Neel Somani Discusses the Energy Market Design and How Pricing Actually Works

When the temperature drops in New England and heating bills spike without warning, most people assume the utility company is simply taking advantage of winter demand. Neel Somani, a researcher and former quantitative analyst at Citadel, says that the real answer is more systemic.

The Last Megawatt Rule

The first thing to know about electricity pricing, according to Somani, is a principle that surprises most people upon first encountering it. “The price for power is based on the last megawatt of power that’s produced,” he explains. This is known in energy economics as marginal cost pricing, and it governs nearly every competitive power market in the United States.

In practice, this means that the cost of electricity at any given moment is not the average of all the generators running on the grid. It is determined entirely by the most expensive, least efficient generator required to meet current demand.

Every producer on the grid, from a zero-marginal-cost wind farm to a decades-old oil peaker plant, gets paid that same rate. The efficient generators pocket the difference as profit. The inefficient ones break even. And consumers pay whatever it costs to keep the lights on.

This design is not an accident. It reflects a deliberate governance choice, one that encourages investing in cheaper generation over time by rewarding efficiency. But it also creates dramatic price swings when demand pushes the grid toward its most expensive resources.

A Regional Case Study: New England

New England provides an unusually clear example of how fuel competition, infrastructure constraints, and pricing rules combine to create electricity bills that leave residents asking hard questions every January.

The generation stack in New England runs roughly in order from cheapest to most expensive: renewables, nuclear (specifically the Millstone plant in Connecticut), natural gas, and finally oil. Oil generators are rarely discussed in most power markets because, as Somani notes, they are extraordinarily inefficient. They are the backup of backups, the kind of generator that sits idle for most of the year waiting for conditions that almost never arrive.

Winter changes that calculus entirely. “In the winter in New England, natural gas has to be used to heat homes. If you don’t heat your home, your pipes can freeze, and that’s super expensive to fix.” The result is a fixed, inelastic demand for natural gas that is driven entirely by survival, not by price. Households will pay nearly anything to keep their homes warm.

When residential heating consumes too much of the available natural gas supply, there simply isn’t enough left over to run the region’s gas-fired power plants at full capacity. The grid operator, facing a shortfall, has no choice but to call on those rarely used oil generators. And because the price of power is set by the most expensive unit running, the entire grid price shoots upward the moment oil comes online.

The Gas Market Amplifier

When oil generators are running and getting paid the oil-based electricity price, any natural gas that can be burned in a gas generator becomes enormously valuable. A natural gas seller sitting at the Algonquin hub, the key pricing point for New England, can see exactly what is happening. They know that their buyer can burn natural gas, generate electricity at the oil price, and pocket a substantial profit on the spread.

“It’s in your interest to keep raising the natural gas price,” Somani explains, “until it’s basically the same cost to produce a megawatt of power from a natural gas unit as it is to produce a megawatt of power from an oil unit.” The natural gas seller will push the price up to that ceiling because charging less leaves money on the table, and charging more loses the sale entirely. The market settles at a new offset, one in which both oil and gas generators earn roughly equivalent returns and consumers pay for all of it.

This compounding effect is what makes New England winters so financially punishing. It is not simply that one expensive fuel comes online. It is that its arrival reprices every other fuel in the stack.

The California Contrast

The California power market operates under a different set of rules and faces a distinct set of challenges, but the underlying logic of incentive design runs through it just as clearly. Neel Somani points to solar power as a defining feature of the California grid, one that creates its own pricing patterns.

During daylight hours, California’s abundant solar capacity sharply lowers the marginal cost of electricity, sometimes to zero or even negative values. Solar plants continue to generate even at negative prices because they receive renewable energy credits, making production financially rational regardless of spot market prices. The grid floods with cheap power while most residents are at work.

Then the sun sets. “Everyone turns on their power all at once,” Somani notes, “and then the price spikes.” The transition from solar-heavy afternoons to peak-demand evenings has become one of the most drastic price movements in American power markets, a pattern so consistent that it has its own name among traders: the duck curve.

The solution the market has developed is largely storage-based. Batteries buy cheap solar power during the afternoon and sell it back during the evening peak, capturing the spread as profit. From a market design perspective, this is the system working as intended: price signals creating investment incentives that, over time, smooth out the volatility that generated them.

What Energy Teaches About Governance

For Neel Somani, the value of understanding these markets extends well beyond electricity bills. The same principles that govern power pricing, marginal cost dynamics, incentive alignment, and compounding systemic effects apply across complex market systems, from financial derivatives to pharmaceutical pricing to digital advertising.

“I try to be very thoughtful and driven by economics,” Somani has said, describing an analytical framework he applies consistently across domains. The energy market is simply one of the most legible examples of how governance rules shape real-world outcomes. The choice to price electricity at marginal cost rather than average cost is a policy decision with enormous distribution consequences.

Leaders who understand these structural dynamics are better positioned to anticipate how systems will behave under stress, where price spikes will concentrate, and which interventions will actually change outcomes rather than simply shift costs around. Somani’s instinct to trace incentives back to their structural source is what separates his analysis from conventional commentary.