A novel application of ACO to price transmission rights in electricity markets

Financial risk management is of high priority for participants in wholesale deregulated electricity markets due to substantial price and volume risks. Due to high complexity of a wholesale electricity market, prices can exhibit high volatility at times of peak demand and supply shortages. Electricity markets are partly dependent on characteristics such as generation, demand, weather patterns etc. Transmission Rights (TR or TRs) are designed to provide a financial hedge for markets participants in a deregulated electricity market. TRs are financial instruments that entitle the holder to a stream of revenues based on the hourly congestion price differences across a transmission path. The dynamic change in electricity prices poses greater challenges to price and compute payouts from TRs. We have observed various similarities between the TRs and Options (derivatives) in financial market and few differences as well. As a first step, we model TR as an option pricing problem. Then, we use a nature inspired meta-heuristic algorithm, Ant Colony Optimization (ACO) to compute option prices and determine future Transmission Rights payouts. Our work suggests that ACO searches computational space eliminating areas that may not provide a profitable solution. Computational time using ACO is lower compared to searching whole solution space exhaustively.