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Security Constrained Economic Dispatch: A Markov Decision Process Approach with Embedded Stochastic Programming

Security Constrained Economic Dispatch: A Markov Decision Process Approach with Embedded Stochastic Programming

Lizhi Wang, Nan Kong
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 16
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781609604530|DOI: 10.4018/joris.2010040101
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MLA

Wang, Lizhi, and Nan Kong. "Security Constrained Economic Dispatch: A Markov Decision Process Approach with Embedded Stochastic Programming." IJORIS vol.1, no.2 2010: pp.1-16. http://doi.org/10.4018/joris.2010040101

APA

Wang, L. & Kong, N. (2010). Security Constrained Economic Dispatch: A Markov Decision Process Approach with Embedded Stochastic Programming. International Journal of Operations Research and Information Systems (IJORIS), 1(2), 1-16. http://doi.org/10.4018/joris.2010040101

Chicago

Wang, Lizhi, and Nan Kong. "Security Constrained Economic Dispatch: A Markov Decision Process Approach with Embedded Stochastic Programming," International Journal of Operations Research and Information Systems (IJORIS) 1, no.2: 1-16. http://doi.org/10.4018/joris.2010040101

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Abstract

The main objective of electric power dispatch is to provide electricity to the customers at low cost and high reliability. Transmission line failures constitute a great threat to the electric power system security. We use a Markov decision process (MDP) approach to model the sequential dispatch decision making process where demand level and transmission line availability change from hour to hour. The action space is defined by the electricity network constraints. Risk of the power system is the loss of transmission lines, which could cause involuntary load shedding or cascading failures. The objective of the model is to minimize the expected long-term discounted cost (including generation, load shedding, and cascading failure costs). Policy iteration can be used to solve this model. At the policy improvement step, a stochastic mixed integer linear program is solved to obtain the optimal action. We use a PJM network example to demonstrate the effectiveness of our approach.

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