To embrace change or fight it? The choice to either act or wait and see is fraught with complexity.
Anatomy of Sealed-Bid Auctions
Bringing flexibility and efficiency to energy RFPs.
pricing structure in Maryland’s RFP, the utility only needs to rank each bidder’s bid from least expensive to most expensive and identify the two least-costly bidders. The complexity of this problem increases almost linearly with the number of bidders (see Table 4) . Under the pricing structure in PPL Electric’s RFP, the utility needs to consider all combinations of bids that yield 10 tranches and identify the least-costly combination. The complexity of this problem increases much faster with the number of bidders (see Table 4).
It might appear that the increase in complexity that accompanies conditional bidding makes such pricing structures in electricity auctions unrealistic. However, this is not the case. While auctions with conditional bidding cannot necessarily be solved using a spreadsheet program like Microsoft Excel, computer software quickly can solve the combinatorial mathematics presented by conditional bidding. For example, determining the winning bidders needed to obtain 10 tranches when 15 bidders each bid to provide a maximum of eight tranches requires assessing more than 3 million combinations; the most basic computer algorithms can identify the least costly combination of bids in about 45 seconds on a common office computer. Advances in computational economics have made it possible to solve much more complicated pricing structures associated with conditional bidding.
In addition, in recent years it has become more common for auctions to be carried out over the internet. Electricity supplies are being procured using internet-based platforms in some states, and many large businesses use such systems as a means of procuring various goods and services. The submission and analysis of offers can be simplified using these internet-based systems. Moreover, the more complex computational challenges easily can be evaluated using computer systems specifically designed to process auction information.
The sealed-bid auctions used to procure electricity to supply non-shopping customers range from the simple to the more complex. However, many of these auctions use structures that constrain types of offers that bidders can make. Appropriately finding ways to make these auctions more flexible, using advances in computational economics, will yield better pricing outcomes for ratepayers. In the process, regulators also should consider implementing these auctions through internet-based platforms. It’s not difficult to improve these existing auctions by focusing on the tranche size, bid format and offer pricing structure, and the auction clearing methodology.
1. There are two notable exceptions: 1) In Maine, the Maine Public Utilities Commission (ME PUC) issues RFPs for wholesale power supply and selects suppliers as opposed to the two local utilities, Bangor Hydroelectric (BHE) and Central Maine Power (CMP), soliciting wholesale power supplies with oversight from ME PUC; and, 2) In Massachusetts, the Department of Public Utilities does not oversee wholesale power procurements by utilities if they are purchasing power products with a term of one year or less.
2. See, for example, Tierney, Susan F., and Todd Schatzki, Competitive Procurement of Retail Electricity Supply: Recent Trends in State Policies and Utility Practices , for the National Association of Regulatory Utility Commissioners, July 2008; and LaCasse, Chantale, and Thomas Wininger, “Maryland versus New Jersey: