Risk assessment of energy trading for a generation company with bilateral contracts

A methodology is developed to quantify the financial risk for a multi-unit generation company with bilateral contracts in a competitive electricity market, using Value-at-Risk as a risk measure. A Monte Carlo simulation technique is used to calculate the Value-at-Risk by modeling different pricing scenarios, bids and schedules of generation. An artificial neural network is used to forecast hourly market prices which determine generating unit schedules through a price-based unit commitment method using a genetic algorithm approach. An economic dispatch then determines local generation and purchase schedules using a linear programming solution. The profit and loss distribution that is obtained from the Monte Carlo simulations becomes the basis for Value-at-Risk calculations. Bidding strategies are formulated from a sensitivity analysis of the Value-at-Risk for various market conditions.