by Planning Engineer (Russ Schussler)
Wind and solar power are often touted as the cheapest sources of electricity in many regions, capable of delivering low-cost energy for the vast majority of the time. At first glance, this might suggest that an energy mix heavily weighted toward renewables would be the most economical choice. However, this assumption overlooks a critical issue: the fat tail problem. Just because a resource is cheaper most of the time does not mean it reduces overall system costs. This post, the first in a series, explores why prioritizing wind and solar can lead to higher costs, starting with an analogy from the financial world.
The Fat Tail in Finance: A Cautionary Tale
To understand the fat tail problem, let’s consider a financial scam once common in late-night infomercials: “Make money on over 90% of your trades—guaranteed!” These ads promised that with their trading strategy, you’d win on 90% of your trades and lose on less than 10%. Sounds like a surefire path to wealth, right?
Not so fast, this is too easy. The flaw lies in the magnitude of the wins and losses. Investments often rise gradually but can plummet dramatically. If you make small gains 90% of the time but suffer massive losses the other 10%, the overall result can be catastrophic. The percentage of winning trades is a poor metric for profitability when the losses are disproportionately large. This is the fat tail problem: rare but extreme events drive the economics.
The Fat Tail in Power Systems
Just as rare but massive losses in trading can wipe out gains, peak demand periods in power systems drive costs that overshadow renewables’ savings during easy times. Electricity demand fluctuates, and supplying power is far more challenging—and expensive—during certain periods. At the end of this post, I have provided a more detailed and quantitative discussion as to how and why the fat tail becomes a major factor impacting energy costs. So as not to lose many readers, I will proceed with a more generalized description here.
Typically, the most difficult times are peak demand periods in winter and summer, which account for less than 5% of the year. During a single hour of peak demand, electricity costs can spike orders of magnitude higher than the typical average cost, forcing utilities to rely on expensive backup plants that sit idle most of the year. For example, during the January 2014 Polar Vortex, a massive cold snap gripped the eastern U.S., driving electricity demand for heating across the PJM Interconnection to record levels. With no spare power to share among states, wholesale prices soared to $2,000 per megawatt-hour, over 60 times the typical $30/MWH average. Smaller localized events are more common with less drastic price fluctuations, but they contribute as well to the fat tail problem.
These types of scenarios can be greatly worsened by the duck curve as illustrated and described below.
Figure 1: The Duck Curve, showing how solar power creates sharp demand spikes at dusk, driving fat tail costs
As a worst case, imagine the duck curve scenario on a peak summer day. As consumers need more and more electricity commercial and home solar drop off significantly requiring a massive fast ramping from an array of dependable generation resource. For annual peak conditions, large costly resources, that may not be needed again all year might have to be called into service at great cost. For a winter peak a similar situation happens just before daybreak. High levels of electricity are required as individuals, businesses and factories deal with oppressive cold and prepare for the coming day.
In contrast, “easy” times, when demand is low and supply is abundant, make up 90% or more of the year and this is where energy and variable cost average are set. It’s a completely different story during hard times for demand and fixed charges. Historically, a single hour of peak demand could determine a utility’s annual peaking charges, highlighting the outsized impact of these extreme conditions.
Wind and solar often shine during easy times, producing electricity at a lower marginal cost than traditional sources like natural gas or nuclear. However, their output is intermittent and less reliable during peak periods, when weather conditions may not align with demand. Relying heavily on renewables requires backup systems—often expensive fossil fuel or nuclear plants—to ensure reliability during these critical fat tail events. The cost of maintaining these backup systems, combined with the infrastructure needed to integrate intermittent renewables, can greatly outweigh the savings from cheap renewable energy during easy times.
As I’ve noted before, “Energy ‘plans’ that call for wholesale changes but do not consider how the final overall system might work are not plans but rather only naïve wish lists.” Policymakers often push wind and solar based on their low costs in favorable conditions, ignoring the fat tail problem and the higher system-wide costs that result.
A Car Analogy: Efficiency/Marginal Costs Aren’t Everything
Consider a practical example. Imagine you’re choosing between two cars. Car A is fuel-efficient and meets your needs 90% of the time, but 10% of the time, you need Car B, which has more power and extra seating. Car B is less efficient, but it’s essential for those critical moments. Would you also buy Car A just because it’s cheaper to operate 90% of the time? Probably not—owning two cars would likely cost more than paying the extra fuel costs for Car B alone.
Similarly, building wind and solar farms to supply cheap energy during easy times doesn’t eliminate the need for reliable resources like natural gas or nuclear during peak periods. The added costs of constructing, maintaining, and integrating renewables—while still paying for backup systems—often make the overall system more expensive. Detailed power system modeling and real-world experience confirm this, yet the misconception persists that renewables’ low marginal costs guarantee economic benefits.
Talking Past Each Other
The fat tail problem may explain why energy debates often feel like ships passing in the night. Proponents of renewables emphasize their low average costs, while generation planners focus on the system-wide associated with the full array of needed generation resources. This disconnect stems from a kind of innumeracy—failing to go beyond average costs to account for the disproportionate impacts of serving peak periods and rare costly events.
In a sad case of common sense gone wrong, Renewable Portfolio Standards (RPS) and similar mandates were enacted under the assumption that renewables are inherently economic. The experts’ models showed otherwise but were often dismissed as biased since they didn’t reflect the value of the “cheap” renewables. In reality, they reflected the fat tail’s harsh arithmetic. This critical insight was overlooked by too many policymakers focused on short-term goals, advocates driven by enthusiasm, and academics unaware of real-world considerations.
Why do financial scams, which also exploit fat tail misunderstandings, fool fewer people than renewable energy promises? Perhaps energy systems’ complexity obscures the fat tail problem, while emotional appeals and trusted institutions lend renewables undue credibility. Also, unlike personal investments, energy policy involves collective costs, perhaps reducing individual scrutiny.
Modern civilization needs electricity most all of the time. Otherwise wind and solar would be a better deal. But having energy 80% or 90% of the time is not enough. Although there are many programs and approaches employed to limit electric use during peak times, large amounts of electricity are not shiftable away from peak periods. Consumers need cooling when it is hot and heating when the temperature is frigid. Those needs ensure the fat tail can’t be significantly slimmed down.
To be clear, I don’t think the issues have commonly been discussed in terms of fat tails. We’ve had a lot of engineers and financial analysts speaking in terms of system costs, that went past and over the heads of the relevant audience. The rebuttals of academics and advocates, as to the economics of wind and solar, have puzzled the engineers and financial experts who generally have not had the clout to cross examine and seek to find clarification. In most cases policy makers with or without needed understandings had the power and made the decisions based on overly optimistic expectations for wind and solar. A word to the wise – those speaking only in terms of average costs should not be trusted in decision making for complex systems. Beware of misleading metrics.
Looking Ahead
The fat tail is just one piece of the puzzle. While it’s a critical and often misunderstood factor, other issues also drive up the cost of wind and solar. Future posts in this series will explore these factors in detail, providing a comprehensive explanation for why “cheaper” wind and solar can and usually do lead to greatly increased electricity costs. Future posts will discuss home solar, focus on utility economics, discuss problems with energy markets and delve into many of the often ignored unaccounted costs associated with wind and solar.
For now, the key takeaway is this: in power systems, as in finance, focusing on what happens most of the time can blind us to the catastrophic costs of what happens less often. The fat tail problem demands a holistic approach to energy planning—one that prioritizes reliability and affordability over simplistic cost comparisons.
Bonus Section: Why are there Fat Tails in Power Systems?
Let’s look at some of the reasons electric systems are prone to have fat tails. Electric demand varies based on the time of day, the day of the week, time of year and of course across many weather-related conditions most importantly temperature. The variance caused by these factors can be seen in a load duration curve. Load duration curves are formed by ordering annual hourly demand from the maximum value observed during the year to the minimum value. Below is a typical load duration curve.
Figure 2: Load Duration Curve, illustrating how peak demand (right) occurs briefly but drives system costs
Moving from right to left we see that values near the peak do not persist for long and as we move to the left, we see that the load drops well below 40% of the peak value for almost a third of the time. For this typical system, only 1.5 % of the time is the load within 90% of the peak value. As shown above only 5% of the time is load within 80% of the peak value. Lower load levels predominate as 50% of the time the load is less than 46% of the peak value.
More pronounced than the changes in demand associated with an electric system are the differences in energy costs hour to hour. The incremental cost of the next bit of energy is called the system lambda. This is a good indicator of the variable cost to serve extra energy each hour. For ERCOT (Texas) last year, the average system lambda was around $25 to $30/MWh. Most values fell between $10 and $100/MWh. But the full range extended from -$10/MWh to around $5,000/MWh. The California ISO maintains a System Marginal Energy Cost similar to a system lambda which last year averaged $20 to $30/MWh, with most values in the range of 0 to $100/MWh, but the full range extended from -$100/MWh to $2,000/MWh.
The range of marginal (incremental) costs is sweeping. Increased wind and solar work to make the ranges even more pronounced than they would be otherwise. Some of you may be scratching your heads seeing the negative values above. Let me explain: Nuclear today pretty much runs full out all the time. Other units, like coal and natural gas combustion turbines have minimum operating levels. There are costs associated with shutting down nuclear, coal and combined cycle units and once shut down they have various minimum down times which might prevent them from being available later if needed. For many units providing needed power during high demand periods means they must generate 24 hours a day. Sometimes wind and solar are given priority to operate whether the power is needed or not. The above factors lead to more energy being available than can be used by the system. A negative lambda is used to discourage generation, and plants are charged for contributing power during these times. (Note- in some times and places due to contractual arrangements and regulations wind and solar might be paid during times of energy surplus even when others are charged for contributing energy.)
We haven’t considered fixed prices here, but just the above-mentioned factors indicate that fat tails can play a big role. High-cost system lambdas may be a couple orders of magnitude above the average system lambda and even worse at times the value of energy is negative.
Before there were significant penetrations of intermittent resources, generation was generally classified as peaking, intermediate and baseload. The incremental costs of each were limited, often well-known and bounded at all but the most extreme times. It was fairly easy to predict load and determine what generation patterns would follow and their associated costs. Intermittent generation changed that situation drastically. Loads can be rising as intermittent generation is decreasing or the reverse. The resulting changes in incremental costs can be stunning at times. As intermittent resources increase power system costs are a fat tail problem on steroids.
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