An optimization-based framework for the quick analysis of power transactions

Effective power transactions can reduce generation costs for an electric utility. Making good transaction decisions, however, is not an easy task since transactions are coupled with the scheduling of generating units through power system demand and reserve requirements. The speed and quality of transaction decisions are nonetheless becoming critical to capture frequently emerging opportunities in the increasingly competitive power market. As transaction opportunities emerge, the known opportunities can be analyzed one at a time. When a large number of such opportunities emerge one by one as time proceeds, this kind of analysis will however lay a huge burden on transaction analysts. A different framework is presented in this paper. While analyzing a known set of opportunities, the economic effects of further purchasing/selling fixed sizes of power blocks are also analyzed. Within the framework, the integrated scheduling and transaction problem is solved by using the Lagrangian relaxation method. Four transaction modes that optimize transaction level and/or duration are developed to deal with various transaction opportunities. A reference table is then established to help transaction analysts quickly deal with emerging opportunities. The algorithm can also be re-run periodically to update the reference table while solving another set of known transactions. Numerical testing results based on Northeast Utilities (USA) data sets show that results can be obtained in reasonable computational times to help transaction analysts make quick and prudent decisions.