Grid Data Analysis for Energy Planning

🏠⚡ Real-world smart meter data reveals how heat pumps, EVs, solar, and battery are reshaping electricity demand ⚡🏠 New analysis from Energy Systems Catapult's Living Lab shows how low-carbon technologies - solar, battery, EVs, and heat pumps - are fundamentally changing residential energy consumption patterns. Using smart meter data from hundreds of UK homes with different combinations of these technologies, my colleague Will Rowe uncovered the following patterns: 🚗 EVs: Demand shifting for time of use tariffs * Peak charging occurs between midnight-6am, showing consumers respond to time-of-use tariffs * Winter demand jumps 34% vs summer - critical for network planning during peak periods ♨️ Heat pumps: Flexible but weather-dependent * Two distinct daily peaks (3:30-6:30 and 12:30-15:30) indicate smart tariff optimisation * Summer consumption indicates ~75 litres hot water usage per household daily * Significant load-shifting capability suggests potential for demand response ☀️ Solar + batteries: Grid relief with seasonal patterns * Homes consistently show lower daily grid consumption across three seasons * Summer sees reduced overnight charging as solar-battery synergy maximises self-consumption * Clear evidence of energy arbitrage behaviour 🌆 The bigger picture:  Consumer behaviour demonstrates strong price responsiveness, but all technologies show pronounced seasonal variation. Winter represents the critical design case for network capacity planning. 🗞️ What this means:  As LCT adoption accelerates, understanding these real consumption patterns becomes essential for network reinforcement, generation planning, and designing future flexibility markets. Read the full analysis: https://lnkd.in/eDGhnjUm Want access to real-world energy data? The Living Lab's 5,000+ households are helping derisk clean energy innovation via sharing data and taking part in trials of new energy technologies. Contact our team via https://lnkd.in/ehQUnw2Y to discuss how we can help you. #EnergyTransition #HeatPumps #ElectricVehicles #SolarPower #NetZero #EnergyData #Decarbonisation

I have spent nearly twenty years building energy system models. Continental-scale at granular spatial scales. Hourly (or finer) temporal resolution. Co-optimising generation, storage, transmission, distributed energy resources (DERs), and demand simultaneously. Thousands of scenarios. I have published in Nature Climate Change, Science and PNAS. My work has over 4,300 academic citations. Here is what I have learned: the tools most organisations still use to plan energy systems are not fit for the decisions ahead. Most capacity expansion models optimise generation only. They bolt on storage as an afterthought. They treat the transmission network as a copper plate or a simplified transport model. They run on annual energy balances, missing the hourly dynamics that determine whether the system actually works. They assume stable, predictable fuel prices. The last four weeks have demonstrated why every one of those assumptions is dangerous. When gas was £30/MWh, a model that ignored fuel price volatility produced a plausible answer. At £67/MWh and rising, with Ras Laffan physically destroyed, with the BoE pricing rate hikes instead of cuts, with the Ofgem cap headed for £2,000+, the same model produces an answer that could lead to billions in misallocated capital. What we actually need: models that co-optimise across the whole system (generation, storage, transmission, DERs, demand) at nodal or zonal resolution with sub-hourly dispatch, weather-synchronised across wind, solar, and demand, with stochastic fuel prices that reflect the world we actually live in. Where you build matters as much as what you build. A wind farm in northern Scotland connected to a constrained transmission corridor produces curtailed energy and consumer costs. The same wind farm sited where the grid has capacity produces revenue and system value. The UK is making decisions right now about grid investment, generation siting, storage deployment, and demand connections that will lock in infrastructure for decades. The grid queue reform, the Clean Power 2030 target, the SSEP, the data centre surge, the Hormuz shock. These are not separate problems. They are one system. The planning tools need to catch up with the reality. #EnergyModelling #EnergyTransition #UKEnergy #PowerSystems #CleanEnergy #RenewableEnergy #GridReform #EnergyPolicy #NetZero #EnergyStorage #CapacityExpansion #SystemPlanning

Grid stability and security are becoming data + control problems. Utilities and large energy operators are already using Artificial Intelligence (AI) to move from reactive alarms to predictive, resilient, and cyber-aware operations—especially as renewables increase volatility. Here’s where Machine Learning (ML) and Deep Learning (DL) deliver real impact: ✅ Anomaly Detection: clustering + autoencoders to flag abnormal grid states and potential cyber events ✅ Fault Detection & Classification: Decision Trees, Random Forests, Support Vector Machine (SVM) models using voltage/current/frequency features ✅ Predictive Maintenance: Remaining Useful Life (RUL) forecasting to reduce unplanned outages (breakers, transformers, lines) ✅ Voltage Stability: Recurrent Neural Network (RNN) + Long Short-Term Memory (LSTM) models to anticipate instability and corrective actions ✅ Cybersecurity: Intrusion Detection System (IDS) + Anomaly Detection System (ADS) using supervised and unsupervised Machine Learning (ML) ✅ Optimal Power Flow (OPF): faster optimization with Machine Learning (ML) surrogates + Linear Programming (LP), Quadratic Programming (QP), Interior Point Method (IPM) constraint handling ✅ Forecasting: Autoregressive Integrated Moving Average (ARIMA) + Seasonal Autoregressive Integrated Moving Average (SARIMA) for load and generation inputs ✅ Uncertainty: Monte Carlo simulation + stochastic programming for renewables and market variability ✅ Autonomous control (next wave): Reinforcement Learning (RL) + Multi-Agent Reinforcement Learning (MARL), plus Federated Learning for privacy-preserving training What’s your biggest grid pain right now: false alarms, asset failures, voltage events, congestion, or cybersecurity? #ArtificialIntelligence #MachineLearning #DeepLearning #PowerSystems #GridReliability #Cybersecurity #PredictiveMaintenance #EnergyTransition

#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

What if grid planning became asset‑specific by design? We already run a full suite of studies and already know where the system is relatively strong and where it could be fragile. But the way we translate that information into siting or M&A decisions is still inefficient. There are several tools that give us visibility into ATC, congestion, basis, queues, and policy signals. But each operates in its own silo and layers in its own assumptions (ex: generator retirements, transmission buildouts, demand growth, etc.). Instead of generic grid capacity maps, what if we built asset-specific intelligence layers? ▪️ Data Centers: nodes that can hold large loads under contingency scenarios, low congestion risk, stable basis, realistic water/gas access, and grid-hardening against extreme weather. ▪️ Hydrogen/Gas: nodes with injection headroom, pipeline proximity, potential thermal retirements (capacity transfers), supportive industrial policy, and lower extreme weather event exposure. ▪️ Solar: buses were grid strength, irradiance, land use, curtailment, all pencil in. ▪️ Wind: corridors where wind resource, transmission strength, basis, and curtailment risk line up with a credible path to new transmission permits. ▪️ SMRs: sites near retiring coal/nuclear plants with existing switchyards, water availability, seismic stability, strong local load pockets, and community & state policy alignment. This isn't about curating generic data but rather it's about layering complex analysis into asset-specific grid shortlists. A holistic map that reveals where certain technologies have the highest probability of success and the lowest interconnection friction. The grid has never been smarter. Our siting decisions should leverage that intelligence. #EnergyTransition #GridModernization #PowerSystems #TransmissionPlanning #RenewableIntegration #DataCenters #Holistic #Planning

What insights can we extract from large scale AMI data to inform building operation, HVAC system type, occupant behavior, and strategies to improve energy efficiency? In this recent study collaborating with Portland General Electric, we presents a comprehensive data-mining framework for analyzing AMI data at multiple temporal and spatial scales, extracting key statistics such as start hour, duration, and peak hour of load periods across daily, weekly, and annual evaluation windows. The framework employs a list of techniques including load-level detection, home vacancy detection, and weather-sensitivity analysis and statistical methods to provide detailed insights into building energy dynamics. Key findings highlight the substantial impact of the COVID-19 pandemic on residential energy use, uncover patterns like intraday load variations, weekly consumption trends, and annual weather sensitivity. The insights gained can potentially inform better energy management strategies, support grid operations and planning, guide policy-making for energy efficiency improvements, as well as improve input and assumptions in urban scale building energy modeling. Details are presented at the open access article in the Energy and AI journal. https://lnkd.in/grak4rVG

𝗟𝗲𝗴𝗲𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀® 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 — 𝗞𝗲𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝟭. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗘𝗻𝗲𝗿𝗴𝘆 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 → Monitors power usage effectiveness (PUE) in real-time → Tracks live energy consumption and trends across facilities → Provides real-time monitoring of power and natural gas loads 𝟮. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 & 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 → Forecasts load demand based on historical patterns → Analyzes energy use profiles to optimize operations → Identifies anomalies or inefficiencies over time 𝟯. 𝗖𝗮𝗿𝗯𝗼𝗻 & 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 → Seamlessly tracks carbon intensity of energy use → Supports real-time and periodic reporting for ESG and compliance needs → Prepares clients for upcoming carbon reporting regulations 𝟰. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 → Identifies energy waste and underperformance in equipment or systems → Helps facilities dynamically manage energy use during curtailment or demand response events. → Supports advanced Demand Side Management (DSM). 𝟱. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗪𝗵𝗼𝗹𝗲𝘀𝗮𝗹𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝘀 → Supports participation in wholesale energy markets → Aligns real-time consumption with market signals → Helps clients monetize flexibility and respond to price signals 𝟲. 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 & 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 → Quantifies cost impacts of inefficiencies or curtailments → Tracks and manages energy procurement strategies alongside real-time operations → Ties procurement, risk management, and operational data together 𝟳. 𝗠𝘂𝗹𝘁𝗶-𝗙𝗮𝗰𝗶𝗹𝗶𝘁𝘆 / 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 → Provides a centralized view across multiple sites or regions → Allows multi-site operators to benchmark and compare performance → Supports aggregated reporting for enterprise-wide energy strategy 𝟴. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 → Supports long-term infrastructure decisions based on actual load data → Helps model capacity expansion and self-generation scenarios → Integrates with natural gas management (daily nominations, balancing, scheduling) 𝟵. 𝗚𝗿𝗶𝗱 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 & 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 →Models how facilities behave during grid events or emergencies →Supports clients in becoming “net assets” to the grid →Simulates isolation, reconnection, and non-linear load effects 𝟭𝟬. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗳𝗼𝗿 𝗦𝗲𝗹𝗳-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 → Informs clients when onsite generation becomes financially and operationally optimal → Tracks ROI on generation investments over time → Bridges analytics directly into Legend Energy Advisor’s self-generation advisory services * * * * * * * * * * 𝗗𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝘂𝘀𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗲𝗻𝗲𝗿𝗴𝘆, 𝘂𝘀𝗲 𝗲𝗻𝗲𝗿𝗴𝘆 𝗯𝗲𝘁𝘁𝗲𝗿® 👉 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: 📩 https://lnkd.in/dGpq2-dC #EnergyAnalytics #RealTimeData #WholesaleEnergyMarkets #EnergyOptimization #EnergyNinjaChronicles

We recently helped a major utility company slash weeks of analysis time down to mere hours. This was a utility in a region with rapid load and generation growth. Things like new industrial load, data centers, and a flood of renewable projects. They had transmission capacity, but not in many of the areas where much of this extra load was planned. They needed a way to easily show "hosting capacity" on their transmission network. Doing this sort of analysis manually thought was painful and resource intensive. A study could take weeks to complete from start to finish. They needed a few things: 1) A standardized method for large-scale hosting capacity analysis at the transmission level. 2) A computationally intensive tool capable of running large-scale power flow studies efficiently. 3) The ability to provide fast, accurate, and accessible data for planning and decision-making. So we partnered with them to co-design a tool to solve this problem. We had already tackled similar challenges before, so we knew where to start, but they were still blown away by the outcome. Studies that could have taken weeks are done in hours, and running multiple scenarios is as easy as just clicking a few buttons. This is the kind of work I get excited about. Combining deep power systems knowledge, thoughtful UX design, software engineering, and devops... all with a focus on human-centered tools. When you build the right tool, you can unlock so much potential. Proud of the Simple Thread team for this one. ⚡️ ️ Read the full case study here: https://lnkd.in/esxXmFyN ⚡️ ️ Read the T&D World Article here: https://lnkd.in/eCSkEcpV #CustomSoftware #EnergyTech #GridModernization #UtilityInnovation #PowerSystems #CaseStudy #SimpleThread

The level of detail here… wow. How will the U.S. power grid keep up with the explosive growth in data centers and AI? The answer may have just gotten a lot less grey, and a lot more interactive. The Speed to Power Data Viewer, developed by NREL in partnership with the U.S. Department of Energy’s Grid Deployment Office, is a new, free tool that lets you explore U.S. data center infrastructure, layer by layer. What stands out to me? - Decades of NREL’s grid modeling & spatial analysis expertise, all in one place. - Visualization of power plants, data center capacity by county, transmission lines, natural gas pipelines, fiber-optic cables, and more. - The ability to identify siting constraints, co-location opportunities, energy system trade-offs, or simply gain a deeper understanding of our power grid. While it’s not a substitute for full site assessments, I see it as a good starting point for early discussions as we navigate the complex landscape of data centers, energy demand, and infrastructure. What do you think? Are tools like this helpful in your work?

That's the most impressive open data project I've seen in a long time. A “Google Maps for the global power system” just quietly went live - and it’s mind-blowing. 👉 https://lnkd.in/dufAnyen OpenGridWorks lets you zoom into any region on Earth and explore: ⚡ 120,000+ power plants 🔌 ~2.7 million transmission lines 🏭 800,000+ substations 📡 even data centers and planned infrastructure What makes it powerful: • You can visually compare energy systems globally (hydro, solar, wind, thermal, nuclear) • It reveals where the energy transition is actually happening - and where it’s not • It highlights bottlenecks, grid saturation, and investment opportunities • It connects electricity infrastructure with digital infrastructure (data centers!) In short: this is not just a map - it’s a decision tool for energy, policy, and investment. But there’s a second layer to this story. Making this level of infrastructure data easily accessible raises real questions: • Critical infrastructure (plants, substations, grid nodes) becomes trivially explorable • Potential vulnerabilities and choke points are visible at scale • The barrier to “understanding a national grid” just dropped dramatically Yes - this data was already public or inferable. I worked with these data sources in during my PhD myself. But aggregating and visualizing it like this changes the game. As one commenter put it, it can feel like handing over a “targeting map” - even if the underlying data isn’t new. So we’re seeing a classic trade-off: ➡️ Radical transparency accelerates innovation, planning, and the energy transition ➡️ But it also lowers the threshold for misuse I am curious how others see this: Is this the future of open energy systems - or are we underestimating the security implications? --- image: screenshot of the OpenGridWorks tools