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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.