Title :
Risk-Constrained Generation Asset Arbitrage in Power Systems
Author :
Li, Tao ; Shahidehpour, Mohammad
Author_Institution :
Illinois Inst. of Technol., Chicago
Abstract :
A competitive generating company (GENCO) can maximize its payoff by optimizing its generation assets. This paper considers the GENCO´s arbitrage problem using stochastic price-based unit commitment while considering the associated risks. The GENCO may consider arbitrage opportunities in purchases from qualifying facilities (QFs) as well as simultaneous trades with spot markets for energy, ancillary services, fuel, and emission allowance. The tradeoff between maximizing expected payoffs and minimizing risks due to market price uncertainties is modeled explicitly by including the expected downside risk as a constraint. The downside risk is defined as the unfulfilled profit. The Monte Carlo simulation is applied to generate scenarios, and scenario reduction techniques are applied to reduce the number of scenarios while maintaining a good approximation of the exact solution. The proposed case studies illustrate the significance of arbitrage in multi-commodity markets and the importance of considering the uncertainty of market prices.
Keywords :
Monte Carlo methods; power generation economics; power markets; power system management; GENCO arbitrage problem; Monte Carlo simulation; ancillary service allowance; competitive generating company; emission allowance; energy allowance; expected downside risk; fuel allowance; generation asset optimization; market price uncertainties; multicommodity markets; power systems; qualifying facilities; risk constrained generation asset arbitrage; scenario reduction techniques; stochastic price based unit commitment; unfulfilled profit; Contracts; Costs; Fuels; Marketing and sales; Power generation; Power systems; Risk management; Spinning; Stochastic processes; Uncertainty; Ancillary services; arbitrage; bilateral contracts; emission allowance; energy; fuel; mixed-integer programming; qualifying facilities; risk management; stochastic price-based unit commitment;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2007.901753