It's as significant for what it does not do as for what it does.
Order 888 marks a significant, yet limited, step in deregulating the U.S. electricity supply industry. Most...
and each ancillary service in which suppliers offer an amount of capacity for a given period of time for a particular price (i.e., the bid price). The coordination entity (e.g., ISO) selects winning offers on the basis of rank-ordered bid price until the projected demand is met.
The most commonly used settlement rule is to pay winning participants at a rate equal to the last (i.e., highest) accepted bid price, irrespective of the participant's own bid price. This "last bid pays price" approach is referred to as a uniform price auction, and is well documented in economic literature (e.g., Feldman 1993). It is not clear, however, why this settlement rule has become the one most often applied to multi-unit, multi-dimensional markets for electricity. A number of researchers have begun to question the use of "last bid pays price" settlements in these markets, and suggest that there are other settlement rules that can produce a more efficient and cost-effective outcome (Mount 1999, Oren 2000).
The rule proposed by Mount (1999) and evaluated in this article is the discriminatory price auction. Under this method, the selection process is the same as described above, but, in this case, the winning participants are paid exactly what each bid. This method also has been called the "pay as bid" approach.
Mount (1999) provides a good theoretical basis for using a "pay as bid" price auction in electricity markets. My analysis evaluates the quantitative benefits to the consumer of using this approach, as compared to the uniform price approach for settlement of energy and ancillary service markets in a typical, large transmission control area.
A Market Simulation: Measuring the Impact of "Gaming"
In order to obtain cost results with realistic orders of magnitude, the Oak Ridge National Laboratory Electricity Market Model was used to simulate a large transmission control area having a peak annual energy demand of 50,000 megawatts. This multi-generator, multi-hour simulation model is a tool for better understanding, testing, and predicting the resulting prices, participation in, profits, and coverage of the interrelated, competitive electric energy and ancillary services markets.
The generating units available to meet the load were modeled after plants located in the PJM Interconnection control area. Recent actual plant data served as a guide in developing model inputs. Though similar in size and composition to the PJM market, this analysis and associated simulations are not intended to portray the PJM area specifically, but rather, to provide results that are representative in behavior and magnitude of large, multi-plant control areas having a broad mix of generation types.
As mentioned earlier, aberrant price behavior occurs frequently when a system experiences high demand. For this analysis, a peak hour was simulated in which the system load factor was 94 percent. In addition to an energy demand of 48,700 MW, ancillary service demands included 500 MW of regulation, 1,150 MW of spinning reserve, 1,000 MW of load following, and 500 MW of non-spinning reserve. Bids for each plant were based on the expected marginal price for that hour. In practice, suppliers do not know with certainty what the