A novel closed‐loop reconfiguration for radial distribution systems with dispersed generation

Wu, Ruay‐Nan; Hsieh, Tsung-Huang

doi:10.1080/02533839.2005.9671010

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2006

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“…These methods include Monte-Carlo simulation (Thalassinakis and Dialynas, 2004), Lagrangian relaxing (Gooi et al, 1999), and Bender's decomposition (Moya, 2004). Recently, with the appearance of artificial and computational intelligence technologies (Juang and Lu, 2005;Wu and Hsieh, 2005), such as genetic algorithms (Xu et al, 2003), and simulated annealing (Mantawy et al, 1999), these have also been applied to solving the spinning reserve scheduling problem. The ant colony system (ACS) (Dorigo and Gambardella, 1997;Dorigo et al, 1996) intelligence technologies.…”

Section: Introductionmentioning

confidence: 99%

Spinning reserve scheduling of power systems: An ant colony approach

Lee¹

2006

Journal of the Chinese Institute of Engineers

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This paper presents an Ant Colony System (ACS) based optimization approach to solve the optimal spinning reserve problem for a power system. Unit commitment risk is used as an index to evaluate the level of spinning reserve. The outage cost and the fuel cost of thermal units were considered in the unit commitment program. The optimal spinning reserve and unit commitment risk were reached while the sum of fuel cost and outage cost was minimized.A 52 units power system was used as a numerical example to test the new algorithm. The optimal spinning reserve and unit commitment risk could be reached in the testing while satisfying the requirement of the objective function.

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Section: Introductionmentioning

confidence: 99%

Spinning reserve scheduling of power systems: An ant colony approach

Lee¹

2006

Journal of the Chinese Institute of Engineers

View full text Add to dashboard Cite

show abstract

A novel closed‐loop reconfiguration for radial distribution systems with dispersed generation

Cited by 1 publication

References 11 publications

Spinning reserve scheduling of power systems: An ant colony approach

Spinning reserve scheduling of power systems: An ant colony approach

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