Genetic algorithm based optimal contracting strategy in a typical standing reserve market

This paper proposes a coding scheme enhanced by problem specific knowledge for a genetic algorithm (GA) based contracting strategy used for optimal selection of standing reserve tenders, the aim of which is to provide the required operating reserve most economically. The proposed coding scheme enables the GA to handle tenders with flexible commitments, hence, to have greater potential to find lower cost solutions. The problem specific knowledge aims to significantly reduce the search space in the aid of reducing solution variance when comes to deal with large systems, such as the practical England & Wales power network. The effectiveness of the proposed technique is demonstrated on a small test system and the England and Wales power network with 83 tenders. The test results suggest that the cost of providing operating reserve has been significantly reduced when the GA is able to deal with tenders with flexible commitments, and further cost reduction and solution variance improvement can be achieved when incorporating the problem specific knowledge into the GA search.