Jia He

I’ll be presenting my latest research at #ACPSiting26 in Aurora, CO! My poster in the Energy Modeling track (#ACPPEAK26) explores how integrating field data with physics-based models can drastically cut BESS energy prediction errors and improve simulated voltage alignment. 🔋. If you’re attending, I’d love to connect and discuss all things renewable performance and computational modeling: https://lnkd.in/g7U54FCX #TeamRWE #CleanEnergy #BESS #EnergyModeling #Renewables #Physics+Data American Clean Power Association (ACP)

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In wholesale power markets like ERCOT, MISO, and PJM Interconnection, a sudden drop in wind generation can trigger massive nodal price spikes and severe balancing penalties within minutes. The challenge? Standard public weather forecasts update too slowly and rely on 10-meter surface wind speeds—completely missing the dynamics at the actual 100-meter turbine hub height. In our latest technical walkthrough, we demonstrate how to build an automated Python pipeline using the Meteomatics Weather API to: 🔹 Batch query multi-region hub-height data across key wind corridors simultaneously into a pandas DataFrame. 🔹 Calculate rolling 1-hour velocity changes at 15-minute intervals. 🔹 Automatically flag critical downward wind ramp events (>4 m/s drop) before spot prices surge. 🔹 Generate real-time operational alerts for power traders, dispatch desks, and portfolio managers. Having precise hub-height intelligence provides actionable lead time when every minute counts. 🎬 Watch the full step-by-step tutorial and see the dashboard in action: GitHub repo in the comments.

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I’ll be chairing a session on stochastic power systems planning at the INFORMS Optimization Society Conference tomorrow at 8am; there will be donuts and coffee, so stop by and learn about the work we’re doing while enjoying breakfast on us! FA02: Power Systems Planning: Optimization Modeling and Software Advancements.  Speakers: Rachael Alfant: Selecting Key Days for Electricity Grid Capacity Expansion Planning Considering Resilience to Compound Hazards Zachary Kilwein: Multifidelity Progressive Hedging and Bundling Strategies for Diverse Stochastic Programming Problems Matthew Viens: Alternative Solutions for Energy Applications without Recourse Assumptions Georgia Stinchfield: Enhancing Stochastic Power Planning through Structured Nonlinear Global Optimization

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Xcel Energy filed a “first-of-its-kind proposal” with Minnesota regulators on Oct. 3 to develop a network of distributed battery resources across its grid and integrate the capacity into its system planning, beginning with an initial procurement of up to 200 MW. Under its Capacity*Connect proposal, Xcel will work with Sparkfund to deploy batteries ranging from 1-3 MW at “strategic locations” on its grid by 2028, paying local businesses, commercial or industrial sites, or nonprofit organizations to host the batteries. Xcel requested the Minnesota Public Utilities Commission approve the program with a budget range from $152 million, based on a minimum procurement of 50 MW, to $430 million if it deploys all 200 MW.

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At the December 18 Open Meeting of the Federal Energy Regulatory Commission (“FERC”), FERC took action to provide guidance on how large loads and generators may co-locate in the PJM Interconnection, L.L.C. (“PJM”). Read more in the blog post by Kenneth Irvin and Charles Beauregard: "FERC Charts a New Path for Large Load Co-Location in PJM."

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Chief Engineer and NERC Fellow Mark Lauby joined a panel of utility, technology, and academic leaders at Purdue University’s Grid of Tomorrow Consortium to discuss the reliability repercussions of increased energy demand on the grid from large data center load integration. Lauby explained NERC’s projections of resource adequacy risks throughout the North American bulk power system over the next 10 years and discussed how data center integration accounts for most of the projected resource demand increase. His panel, titled “Reliability and System Adequacy Under Rapid Load Growth,” also discussed data center modeling, practical steps to reliably integrating these large loads, and potential applications of artificial intelligence to partner with current reliability practices aimed at bolstering a more resilient bulk power system.

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Much of grid risks failure in years ahead as fossil fuels phase out, regulator warns "I can say with some confidence, hundreds of American lives have been saved, because of [Trump] leaning in and stopping the killing of coal and revitalizing coal," Wright said. "Coal, over the last few days, where we had peak demand and electricity, has delivered 20 times more electricity than solar and batteries." https://lnkd.in/gaMYxKwf

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🔌 A market signal that keeps coming up in U.S. energy conversations: More projects aren’t stalling because of capital or technology, they’re stalling because of execution risk. Interconnection queues, grid congestion, and power availability are no longer just "technical hurdles." They are now the primary drivers of ⤵️ Project timelines - 5+ year waits are the new baseline. Site selection - Location is now dictated by capacity, not just land cost. Go/no-go decisions: If the grid won't take it, the project doesn't exist. What’s changing is when this gets addressed. The teams moving fastest are pulling grid strategy into the pre-development phase. They aren't treating it as a downstream problem; they are treating it as a foundational risk. This shift is quietly redefining what “bankable” looks like across energy, storage, and data center development. It is also redefining the talent landscape, we are seeing a massive surge in demand for interconnection experts and grid strategists who can navigate this complexity on day one. Are grid and interconnection constraints now shaping your project decisions earlier than financing or technology choices? #EnergyTransition #EnergyStorage #Infrastructure #GridModernization

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Utilities are using advanced anaerobic digestion to drive cost savings, new revenue, and smoother operations. This BC-led review distills lessons from six full-scale technologies, so you can choose the right path for performance, compliance, and resilience. 💬 Planning upgrades or a modernization roadmap? DM me — we’re happy to help. 👉 https://ow.ly/bAEl30sSCiz #Utilities #Biosolids #AnaerobicDigestion #WaterTreatment #BrownandCaldwell #ResourceRecovery

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As power systems increasingly rely on natural gas generation to support decarbonization and renewable integration, the interdependence between electric grids and gas infrastructure has intensified. These Integrated Power and Gas Systems (IPGS) improve efficiency and sustainability but also introduce new operational risks. This technology, developed by North Dakota State University researchers Nof Yasir and Di Wu, introduces a novel influence graph-based vulnerability assessment framework for Integrated Power and Gas Systems (IPGS), designed to identify critical components that initiate cascading failures. The method first models cascading failures using fault chain theory, where sequential component outages are characterized by transition probabilities derived from overload conditions. Unlike conventional approaches that treat electric and gas networks separately, this framework develops a unified fault chain model for the entire IPGS. Both electric power flow (AC power flow) and dynamic gas flow models are integrated to simulate realistic interdependencies between subsystems. From the generated fault chains, an influence graph is constructed. In this directed and weighted graph: *Nodes represent transmission lines or gas pipelines. *Edges represent propagation paths of cascading failures. *Edge weights are defined using two complementary metrics: -Energy Not Supplied (ENS) to quantify outage severity. -Repetitive failure frequency to reflect statistical vulnerability. To identify the most critical branches, the framework applies eigenvector centrality, which evaluates not only how often a component fails, but also how influential its neighboring components are. This allows detection of components that may not fail frequently, yet trigger severe system-wide impacts when they do. Validation on a 39-bus, 29-node IPGS model demonstrates that the method accurately isolates high-impact branches whose simultaneous failure leads to blackout conditions—outperforming traditional centrality-based approaches. This technology has a U.S. Patent Pending. Interested in learning more about licensing or collaboration opportunities? Contact the NDSU Research Foundation. https://lnkd.in/gPhzse7z

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