Large Load Integration in Grid Infrastructure

As grid operators and planners deal with a wave of new large loads on a resource-constrained grid, we need fresh approaches beyond just expecting reduced electricity use under stress (e.g. via recent PJM flexible load forecast or via Texas SB 6). While strategic curtailment has become a popular talking point for connecting large loads more quickly and at lower cost, this overlooks a more flexible, grid-supportive strategy for large load operators. Especially for loads that cannot tolerate any load curtailment risk (like certain #datacenters), co-locating #battery #energy storage systems (BESS) in front of the load merits serious consideration. This shifts the paradigm from “reduce load at utility’s command” to “self-manage flexibility.” It’s BYOB – Bring Your Own Battery and put it in front of the load. Studies have shown that if a large load agrees to occasional grid-triggered curtailment, this unlocks more interconnection capacity within our current grid infrastructure. But a BYOB approach can unlock value without the compromise of curtailment, essentially allowing a load to meet grid flexibility obligations while staying online. Why do this? For data centers (DC’s), it’s about speed to market and enhanced reliability. The avoidance of network upgrade delays and costs, along with the value of reliability, in many cases will justify the BESS expense. The BYOB approach decouples flexibility from curtailment risk with #energystorage. Other benefits of BYOB include: -Increasing the feasible number of interconnection locations. -Controlling coincident peak costs, demand charges, and real-time price spikes. -Turning new large loads into #grid assets by improving load shape and adding the ability to provide ancillary services. No solution is perfect. Some of the challenges with the BYOB approach include: -The load developer bears the additional capital and operational cost of the BESS. -Added complexity: Integrating a BESS with the grid on one side and a microgrid on the other is more complex than simply operating a FTM or BTM BESS. -Increased need for load coordination with grid operators to maintain grid reliability. The last point – large loads needing to coordinate with grid operators - is coming regardless. A recent NERC white paper shows how fast-growing, high intensity loads (like #AI, crypto, etc.) bring new #electricty reliability risks when there is no coordination. The changing load of a real DC shown in the figure below is a good example. With more DC loads coming online, operators would be severely challenged by multiple >400 MW loads ramping up or down with no advanced notice. BYOB’s can manage this issue while also dealing with the high frequency load variations seen in the second figure. References in comments. 

Last year Sagnik Basumallik and I wrote a paper on the challenges large loads pose to grid reliability and some potential solutions to mitigate these challenges. Our paper - “Reliability Challenges and Solutions for Large Load Integration in Bulk Power Systems,” was accepted for IEEE T&D 2026! We started this effort after working on the first NERC LLTF white paper and this paper built on our experience there. In this paper we expanded on that work with event reviews and identified possible mitigation options for the risks these loads pose to the bulk power system. In the paper we analyzed the impact to the grid from several events where large loads tripped in response to normal system faults, and oscillations originating from large loads across the AEP, Dominion, EirGrid, and ERCOT systems. Then we identified the following causes of events that have been seen and developed a taxonomy of root causes per their source - hardware or software. These causes included: ⚡️Fault-Induced Customer Initiated Load Reduction/Tripping ⚡️Oscillations due to Instability in Electronic Controllers ⚡️Oscillations due to Outdated Firmware Settings ⚡️Transients due to Regular, Cyclical Fluctuations in Data Center Digital Processes ⚡️Coordinated Customer Initiated Load Reduction After the event reviews we looked at what possible mitigations could address the reliability challenges that we identified. Facility side mitigations included: UPS and power supply controller changes to manage oscillations along with hardware updates for voltage ride-through support, coordination with transmission protection schemes, and grid forming loads. Grid side mitigations included E-STATCOMs, better dynamic modeling, improved monitoring capabilities, and market services. Future work is still needed however on large load dynamic modeling, improved monitoring such as point on wave monitoring, and large load characterization. You can read the preprint version of the paper here: https://lnkd.in/gKsJTRz6

🤔 How BYD Solve the Grid Nightmare of Megawatt Charging? Let's look closer to BYD’s new All-Liquid-Cooled Megawatt Charger, isn’t just about speed. It’s a masterclass in redefining charging infrastructure economics. 🔌🔋 ⚡ The "Impossible Math" Solved Traditional megawatt charging requires a 1,600kVA transformer ($$$$), brutal grid loads, and $$$ civil works. BYD’s system?   - Transformer Size Slashed: 315kVA (80% smaller!) → cuts grid strain and saves $40k/year in post-2030 utility fees.   - Cost Halved: Total station build drops from ~$70k to $15k (transformer + construction).   - Secret Sauce: Integrated 225kWh battery storage buffers grid demand, enabling 1MW charging with a fraction of the power draw.  🔋 Storage Meets Speed: The Killer Combo   - 5-Minute 400km Charge: Matches gas station speed, no swap stations needed.   - Grid-Friendly: Storage absorbs peak loads, avoiding costly grid upgrades.   - Profit Play: Off-peak charging + peak discharge turns stations into virtual power plants (VPPs).  🌍 Why This Will Go Viral  1. Scalability: Tiny footprint + low grid dependency = rapid nationwide rollout.   2. Policy Proof: Dodges post-2030 “basic electricity fee” traps (saves ~$4k/month per station).   3. Storage Gold Rush: Each charger needs a battery – 3M+ EVs in China alone could birth a $30B+ storage market (bigger than commercial & industrial ESS!).  📊 BYD vs. Traditional Chargers   Metric        BYD’s System | Legacy Megawatt Charger Transformer Size 315kVA | 1,600kVA Build Cost $15k | $50k+ Grid Impact   Low (storage-buffered) | High (direct grid pull) ROI Timeline    <3 years | 5–7 years 🔥 The Bigger Picture   “This isn’t just charging – it’s energy infrastructure democratization,” said Lian Yubo, BYD’s Engineering VP. With 4,000+ stations planned, BYD is turning every charger into a grid asset, not a liability.  💡 Question:   Could this model make standalone ESS projects obsolete?  #BYD #EnergyStorage #EVCharging #SmartGrid #Innovation  

For most of the last century, generators stabilised the grid as a by-product of producing energy. Today, we are building assets that stabilise the grid without producing energy at all. That shift identifies the binding constraint. Electricity system transition is no longer constrained by renewable resource availability. It is constrained by deliverability and operability. In inverter-dominated systems under rapid load growth, the binding constraints are: - transmission and major substation capacity - system strength, fault levels, frequency and voltage control - connection and commissioning throughput - secure operation under worst-day conditions - execution pace across networks and system services Generation capacity remains necessary. On its own, it no longer delivers firm supply or supports large new loads. Historically, synchronous generators supplied energy and stability together. Inertia, fault current, voltage support, and controllability were implicit. As synchronous plant retires, these services must be provided explicitly. Stability shifts from physics-led to control-led. System behaviour becomes more sensitive to modelling accuracy, protection coordination, control settings, and real-time visibility. Curtailment is not excess energy. It is a deliverability or security constraint. When transmission and substations lag generation, congestion and curtailment rise. Independent analysis shows that delay increases prices and emissions by extending reliance on higher-cost thermal generation. Distribution networks are no longer passive. They now host distributed generation, storage, EV charging, and large loads at the edge of transmission. Voltage control, protection coordination, hosting capacity, and connection throughput now constrain both decarbonisation and industrial growth. Firming is a hard requirement. Batteries provide fast frequency response and contingency arrest. They do not provide multi-day energy and do not replace networks or system strength in weak grids. Demand response reduces peaks. It cannot be relied upon for system-wide security under stress. Execution speed is critical. Slow delivery increases congestion duration, curtailment exposure, reserve requirements, and reliance on ageing plant. These effects flow directly into costs, emissions, and reliability. This is why electricity bills can rise even when average wholesale prices fall. Costs are driven by peak demand, contingencies, and security, not average energy. Large digital and industrial loads are transmission-scale, continuous, and failure-intolerant. They increase contingency size and correlation risk. At that scale, loads do not connect to the grid, they shape it. Supporting growth requires time-to-power, transmission and substation capacity in load corridors, explicit system strength and fault levels, operable firming under worst-day conditions, scalable connection and commissioning, and early procurement of long lead time HV equipment. #energy

Power markets weren’t designed for the hyperscale era – but in a rare example of regulatory speed and innovation, Southwest Power Pool (SPP) just unveiled a new set of tools that could significantly change how large loads are integrated into the power system. Facing urgent demand and long lead times, SPP is introducing a new non-firm transmission service with a rapid 90-day connection study for large flexible loads unable to wait for completion of system upgrades, which will be curtailable under reliability conditions and designed as a bridge to firm service. It’s called CHILL – short for Conditional High Impact Large Load. SPP is also launching a companion study process, HILLGA (High Impact Large Load Generation Assessment), to evaluate paired generation that can help serve new large loads without triggering years-long delays in the generator interconnection queue. I especially love SPP’s first guiding principle for the initiative: "Inspire mindsets and employ innovative, art-of-the-possible thinking" – exactly the mindset we all need as we navigate the intersection of rapid load growth and grid transformation. More here: https://lnkd.in/gzprwnr2

Last month, I broke down Alberta’s new rules for large loads. One clause deserves its own spotlight: Large Loads Must Ride Through Positive-Sequence Phase Angle Jumps of up to 25° in a Single Cycle. This has always been a generator requirement. Never a load requirement. Until now. Why it matters: ➤ A 25° phase jump in a 60 Hz system means the waveform shifts in ~1.16 ms. Enough to confuse relays, desynchronize PLLs in converters, or crash UPS systems. ➤ During the Jan 23, 2023 Odessa fault, several solar and wind parks tripped despite voltages being within LVRT limits, caused by inverter synchronism failure under phase-angle jumps. ➤ Alberta recognised that data-centre rectifiers and UPS inverters use the same PLL chips. If generators couldn’t ride through the angle step, neither will a 300 MW load once >60% inverter-fed. ➤ For decades, loads were passengers: when the grid shook, they could vanish. ➤ Now Alberta says: if you want to connect at hundreds of MW, you stay on, you hold steady, you behave like generation. What this means in practice: • A hyperscale data centre must keep operating through a disturbance strong enough to throw inverters out of lock-step. • By imposing generator-grade ride-through on loads, AESO is collapsing the old divide: stability is everyone’s job. • Paper specs won’t cut it. Centres must supply validated EMT and phasor-domain models, tested against real faults. • Fail to prove compliance? You don’t connect. The bar has shifted from permission to connect → proof you can stabilise. The bigger picture: Alberta is a 12.4 GW system. A 500 MW data centre isn’t background noise, it’s a system shock. • Lose one invisibly, and planners are chasing ghost contingencies never logged. • Spain has already seen what hidden dynamics can do. • Ireland is straining under hyperscale clustering, forcing curtailments and planning freezes. The UK isn’t far behind as AI demand ramps. • ERCOT and PJM are next. The precedent won’t stay in Alberta for long. AESO’s 2025 Roadmap is explicit: instead of curtailing new load or blanketing the grid with synchronous condensers, “we will require the load itself to stay online for the same disturbances we expect generators to survive.” Short term, that’s the cheaper path. Long term, synchronous condensers are still on the table. But for now, hyperscale demand must carry its share of stability. My view: This is more than a technical clause. It’s the clearest sign yet that programmable demand has crossed the line from consumer to grid actor. AESO’s message is blunt: 👉 Hyperscale demand is no longer a disturbance to manage. It’s a stability resource to command. The question isn’t whether other grids will follow. It’s this: 👉 Should hyperscalers everywhere be forced to ride through like generation, or is Alberta setting an impossible bar? #AI #DataCenters #GridStability #PowerSystems #Policy #SystemStrength #TransmissionAndDistribution

𝐒𝐡𝐚𝐫𝐞𝐝 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐢𝐧 𝐃𝐂 𝐃𝐚𝐭𝐚 𝐂𝐞𝐧𝐭𝐫𝐞𝐬 𝐚𝐧𝐝 𝐃𝐂 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 🔋 In industrial DC applications, rapid load swings resulting from robot movements and machine stops can lead to overdimensioning of the infeed connections. Several DC industry projects demonstrated an elegant solution: integrating energy storage systems on a shared DC bus with infeed and load converters. At one of the pilot sites, a highly dynamic DC/DC converter together with high-performance capacitors reduced peak infeed power from 38 kW to 26 kW—a reduction of over 30%. The system operated without communication between devices, using droop curves to coordinate power sharing. In another configuration, peak power dropped from 47 kW to 27 kW, achieving a 43% reduction. Correct placement of energy storage prevents overdimensioning of grid connections while maintaining system stability during dynamic industrial operations.   🔦 AI infrastructure faces remarkably similar challenges. Synchronised GPU workloads create rapid power swings from idle (approximately 30% of rack power) to 100% utilisation during intense matrix computations.  NVIDIA's recent white paper addresses this through a multi-layered energy storage strategy: electrolytic capacitors handle sub-100 ms transients, intermediate storage covers 100 ms to 10-second events, and lithium-ion batteries manage longer-duration requirements.  Mitigating load swings close to the GPU reduces RMS current increases—a square wave with a 50% duty cycle creates a 25% increase in RMS losses—and keeps infrastructure costs manageable.    ⚡ Both industries prove that integrating energy storage into LVDC architecture delivers substantial benefits: reduced peak power demand, improved grid stability, and optimised infrastructure utilisation. However, these advantages come with critical safety responsibilities. The large amounts of energy stored in capacitors and batteries can discharge extremely quickly into faults, creating severe hazards for both equipment and personnel. Appropriate protection devices—including solid-state circuit breakers, blocking diodes, and coordinated fault isolation strategies—are essential to safely harness the benefits of energy storage in DC systems. As AI datacenters and smart factories converge on similar power architectures, it will be interesting to observe the new technology developments in the electric infrastructure world. #lvdc #directcurrent #solidstate #datacenter #ai #abb #protection #energystorage #bess #batteryenergystorage

🔥 #AI #datacenters are being treated like “just another big load.” That’s a dangerous planning assumption. Most of the power they draw isn’t flexible by default - it’s reliability-driven and must stay on to keep compute running. Backup systems aren’t demand response, they are continuity systems. And GPUs don’t pull smooth power - they fluctuate in ways grids were not designed for. But here’s where the story has potential to shift 👇 📌 Batteries and energy storage aren’t just backup anymore - they can make large power users behave like flexible grid assets. With the right controls, storage can charge when the grid is abundant and discharge when it’s stressed, helping balance supply and demand and support frequency and stability, all while keeping compute running. This is backed by recent grid research on dispatch and optimal BESS use. 📌 Growing work on grid-interactive UPS and storage systems shows that data centers can participate in ancillary markets and provide services like fast frequency response and other flexibility if designed and governed with that intent. In #Europe, this is already moving from theory to planning reality ⚡ Reports show that grid congestion and connection constraints are now influencing where data centres are built, with utilities reassessing connection rules and flexibility incentives as grid capacity becomes a decisive factor in investment decisions. So the real shift isn’t debating whether AI loads are “flexible” - it’s about engineering them to be grid-interactive assets, not inflexible liabilities. 👉 If we plan for them as firm loads PLUS intentional, contracted flexibility, we unlock new options for reliability, carbon goals, and grid stability. This isn’t future talk - credible research and emerging deployments are already pointing toward hybrid storage, smarter dispatch, and real grid value. In our work where we help design control centers of the future for utilities and system operators, this needs to be part of the discussion. https://lnkd.in/dXDvf3BE https://lnkd.in/dnMXjKzh ⚡ #GridPlanning #AIInfrastructure #DataCenters #EnergyStorage #BESS #GridFlexibility #EnergyTransition photo: Interactive map of data centre hubs alongside associated power and digital infrastructure // IEA's Energy and AI Observatory

America’s grid faces a stress test: demand is surging, but supply can’t keep up. Data centers, EVs, and electrified heating are pushing U.S. electricity demand up 21.5% this decade. AI alone is creating jaw-dropping energy needs, with Microsoft and Google racing to secure 24/7 clean power for their data centers. Yet new plants and transmission take years, stuck in queues, permitting delays, and regulatory gridlock. So how do we meet demand today without waiting a decade for steel in the ground? A recent paper by Norris, Profeta, Patino-Echeverri, and Cowie-Haskell highlights one answer: load flexibility. Instead of treating demand as fixed, flexible loads (data centers, industrial plants, EV fleets) can temporarily scale back when the grid is stressed. The findings are striking: - With just 0.25% annual curtailment (~1.7 hrs/yr), the U.S. could integrate 76 GW of new load. - At 1% curtailment, that expands to 126 GW. - In PJM (the nation’s largest power market, serving 65 million people across 13 states) 18 GW of new demand could be added without building new plants. Flexibility isn’t a silver bullet, meaning it can’t replace the need to build new clean generation, transmission, and storage. But it buys time, reduces costs, and makes the system more resilient. Software, sensors, and batteries can unlock efficiency at a fraction of the price of new steel in the ground. The lesson is simple: flexibility is capacity. Execution is survival. But we need both efficiency and investment if we want a grid that keeps up with the 21st century. Here's the full paper from Nicholas Institute for Energy, Environment & Sustainability at Duke University: https://lnkd.in/gBh_3Fva