Managing Seasonal Demand Fluctuations

"One of the key ways to make energy systems more reliable is by maximizing flexibility — improving how well the system can adapt in real time to changes in supply and demand. The more flexible the system, the better it can handle sudden demand spikes in the event of extreme weather, such as cold snaps or heat waves, or respond to supply disruptions such as plant outages. Improving flexibility includes upgrading aging infrastructure. Much of the U.S. grid was built decades ago under different demand patterns. Modernizing the grid — by updating substations and transmission equipment, deploying advanced sensors and incorporating advanced transmission technologies (ATTs), for example — can reduce failure rates during extreme heat and cold. These technologies help operators detect problems quicker, reroute power if equipment is damaged and restore service fast. Modernization not only improves reliability but also reduces expensive emergency interventions and lowers long-term maintenance costs. Increasing grid capacity, both through deployment of ATTs and building regional and interregional transmission lines, can reduce the risk of a local weather event turning into a widespread outage. Creating a more interconnected grid allows regions to share power during shortages. Having this greater transmission capacity also help keep prices down by allowing lower-cost electricity to reach areas facing higher demand. Demand-side management options can help ease pressure on the system during extreme weather events. These include encouraging customers and large users to reduce or shift electricity use during peak periods in exchange for lower bills or leveraging distributed energy resources to help prevent shortages. Systems that rely too much on a single fuel are more vulnerable to disruption. Diversification across energy sources and technologies helps reduce the risk of issues related to fuel shortages, infrastructure failures and localized weather impacts. Finally, policy is also critical. It’s vital that incentives are properly aligned with modern needs for flexibility and preparedness. This can help utilities make system investments that really work in extreme weather and minimize costs to consumers in both the short and the long run." Kelly Lefler World Resources Institute https://lnkd.in/e5syqXQp

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. 

Some of us keep talking about DERs and better grid utilization to help solve the power demand problem. Excited to see things are starting to move in that direction. For years, when utilities needed to meet peak demand, the answer was almost automatic: build a gas peaker plant. That assumption is starting to crack. Not because of ideology—but because the math is changing. Take Consolidated Edison’s Brooklyn-Queens Demand Management program. Instead of building a new gas peaker and substation upgrade, they deployed a portfolio of distributed energy resources—efficiency, rooftop solar, and behind-the-meter batteries. It delivered the same reliability outcome at a fraction of the cost. Or look at what’s happening more broadly with virtual power plants—aggregations of home batteries, smart thermostats, EVs, and flexible loads. In places like California and Texas, these systems are now being treated as real capacity resources—able to shave peaks and reduce the need for fossil peakers. What’s emerging is not a one-off workaround. It’s a pattern. Distributed energy resources are increasingly taking over the role that gas peakers used to play: meeting short-duration spikes in demand, cheaply and quickly. And now there’s a new twist: Large loads—especially data centers—are beginning to join that stack. Through demand flexibility and workload shifting, they can act less like passive demand and more like dispatchable capacity. If this continues, the implications are significant: • Less need to build new gas peakers • Lower system costs (because DERs are modular and faster to deploy) • A grid that’s more flexible—and more participatory To be clear: DERs aren’t replacing all firm capacity. We still need solutions for multi-day reliability and extreme events. But they don’t have to. If DERs can cover even 10–20% of peak demand by 2030—as several analyses suggest—that’s enough to avoid a large share of new peaker builds. The “default” is shifting from one big plant solving the problem to a portfolio of smaller, smarter resources working together. That’s not just a technology story. It’s a different way of thinking about the grid. Keep watching this trend ….

Battery Energy Storage Systems (BESS) help Malaysian homes and businesses cut bills after the tariff hike by shifting usage from expensive peak hours (e.g., 2–10 pm weekdays) to off-peak periods, shaving maximum demand/capacity charges, and maximizing self-consumption of rooftop solar. For residences (best for higher-usage or solar homes), 5–15 kWh LFP batteries store midday solar or cheap night power for evening use and provide seamless backup. For commercial users, 30–500 kWh (scalable to MWh) batteries flatten daily peaks, arbitrage ToU price gaps, and smooth HVAC/chiller loads—often delivering 6–7%+ total energy-cost reduction, faster if paired with PV. Industrial sites deploy MW/MWh-scale systems to trim costly MD spikes (saving tens of thousands monthly), ride through disturbances, improve power quality, and comply with 2025 rules that require storage for >72 kWp self-consumption PV, while enabling deeper PV usage. Economics are boosted by GITA (100% capex tax allowance for BESS) and green financing, making solar+storage paybacks commonly ~3–6 years in C&I (longer for typical homes, shorter for large users). Key actions: right-size battery to your peak window, enroll in ToU where suitable, integrate with PV for >80% self-use, prioritize critical-load backup, and use smart controls to target the exact 30-minute peaks that set charges. Overall, BESS turns tariff volatility into savings and resilience across residential, commercial, and industrial sectors.

Are time-of-use (TOU) rates good or bad for the electric grid? While TOU rates aim to reduce system-wide peaks, they can increase grid stress and costs under many current designs—especially with the rapid growth of #electricvehicles and #electrification. Here’s why: Residential TOU peak periods typically end around 7-9 pm (survey of 30 large utilities). Many EV owners start charging immediately after off-peak rates begin, but these periods are based on system-wide loads, not local distribution peaks. Now, picture a neighborhood with 10 homes on a shared transformer, where 5+ homes have EVs. With each EV drawing around 7 kW, the load can more than double each household's load. The result? Transformer failures are the first sign of strain. As electrification grows, the stress will extend to feeders, substations, and beyond. So, should we abandon TOU rates? Regulators favor them because they shift load off-peak, are low cost, and are backed by historical results. But the more compliance, the more severe the local #grid stress. Another challenge: shifting peak periods. As #renewables like #solar and #wind expand and grid-scale #batteries become common, peak times are moving. California’s "duck curve" shows demand now shifting to different parts of the day. We now need to encourage EV charging mid-day in solar-rich areas! Constantly re-educating consumers on changing peak/off-peak times is impractical. What’s the fix? OPTION 1: Move off-peak to midnight. Some utilities now start off-peak for EVs at midnight when household demand is low, reducing but not solving the surge problem. OPTION 2: Stagger TOU start times. Spreading start times across households could ease local strain but is complex and unpopular with regulators. OPTION 3: Adopt dynamic solutions. The best option for now is managed EV charging (until we get #V2G). Customers set a "ready by" time (e.g., morning), and utilities optimize charging based on battery status, grid conditions, and costs. This keeps costs low for both consumers and the grid and the consumer gets a full charge without any intervention. 3A: Whole house vs. EV specific rates? Different appliances have different characteristics, time-based value, and needs. I think it makes sense to treat EV pricing separately that the other appliances in the house, just like we do for solar rooftop. While dynamic solutions like managed charging are the future, a mix of pricing options is essential. No single approach will work for every customer or address the grid’s evolving needs. Your thoughts? P.S. I've included a link to a longer PLMA (@PLMAflm) discussion about electricity pricing that includes ideas from myself and Ahmad Faruqui. #energy #utilities #gridmanagement #TOU #EVcharging #tesla #rivian #electricvehicles

When the duck curve turns into an all-day plateau, industrial consumers need either fuel-switching options or long-duration storage to stay competitive. Today’s day-ahead prices in the DE-LU bidding zone look very different from the usual pattern. The typical midday dip is missing; instead, prices stay high from early morning until late evening. With an average of 220.5 €/MWh, 25 November now ranks as the third most expensive trading day of 2025, surpassed only by two extreme days in January. And the situation is not easing: the forecast for 26 November remains only marginally lower. 𝐖𝐡𝐲 𝐩𝐫𝐢𝐜𝐞𝐬 𝐫𝐞𝐦𝐚𝐢𝐧 𝐞𝐥𝐞𝐯𝐚𝐭𝐞𝐝 𝐟𝐨𝐫 𝐯𝐢𝐫𝐭𝐮𝐚𝐥𝐥𝐲 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐝𝐚𝐲 A set of reinforcing factors compresses the price curve upward: ➤ Weak wind and solar generation limit supply across all hours. ➤ Low temperatures keep the residual load above 65 GW, even during midday. ➤ Structural scarcity in dispatchable capacity, as recently highlighted by Andri Busch. On top of that: once short-duration storage is exhausted, demand becomes largely inflexible, reducing the system’s ability to respond to price spikes. ➤ Additional contributors may be at play, potentially including local market power effects. The outcome is a remarkable spread between electricity and natural gas: for several consecutive hours, power prices exceed ten times the gas spot price (~32 €/MWh). 𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐞𝐧𝐞𝐫𝐠𝐲 𝐮𝐬𝐞𝐫𝐬 Industries with substantial process-heat requirements face a simple fact: without a non-electric heat source or a high-temperature, long-duration storage asset, they are forced into producing heat at highly unfavorable electricity prices. Both strategies, fuel-switching or long-duration storage, are becoming critical elements of cost resilience. 𝐖𝐡𝐲 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐝𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 In a renewables-driven system, short-duration storage smooths volatility but does not compensate for system-wide generation deficits spanning an entire working day. When price plateaus persist for eight hours or more, only technologies capable of shifting energy across multi-hour to multi-day intervals can stabilise operations and costs. These include: ➤ Long-duration thermal storage ➤ Hydrogen-ready or hybrid plants ➤ Other forms of long-duration energy storage (LDES) Days like today make one point unambiguously clear: flexibility is not solely about rapid response. It is about endurance. A renewables-based system requires assets that can maintain supply through extended scarcity periods without defaulting back to additional fossil capacity. Brenmiller Energy ENERGYNEST KRAFTBLOCK Kyoto Group AS Rondo Energy et al.

Reflecting on recent reports of peak demand shortages that the #IndianGrid is tackling, I was trying to make sense of energy storage arbitrage opportunities—particularly for 2-hour and 4-hour systems. India’s electricity demand surged to ~240 GW in April 2025, a 10% YoY growth driven by summer heat, industrial recovery, and rural electrification. Per capita consumption hit 1,500 kWh, echoing urban and economic momentum. Yet, evening peak demand (6–9 PM) consistently outstripped supply, with ~5–10 GW deficits in northern states. On the supply side, India’s 450 GW capacity (175 GW solar, 50 GW wind) saw renewables contribute 42% of generation. But midday solar surplus (10 AM–3 PM) led to grid congestion, and coal—still 60% of non-solar generation—faced 15–20% outages. This imbalance makes a compelling case for flexible storage. IEX price signals add weight. A short IEX data scan and some back-of-the-envelope math reveal this: day-ahead (G-DAM) prices during solar hours hover around ₹4.00–₹4.20/unit, while RTM/G-TAM evening peaks spike to ₹7.50–₹8.50/unit. That ₹3.50–₹4.50/unit gap opens a solid arbitrage window. A 1 MW/4 MWh lithium-ion BESS charging at ~₹4.00/unit and discharging at ~₹8.00/unit (90% efficiency) nets ~₹12,250/day post O&M. On strategy: 2-hour systems (₹25 lakh/MWh, 95% efficiency) suit sharp evening peaks (6–8 PM). A 1 MW/2 MWh setup earns ~₹6,100/day, ideal for C&I demand charge management. 4-hour systems suit 6–10 PM peaks and morning ramps—earning ~₹12,250/day, with ~2.2-year payback (validated by SECI’s ₹3.52 lakh/MW/month BESS tender). Grid challenges persist—17% AT&C losses, $11.38Bn DISCOM debt—but BESS enables peak shaving, coal displacement, and supports the 500 GW RE goal. With VGF and evolving real-time markets, storage economics are shifting. Still, let’s be clear: arbitrage won’t last forever. As BESS scales up, price differentials may shrink. Grid modernization, smart dispatch, and reforms like Time-of-Day tariffs will compress margins. Arbitrage is a bridge—not the destination. Storage, especially lithium-ion, remains central to grid integration. With AI-driven IEX bidding, profit margins can rise 10–15%. Winter peaks (Nov–Jan) offer more windows. India’s grid is evolving. Storage is no longer a tool—it’s the backbone of resilience. Let’s seize today’s arbitrage, while preparing for tomorrow’s smarter, stacked models. #IndianEnergy #GridOperations #EnergyStorage #BESS #IEX #Arbitrage #Renewables #NetZero2030 #StorageEconomics #PeakDemand

EV Demand Management Aggregation Is Commercializing There are four pathways for exploiting the massive battery capacity that's usually sitting idle in electric cars. Some have a lot more potential than others. Full article with graph of scenario: https://lnkd.in/gmrcUDyE Vehicle-to-Grid (V2G): Using EV batteries to supply power back to the grid during peak demand. While conceptually promising, V2G faces critical challenges. Cars are typically plugged in during peak demand, making them contributors to the problem, not the solution. People are hesitant to let utilities use their batteries due to concerns about battery degradation and insufficient compensation. Kahneman's prospect theory is informative. Vehicle-to-Home and Task Power: EV batteries used as backup power for homes or tools at work sites. This approach has niche applications, primarily in markets like the U.S. and Australia, where detached homes with private driveways or small off-grid work sites are common. This is impractical for the majority of the global population, who live in multi-unit buildings with shared parking. For work sites, EV batteries are useful for small tasks but are quickly being overshadowed by large scale electrification. Automatic Demand Management in Buildings: This pathway is already gaining traction in parking lots for fleets, offices, malls, commercial buildings, and multi-unit residences. Operators face significant demand charges for electricity use during peak hours. Automatic systems dynamically pause or reduce EV charging when demand is high, saving costs and reducing grid strain. This is becoming a standard feature as EV adoption accelerates. Aggregated Demand Management: Aggregating EVs in a grid area to form large demand reduction blocks offers utilities a powerful tool for grid management. Companies like BluWave-ai are delivering this today, and utilities in Europe are signing up EV owners for it. Automatic demand management in buildings and aggregated systems for utilities are shaping up to be the dominant strategies. As I predicted four years ago, these approaches align incentives and overcome key barriers to scale. V2G and V2H, while dominant in the popular press and a lot of literature, will be also rans. If you’re an EV driver in Ontario or Prince Edward Island, consider signing up with BluWave-ai in their current round of driver onboarding.

Peak demand is the most expensive problem in electricity. A 15-minute DC fast charge can create a demand spike exceeding 1 MW per vehicle, requiring oversized transformers and stranded distribution assets. These short-duration, high-amplitude peaks lower asset utilization and force utilities into costly overbuilds. The spike isn't the only part of the issue. We also have to consider the human behavior behind fast charging. When people fast charge, it’s usually because they’re in the middle of a trip or scrambling to recover from forgetting to plug in. In those moments, they’re inflexible. They need energy right now. That urgency means utilities can’t shift the load. At home, the opposite is true. Whether your car fills at 9 p.m. or 11 p.m. doesn’t matter... as long as it’s ready by morning. That flexibility is gold for utilities. It allows charging to be spread out, shifted to off-peak hours, and harmonized with other loads. That’s why a distributed, low-power Level 2 model produces a long-dwell, low-amplitude load curve. The aggregate effect is a flatter, more predictable demand profile: - Loads are shifted into overnight off-peak periods - Transformer capacity is preserved by spreading kWh delivery over time - Distribution utilization improves, increasing ROI on existing assets When deployed in multifamily properties (dense clusters of vehicles colocated near commercial load centers), this model supports local grid balancing without requiring new generation. The outcome is a rare alignment: Utilities reduce capital costs, property owners provide charging at scale, and EV drivers gain convenience. This isn’t about “slow vs. fast charging.” It’s about aligning charging profiles with utility economic models. #EnergyManagement #UtilityEconomics #EVInfrastructure

It’s 16:30 on a Tuesday. Your facility manager just received September’s electricity bill. €47,300. 8% higher than August. Same kWh consumption. He can’t explain why. Neither can you. Here’s why this happens (and how to fix it in under 30 days): The structural problem: European electricity bills have two cost components most companies overlook: Energy cost: €/kWh × consumption Peak demand charges: penalty for maximum consumption during specific windows That second component can reach 30–40% of your bill. And it’s optimizable without reducing production. The Temporal Optimization Framework (implementation: 30 days): Week 1: Temporal Consumption Mapping You don’t need expensive tech—just data you already have. Tools: bills from the last 12 months + monitoring software. Objective: identify three things: Which equipment consumes most? When does it operate? Does it coincide with premium price windows? Output: visual map of hourly consumption vs rates. Week 2: Quick Win Identification Ask operations: “Which of these processes MUST run at these hours for technical reasons?” Answer: “None. We’ve always done it this way.” Quick wins: → Battery charging (forklifts, equipment): move to 02:00–06:00 → Cleaning cycles: shift off-peak → HVAC pre-heating: adjust timing → Batch processes: reschedule off-peak Week 3: Pilot Test Don’t change everything at once. Pick one high-consumption process and move it off-peak for two weeks. Measure: Operational impact (usually none) Bill reduction (typically 12–18% for that line item) Example (logistics company, Belgium): shifted EV charging from 18:00–20:00 to 23:00–05:00. Savings: €28K annually. Implementation: 4 days. Week 4: Rollout & Monitoring Extend changes to all identified processes. Add alerts if peak use exceeds thresholds. The maturity model: Level 1 (70% of companies): Monitor monthly consumption. Level 2 (30-day goal): Monitor hourly consumption. Optimize timing. Level 3 (6-month goal): Automate load shifting on real-time prices. Typical investment: Monitoring software: €2–5K Facility manager time: 20 h over 4 weeks Operational changes: €0 (reprogramming) Typical return: Cost reduction: 15–25% Payback: 2–4 months Year 1 savings: €50K–250K depending on facility size The metric that matters: Your timing Power Factor — ratio between peak and off-peak consumption. Ideal: 0.6 (40% less in peak). European average: 1.4 (40% more). If you don’t know your ratio, you’re overpaying. Immediate action: Ask your supplier for hourly consumption data from the last three months. With that and a few hours of analysis, you have your savings roadmap. Timing isn’t sustainability. It’s operational intelligence. Are you going to keep paying premium for convenience? Sources: Helexia Belgium operational efficiency projects, European tariff structures, Industrial Energy Management best practices. #LoadShifting #SmartEnergy #FacilityOptimization #EnergyManagement #IndustrialIoT #OperationalEfficiency #Helexia