Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

Effects of data characteristics on the results of reserve selection algorithms

We tested the effects of four data characteristics on the results of reserve selection algorithms. The data characteristics were nestedness of features (land types in this case), rarity of features, size variation of sites (potential reserves) and size of data sets (numbers of sites and features). We manipulated data sets to produce three levels, with replication, of each of these data characteristics while holding the other three characteristics constant. We then used an optimizing algorithm and three heuristic algorithms to select sites to solve several reservation problems. We measured efficiency as the number or total area of selected sites, indicating the relative cost of a reserve system. Higher nestedness increased the efficiency of all algorithms (reduced the total cost of new reserves). Higher rarity reduced the efficiency of all algorithms (increased the total cost of new reserves). More variation in site size increased the efficiency of all algorithms expressed in terms of total area of selected sites. We measured the suboptimality of heuristic algorithms as the percentage increase of their results over optimal (minimum possible) results. Suboptimality is a measure of the reliability of heuristics as indicative costing analyses. Higher rarity reduced the suboptimality of heuristics (increased their reliability) and there is some evidence that more size variation did the same for the total area of selected sites. We discuss the implications of these results for the use of reserve selection algorithms as indicative and real-world planning tools.

Reserve price commitments in auctions

It is well known that the optimal auction-one that maximizes the seller's expected revenue-can be implemented using a standard auction format with a suitably chosen reserve price. This reserve price is above the seller's value of retaining the object and the mechanism requires a commitment not to sell the object below the reserve. This commitment is what makes the reserve valuable to the seller. However, in practice, a reserve price commits the seller to sell the object if the reserve is reached, but does not commit her to withhold the object from sale if bidding falls short of the reserve. In this note we investigate whether reserve prices remain valuable for the seller when she may negotiate with the highest bidder if the reserve is not met. We show that the value of the reserve price may be completely undermined if the seller is a sufficiently weak bargainer. (c) 2004 Elsevier B.V. All rights reserved.