By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

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.

Published in:

Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference on

Date of Conference:

5-8 Nov. 2007