Backward reduction application for minimizing wind power scenarios in stochastic programming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Citations (Scopus)

Abstract

In order to make informed decisions in the presence of uncertainties, risk management problems of power utilities may be modelled by multistage stochastic programs. These programs use a set of scenarios (or plausible realizations) and corresponding probabilities to model the multivariate random data process, e.g. electrical load, stream flows to hydro units, generation output of intermittent renewable sources as well as fuel and electricity prices. The number of scenarios needed to accurately represent the uncertainty involved is generally large, thus due to computational complexity and time limitation, scenario reduction techniques are often utilized. The paper proposes a new application for recursive backward scenario reduction to establish possible next-day scenarios for wind power generation at Mersing Johor, Malaysia. The algorithm determines a subset from the initial scenario set and assigns new probabilities to the preserved scenarios. The output is intended to assist generation scheduling of power system employing intermittent type renewable sources.

Original languageEnglish
Title of host publicationPEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts
Pages430-434
Number of pages5
DOIs
Publication statusPublished - 2010
Event4th International Power Engineering and Optimization Conference, PEOCO 2010 - Shah Alam, Malaysia
Duration: 23 Jun 201024 Jun 2010

Other

Other4th International Power Engineering and Optimization Conference, PEOCO 2010
CountryMalaysia
CityShah Alam
Period23/06/1024/06/10

Fingerprint

Stochastic programming
Wind Power
Stochastic Programming
Set theory
Wind power
Scenarios
Stream flow
Risk management
Power generation
Computational complexity
Electricity
Scheduling
Uncertainty
Malaysia
Output
Risk Management
Power System
Assign
Computational Complexity
Unit

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Control and Optimization

Cite this

Muhamad Razali, N. M., & Hashim, A. H. (2010). Backward reduction application for minimizing wind power scenarios in stochastic programming. In PEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts (pp. 430-434). [5559252] https://doi.org/10.1109/PEOCO.2010.5559252
Muhamad Razali, Noor Miza ; Hashim, A. H. / Backward reduction application for minimizing wind power scenarios in stochastic programming. PEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts. 2010. pp. 430-434
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Muhamad Razali, NM & Hashim, AH 2010, Backward reduction application for minimizing wind power scenarios in stochastic programming. in PEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts., 5559252, pp. 430-434, 4th International Power Engineering and Optimization Conference, PEOCO 2010, Shah Alam, Malaysia, 23/06/10. https://doi.org/10.1109/PEOCO.2010.5559252

Backward reduction application for minimizing wind power scenarios in stochastic programming. / Muhamad Razali, Noor Miza; Hashim, A. H.

PEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts. 2010. p. 430-434 5559252.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In order to make informed decisions in the presence of uncertainties, risk management problems of power utilities may be modelled by multistage stochastic programs. These programs use a set of scenarios (or plausible realizations) and corresponding probabilities to model the multivariate random data process, e.g. electrical load, stream flows to hydro units, generation output of intermittent renewable sources as well as fuel and electricity prices. The number of scenarios needed to accurately represent the uncertainty involved is generally large, thus due to computational complexity and time limitation, scenario reduction techniques are often utilized. The paper proposes a new application for recursive backward scenario reduction to establish possible next-day scenarios for wind power generation at Mersing Johor, Malaysia. The algorithm determines a subset from the initial scenario set and assigns new probabilities to the preserved scenarios. The output is intended to assist generation scheduling of power system employing intermittent type renewable sources.

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Muhamad Razali NM, Hashim AH. Backward reduction application for minimizing wind power scenarios in stochastic programming. In PEOCO 2010 - 4th International Power Engineering and Optimization Conference, Program and Abstracts. 2010. p. 430-434. 5559252 https://doi.org/10.1109/PEOCO.2010.5559252