Stochastic-based risk-constrained optimal self-scheduling for a generation company in electricity market

In the deregulated power industry, a generation company (GENCO) has to sell energy and ancillary services through a market environment. This paper develops a methodology that allows the GENCO to perform stochastic price-based unit commitment and optimal self-scheduling to obtain maximum profit and minimum financial risk raised from uncertainty of the electricity market price. The risk is properly incorporated into the model using conditional value at risk (CVaR) methodology. Uncertainty of the market clearing price, as the main source of financial risk in the electricity market, is handled by treating hourly prices as stochastic variables which are modeled by scenario approach, while the Monte Carlo simulation is adopted to generate discrete random market prices. The procedure developed in this paper utilizes a comprehensive model of the production units that makes it suitable for practical operation. Moreover stochastic mixed-integer programming framework has been used to formulate the problem. Further analysis and concluding remarks are provided through an illustrative case study.