Knowing how to maximize profits from existing generation equipment is just one challenge that electric power generators face. An equally important challenge is making wise investments in expanded generating capacity for the future. Because future electricity demand and prices are unknown, decisionmakers have difficulty knowing how much, where, and what kind of additional generating capacity will give a good--let alone the best--payback and help meet growing demand efficiently. And the number of electricity-generating options now available is daunting. They range from traditional electric power plants to various innovative technologies with widely varying costs and characteristics.
The pricing model and decision-making tool described in the previous article can help such decisionmakers identify good investments for the future. Indeed, unlike most investment analysis techniques, the MIT methodology can tell generating companies not only how much but what type of generating capacity to invest in. Because the methodology includes a physical model of the power system, it can recognize important aspects of operating different types of generating units, for example, the time and cost involved in starting up and shutting down the unit. Thus, it can recognize the special value of units such as microturbines and fuel cells, which are small but highly flexible. The methodology can assess a generator's potential long-term cash flow from building a microturbine versus a nuclear plant, and it can determine which of those technologies would best match the typical level and pattern of the demand in the region.
With further development, the MIT technique could be commercialized into software that industrial decisionmakers can use to assess their investment options. But already the methodology is helping the MIT researchers understand the evolving electric power industry, in particular, how market prices influence generator investment and thus the capacity available on a power system. In any commodity market, when growing demand leads to scarcity of supply, suppliers respond by expanding their production capacity. On an electric power system, this process is critical: it is the key to avoiding capacity shortages that may lead to blackouts.
In recent work, Dr. Ilic, Mr. Skantze, and graduate student Poonsaeng Visudhiphan simulated the investment behavior of generators in order to investigate two questions. How might changes in spot prices affect generators' investment decisions and ultimately systemwide generating capacity over time? And what happens to investment and systemwide capacity when regulators put caps on prices on the spot market, as they did in California?
To address those questions, the researchers used their pricing model and decisionmaking tools to simulate the investment decisions of generators and calculate the resulting effect on systemwide capacity as spot prices change over time. The two figures below present sample results for a hypothetical power system. The top figure shows the variation in spot price over time, with and without price caps. The bottom figure shows the changing reserve margin, defined as excess available capacity as a fraction of total demand at a given time.
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The link between high prices and increased reserve margins is evident in the curves representing the uncapped situation. High spot prices signal growing demand and dwindling supply; generators invest in additional generating capacity; and the reserve margin grows. However, there is a considerable time lag between the high prices and increases in the reserve margin.
When market prices are capped, both prices and reserve margins behave differently. The cap does eliminate price spikes. But keeping prices artificially low when supply is scarce removes the signal and a primary incentive--greater earnings--for generators to invest in new generating capacity. As a result, the estimated reserve margin is generally far lower than in the uncapped situation, and at times it goes well below zero--a situation in which the system operator has to invoke rolling blackouts to prevent the collapse of the entire system. Interestingly, at times the capped price is higher than the uncapped price. The explanation lies in the investment response. With prices capped, an economic signal for generators to build new plants is removed. The supply scarcity continues, so prices do not drop.
Based on their analysis, the researchers point out a trap that regulators must avoid. Using price caps to flatten out spot prices will lead to periods of underinvestment, capacity shortages, and very likely a higher average price of electricity over a several-year period. Regulators may then be tempted to force down the average price by further reducing the level of the price cap--a move that will further reduce investment. Over the long term, investment will not keep up with demand growth; and capacity shortages and service cutbacks are almost inevitable.
The MIT researchers recognize that imposing price caps can be an important means of keeping generators from driving up prices during shortages. But regulators must choose their caps carefully. A cap must be sufficiently high that prices can still stimulate investment as needed for system reliability. Among the products from the MIT team is a formula for calculating price caps that are low enough to protect customers from artificial spikes but high enough to send appropriate investment signals.
The MIT analysis also suggests that using long-term contracts not only enables customers to protect themselves from spikes in the spot price but also plays a critical role in stabilizing power systems by providing information on likely demand and prices in the future. That information would give generators an early warning of impending supply shortages. Their prompt investment response could eliminate the delay in capacity expansion observed by the researchers and thus reduce the likelihood of generation shortages and blackouts. The MIT researchers are now using the decisionmaking tools described in the next section to see how the early availability of price and demand information would influence individual generator behavior.
Poonsaeng Visudhiphan is a PhD candidate in the Department of Electrical Engineering and Computer Science. This research was supported by the MIT Energy Laboratory's Consortium on New Concepts and Software for Competitive Power Systems: Operations and Management and by the MIT Department of Electrical Engineering and Computer Science. Further information can be found in references.