The Texas crisis need not be the death of dynamic pricing.
The Texas energy disaster last month will yield many important lessons for assuring resource adequacy in a time of increasingly unpredictable weather. The supply shortfall in ERCOT, which Jim Bushnell wrote about two weeks ago, amounted to about 25% of what customers would have liked to consume and it continued for about four days. That’s roughly 10 times larger than the shortage California had last August, and lasting more than 20 times longer.
Still, one takeaway from Texas is the same as here: demand adjustment should be playing a much larger role during times of tight supply. Among nearly all energy economists, and an increasing number of grid operators and utility managers, there is a consensus that the current approach of paying for demand reduction is not working well, and that dynamic pricing is a more cost-effective approach to demand response during high stress on the grid.
Yet, the Texas crisis made abundantly clear the problems of exposing customers to spot price volatility and risking mammoth bills. The “simple” solution that I have heard from many economists is hedging. For instance, a customer could purchase its expected consumption quantity in advance, before the weather is known, which on a forward market would have cost $20-$30 per MWh for Texas power in February.
Locking in a price in advance for a fixed quantity dampens the customer’s bill volatility. At the same time, the customer still has the full price incentive to reduce consumption during a price spike, because every MWh the customer saves is one less additional MWh it has to buy at the high spot price. (Or, if the hedge quantity is greater than its demand, it’s one more MWh it can sell back at the spot price).
Customers hedging their expected consumption is a good start, but in Texas last month it would have been woefully inadequate.
To see just how inadequate, consider Max’s Strudel Hut, a Texas customer on a pricing plan that passes through the wholesale spot price to customers like the Strudel Hut. Let’s say that Max hedged this exposure to the spot market by purchasing the Hut’s expected February consumption through a forward market at $25/MWh. ERCOT estimates the full demand during the 95-hour crisis averaged about 65% above its typical daily February load. Say that the Strudel Hut’s demand rose during the crisis by the same proportion as system demand, so it ended up buying “only” about 40% (0.65/1.65) of its actual consumption at spot prices. That means that for the 95 hours of the extreme electricity crisis it bought 60% of its power at $25/MWh and 40% at ERCOT’s $9000/MWh price ceiling (where the spot price remained throughout the crisis). If you do the math, Max’s Strudel Hut would face an electricity bill for the month of February about 34 times higher than expected.[i]
The problem is that the Hut’s demand is positively correlated with the price, so hedging typical consumption turns out not to be very much protection. One way to address this correlation is to over-hedge, that is, to purchase on the forward market a quantity larger than the Hut’s expected consumption, as shown by McKinnon in 1967 (and rediscovered by me 40 years later).
But there is an even better solution: a hedge quantity that fluctuates proportionally with system demand. To be concrete, let’s say that Max’s Strudel Hut averages about 1 MWh per hour during normal times (yes, that’s a lot, but it’s really outstanding strudel), and its demand for electricity fluctuates in the same proportion as system demand. Max buys 1 unit of this “quantity-indexed” hedge at a given price for the month (we will get back to the price in a minute). Say the average system demand is 40 GW in February, so the hedge contract delivers 1 MWh of power at the contract price if system demand is 40GW in an hour. If the Strudel Hut consumes more in that hour, it has to buy the extra at the spot price, and if it consumes less, it automatically sells the residual quantity back to the market at the spot price. Either way, the cost (or opportunity cost) of consuming additional electricity is the spot price.
If system demand is 60 GW in a given hour, however, the hedge contract delivers 1.5 MW of electricity at the same contract price. And if the system demand is 30 GW, the contract delivers 0.75 MW. Regardless of the hedge quantity, any change in the Hut’s consumption still costs (or benefits) Max the spot price in that hour. Still, the fluctuating hedge quantity means that the hedge in a given hour is likely to match the Hut’s consumption much more closely than a constant-quantity contract. And if the Strudel Hut’s demand varies less (or more) than system demand, Max can go with a combination of the quantity-indexed hedge and a constant-quantity hedge to get as much or little variation as he needs to match the Hut’s correlation with system quantity. So, Max doesn’t have to worry about huge electricity bill fluctuations and can instead focus on developing his special recipe for lebkuchen before the holiday season.
Importantly, the hedge quantity would not change with the Strudel Hut’s own electricity usage; that would undermine the incentive to conserve when the price is high. Actually, that is what the most common pricing plans today do by giving a customer the option to buy all it wants at a preset price, which is called a “requirements contract”. Instead, the quantity-indexed hedge would change with the total system demand, a quantity the Hut’s usage doesn’t affect, so the hedge itself doesn’t change its incentive to consume.
In practice, well before each month begins, Max would choose how many units of the quantity-indexed hedge to buy, or his retail provider could do it for him based on the Hut’s past consumption and the goal of minimizing bill volatility. Either way, once he makes the purchase, he couldn’t change the hedge quantity in specific hours. So, sellers of the hedge (probably a utility or a financial market participant, possibly through a market mediated by an RTO/ISO) can price them by analyzing the relationship between market demand and the spot electricity price. For those who think about such things, the seller might not easily be able to balance such a contract completely with its own purchases. One natural counterparty, a generator, would face performance risk in its electricity production, as we saw in Texas. A retail provider selling such hedges, however, would still be far less exposed than it is now when it offers a requirements contract.
Note the difference between such dynamic pricing with hedging and programs that pay customers for reduction from a baseline that is a function of the specific customer’s past demand. Programs that pay for reduction are subject to (a) baseline manipulation, (b) baseline selection – customers often get to choose among different baseline formulas, so understandably choose the one that delivers the highest payout with the least effort/quantity reduction, and (c) participation selection – customers who don’t find a program that is a winner for them simply choose to continue buying all they want during price spikes under a fixed-price requirements contract.
Dynamic pricing creates more powerful incentives for conservation during high-priced periods, and thoughtfully-constructed hedging greatly reduces bill volatility. At the same time, it reduces rewards for apparent reductions that are actually just due to clever baseline strategies or participation choices. Large and sophisticated customers could make their own hedging decisions, and would more effectively lower bill risk with a quantity-indexed edge as described here. Less sophisticated customers could have their retailer hedge for them, or they could sign up for a simpler dynamic pricing plan, such as optional critical peak pricing.
Dynamic pricing isn’t going to completely replace other demand response programs anytime soon. We will still want to have interruptible rates that a system operator can activate just before it has to resort to involuntary outages. But well-crafted dynamic pricing can greatly reduce the need for more drastic interventions without subjecting customers to unacceptable bill risk. And it would go a long ways towards keeping the Texas and California electricity markets out of the news.
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Suggested citation: Borenstein, Severin. “Texas, Hedg’Em” Energy Institute Blog, UC Berkeley, March 15, 2021, https://energyathaas.wordpress.com/2021/03/15/texas-hedgem/
[i] Showing my work: During an average February, Max’s Strudel Hut would purchase 1 MWh/hour, or 672 MWh, at $25/MWh for a total bill of $16,800. During the 95-hour crisis, it would have purchased an extra 0.65 MWh each hour, or 61.75 MWh, at $9000/MWh for an extra cost of $555,750, or a total bill of $572,550, 34 times higher than its expected bill.
Severin Borenstein is Professor of the Graduate School in the Economic Analysis and Policy Group at the Haas School of Business and Faculty Director of the Energy Institute at Haas. He received his A.B. from U.C. Berkeley and Ph.D. in Economics from M.I.T. His research focuses on the economics of renewable energy, economic policies for reducing greenhouse gases, and alternative models of retail electricity pricing. Borenstein is also a research associate of the National Bureau of Economic Research in Cambridge, MA. He served on the Board of Governors of the California Power Exchange from 1997 to 2003. During 1999-2000, he was a member of the California Attorney General's Gasoline Price Task Force. In 2012-13, he served on the Emissions Market Assessment Committee, which advised the California Air Resources Board on the operation of California’s Cap and Trade market for greenhouse gases. In 2014, he was appointed to the California Energy Commission’s Petroleum Market Advisory Committee, which he chaired from 2015 until the Committee was dissolved in 2017. From 2015-2020, he served on the Advisory Council of the Bay Area Air Quality Management District. Since 2019, he has been a member of the Governing Board of the California Independent System Operator.