is no longer just good practice. It's the prerequisite for everything AI can do with your data. Every critical decision has a where. Now AI understands that too. #GIS #GeospatialAI #DomainKnowledge #ArcGIS #Esri #EmergencyManagement #RAG #EnterpriseAI #AIAgents

"Capacity maps provide an excellent starting point for addressing grid congestion. These maps can be developed to enhance transparency on available grid capacity, help identify where new investments are needed and attract innovative solutions by clarifying the causes and costs of congestion. Geographic information about grid capacity and potential new connections is also useful when designing incentives for co-locating supply and demand. EV charging, for example, can be built in locations where rooftop solar PV exists to minimise the need to transport the electricity through the distribution grid. As many countries have developed similar capacity maps, network operators can inspire and learn from each other to make the information presented as transparent and useful as possible. Leveraging flexibility from distributed energy resources such as rooftop solar PV, home battery systems, EVs and smart appliances is also crucial for managing grid congestion. This requires modern communication and control equipment, as well as appropriate price signals to ensure their consumption and production behaviour benefits the entire energy system. The rapid growth in grid-scale battery storage systems can also provide important solutions for grid congestion. To ensure that flexibility assets are operated in a way that helps solve grid congestion, locational and operational price signals should reflect the situation of the local grid, as well as the national system, including through new contract forms and local flexibility markets." https://lnkd.in/eNWt-GJN

⚡Smarter RAN Energy Savings with AI and Geolocation RAN energy savings have become a top priority for mobile operators, as energy costs account for 20–40% of total network #OPEX. Every gain in efficiency directly reduces expenses, improves margins, and supports sustainability goals. However, optimizing RAN energy use is a complex balancing act. Operators must ensure that savings don’t compromise performance, quality of service, or violate SLAs. Emerging technologies like geolocation and #AI are reshaping how networks manage power. Geolocation enables subscriber-centric optimizations, allowing networks to identify when and where demand drops, and dynamically put cells or antennas into sleep mode. This ensures power savings with minimal impact on user experience. Meanwhile, AI-driven energy management takes this further — continuously learning from traffic patterns, predicting demand, and orchestrating cell sleep and MIMO sleep modes in real time. This closed-loop approach ensures that energy reductions never come at the cost of reliability or service quality. Key innovations include: - Cell Sleep Mode: Uses ML to predict low-traffic periods and put entire 4G/5G cells into sleep or deep-sleep states, reactivating instantly as demand returns. - MIMO Sleep Mode: Dynamically disables selected antenna branches to reduce power draw while maintaining coverage and performance. Together, AI and geolocation are enabling the next generation of intelligent, sustainable RANs — where efficiency and experience go hand in hand.

💡 𝑾𝒉𝒚 𝑮𝑰𝑺 𝑯𝒂𝒔 𝑩𝒆𝒄𝒐𝒎𝒆 𝑬𝒔𝒔𝒆𝒏𝒕𝒊𝒂𝒍 𝒇𝒐𝒓 𝑴𝒐𝒅𝒆𝒓𝒏 𝑴𝑽 𝑮𝒓𝒊𝒅 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈. Geographical Information Systems are a GIS-specific applications. However, GIS does not simply provide maps – it is used to provide insight into MV grids (in this context) on four levels: 1. 🗺️ 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 What it does: Provides digital representations of networks, including feeders, poles, customers and topology throughout the entire area in which they are located. The Benefit: Optimised Feeder Routing/Route Patterns, Forecasted Loads and the Ease of Planning for Distributed Energy Resource Integration (eg solar/EV). 2. 🛠️ 𝐅𝐚𝐬𝐭 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 What it does: Houses and manages all assets in the same system to ensure all users have access to all updated data for their assets. The Benefit: Instant Fault Location, Reduced Outage Time, and Shifted Maintenance Focus from Reactive Repairs to Predictive Maintenance Activity. 3. 🔬 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐏𝐨𝐰𝐞𝐫 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 What it does: Provides engineering tools (ie PowerFactory/ETAP) with accurate data on network length, topology, impedance, etc. The Benefit: Higher Fidelity Simulation Results, Improved Voltage Control and Control Planning and Execution of Safe Grid Expansion. 4. 🤖 𝐒𝐂𝐀𝐃𝐀 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 What it does: Georeferences and maps all remotely-controlled devices in GIS and connects those points directly to Control System (ie DMS). The Benefit: Rapid identification of Faults, Enhanced Situational Awareness During Incidents, and the Establishment of the Foundation for Self-Healing Automated Grids. Overall, GIS will help transition the challenge of building complex MV Networks to Data-Driven, Predictive and Highly Automated Grid Organizations managing and providing information on the grids. #GIS #SmartGrid #UtilityTech #PowerDistribution #DigitalTwin

#Geospatial intelligence is no longer just about maps. For electricity transmission and distribution (T&D) companies, it's becoming a critical tool for managing the demands of an AI-powered world — sustainably. A recent Forbes Technology Council article, https://lnkd.in/eNuNDWMZ by Venkat Kondepati put it plainly: if we don't plan proactively for AI's resource consumption, we risk real consequences for local communities, water supplies, and the grids we depend on. For T&D operators, that's a direct operational challenge. Here's what GIS makes possible today: ⚡ Digital Twins of grid assets — enabling real-time load analysis and early detection of capacity constraints before failures occur 🌞 Renewable load balancing — scheduling demand around solar and wind availability to reduce grid pressure and maximise clean energy use 🔍 Proactive capacity planning — evaluating grid reliability and renewable potential spatially, rather than reacting to connection requests 🌊 Environmental risk mapping — understanding the relationship between infrastructure, water resources, and community impact as regulatory expectations grow And #AgenticAI takes this further — moving from insight to action. Rather than surfacing analysis for human review, agentic systems can autonomously detect anomalies, trigger maintenance workflows, and flag environmental threshold breaches, all grounded in real-time spatial data. The organisations investing now in digitising assets and building strong spatial data foundations will be best placed to deploy these capabilities at scale. The grid of the future will be intelligent, spatially aware, and proactively managed. #EnergyTransition #GeospatialIntelligence #SmartGrid #AgenticAI #TransmissionAndDistribution #Sustainability

A Site Selection Framework For Urban Power Substation At Micro-Scale Using Spatial Optimization Strategy And Geospatial Big Data -- https://lnkd.in/gUAZ8sKd <-- shared paper -- H/T Peng Luo “In this study, [they] model spatiotemporal heterogeneity and incorporate it into optimizing the location of substations. The optimized substation placement ensures electrical service coverage for over 99% of the area during peak power usage seasons, compared to the current coverage of 72%...” -- “The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unreasonable energy transfer and waste, leading to power outages in some areas. Aiming to maximize the grid coverage and transformer utilization, [they] propose a multi-scene micro-scale urban substation siting framework (UrbanPS): (1) The framework uses multi-source big data and the machine learning model to estimate fine-scale power consumption for different scenarios; (2) the region growing algorithm is used to divide the power supply area of substations; and the (3) location set coverage problem and genetic algorithm are introduced to optimize the substation location. The UrbanPS was used to perform siting optimization of 110 kV terminal substations in Pingxiang City, Jiangxi Province. Results show that the coverage and utilization rate of the optimization results under different power consumption scenarios are close to 99%. [They] also found that the power can be saved by dynamic regulation of substation operation…” #GIS #spatial #mapping #spatialanalysis #spatiotemporal #siting #demand #electricity #heterogeneity #substations #powertransmission #electricalpower #distrubition #service #city #urbanisation #extremeweather #model #modeling #parameters #factors #energycrisis #energy #urbanplanning #routing #outages #framework #UrbanPS #bigdata #AI #machinelearning #Pingxiang #Jiangxi #China #casestudy #coverage #utilisation #dynamic #load #loading #loadbalancing

🌐 How do you plan for the unthinkable? Hawai‘i’s award-winning Geospatial Decision Support System (GDSS) is transforming how we approach disaster preparedness. Using GIS mapping, the tool identifies the relationships between energy infrastructure and the lifelines that keep our communities functioning. 💡 Here’s why it’s a game-changer: -It calculates the risk of disruptions to critical infrastructure like substations, pipelines, and power plants. -It visualizes cascading impacts, helping us understand which systems are most vulnerable to flooding, high winds, or other disasters. -It prioritizes actions to protect the most vital links in our infrastructure chain. For Hawai‘i, this means smarter strategies to strengthen our grid and protect our communities. For the rest of the world, this is a lesson in using data to drive resilience. Mahalo to the team at the Hawaii State Energy Office for their hard work in making this tool available. What other regions could benefit from such a proactive approach? Let’s discuss in the comments! 👇 #GIS #Microgrids #EnergyInnovation #ResilientCommunities #AJPerkins #MicrogridMentor

With the right systems in place, Earth Observation (EO) - combining satellite data, sensors and machine learning - can become core digital infrastructure for climate-resilient energy development. With over 15,000 satellites expected to launch this decade, governments across the Global South are starting to operationalize geospatial data for energy access, climate resilience and investment planning. With fewer than 20% of African countries integrating EO into national energy plans, there's a clear opportunity to transform decision-making - from pixels to policy: 🇪🇬 Egypt and 🇿🇦 South Africa are leveraging CBERS satellite imagery to identify optimal sites for renewable energy projects. 🌐 GeoSUR connects over 100 agencies across Latin America to co-manage geospatial infrastructure, accelerating regional coordination and planning. 🇧🇷 Brazil’s EO+AI platform combines satellite and climate data to map energy needs across five major biomes — driving smarter, climate-informed investment. South–South initiatives like CBERS and GeoSUR are proving that the Global South isn’t waiting; it’s already leading the digital revolution. Explore the opportunities for energy and EO in our latest edition of the Sustainable Energy Bulletin: 👉 https://lnkd.in/e7PmUFtw #EnergyForDevelopment #EarthObservation #DigitalInfrastructure #GeospatialDevelopment

What you’re seeing is how #utilities using GIS view their entire infrastructure, asset base, and customer connections, gaining unprecedented visibility into grid health, performance, and network operations. #GIS empowers utilities to proactively manage maintenance schedules, spot potential issues before they escalate, and identify optimal areas for expansion. Real-time monitoring and advanced analytics provide the ability to analyze trends, assess risks, and make data-driven decisions. Coupled with #IoT and the integration of sensors for real-time data collection, utilities can make their grids fully #smart. These sensors provide critical information such as equipment status, temperature, frequency, and other variables that help predict and prevent trips, breakdowns, and downtime. This comprehensive insight also enables more accurate revenue projections, aligning financial planning with actual network demands. In essence, GIS puts the world at an organization’s fingertips, transforming #maps into powerful tools for smarter operations, effective monitoring, and strategic planning.