Building and Evaluating GENCO Bidding Strategies and Unit Commitment Schedules with Genetic Algorithms

Abstract

Far from being an artifact of the past, the unit commitment (UC) algorithm is essential to making economical decisions in today’s competitive electricity industry. Increasing competition; decreasing obligations-to-serve; and enhanced futures, forwards, and spot market trading in electricity and other related markets make the decision of which units to operate more complex than ever before. Decentralized auction markets currently being implemented in countries like Spain use UC-type models, which should encourage researchers to continue working on finding better and faster solution techniques. UC schedules may be developed for a generation company, a system operator, etc. The need for many flavors of UC algorithms, each considering different inputs and objective functions, is growing. Factors such as historical reliability of units should be considered in designing the UC algorithm. Although a particular schedule may result in the lowest cost, or highest profit, it may depend on generators that have varying availabilities. Traditionally, consumers had very reliable electricity whether they needed it or not. Given a choice in a market-based electricity system, many consumers might choose to pay for a slightly lower level of power availability if it would result in sufficient savings. As the number of inputs and options grows in UC problems, the genetic algorithm (GA) becomes an important tool for searching the large solution space. GA times-to-solution often scale up linearly with the number of units, or hours being considered. Another benefit of using the GA to generate UC schedules is that an entire population of schedules is developed, some of which may be well suited to situations that may arise quickly due to unexpected contingencies.