TY - GEN
T1 - Balance programming between target and chance with application in building optimal bidding strategies for generation companies
AU - Lu, Gang
AU - Wen, Fushuan
AU - Zhao, Xueshun
AU - Chung, C. Y.
AU - Wong, K. P.
PY - 2008/1
Y1 - 2008/1
N2 - 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.
AB - 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.
KW - Balance programming between target and chance
KW - Bidding strategies
KW - Chance-constrained programming
KW - Dependent chance programming
KW - Expected value model
UR - https://www.scopus.com/pages/publications/50249177172
U2 - 10.1109/ISAP.2007.4441639
DO - 10.1109/ISAP.2007.4441639
M3 - Conference article published in proceeding or book
AN - SCOPUS:50249177172
SN - 9860130868
SN - 9789860130867
T3 - 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP
BT - 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP
ER -