Balance Programming between Target and Chance with Application in Building Optimal Bidding Strategies for Generation Companies

Abstract

Stochastic problems existing in many research domains could be solved through three kinds of methods viz. expected value model (EVM), chance-constrained programming (CCP), and dependent chance programming (DCP). However, these methods, sometimes, give different or even contrary results when dealing with the same real world problems. This paper proposes a new stochastic programming method, termed as balance programming between target and chance, based on the concept of effective decision frontier curve, which can solve the stochastic problems in a more rational, flexible, and applicable manner, and can diminish conflicts of the three above-mentioned methods. The effectiveness of the proposed method is demonstrated by building optimal bidding strategies for generation companies with risk management in the electricity market environment. A genetic algorithm with Monte Carlo simulation is employed to solve the programming model.