Advanced Method for Unit Commitment Problem Using Genetic Algorithm and Mathematical Programming

Takata, T.; Takahashi, Junichi; Yokoi, Hiroomi; Nakano, Hiroshi; Aoyagi, Mari; Kato, Masakazu; Shimada, K.; Arai, Junichi

doi:10.1541/ieejpes1990.119.3_333

Cited by 3 publications

(3 citation statements)

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“…The Lagrangian relaxation method solves the unit commitment problem approximately by solving dual problem (8). This is done by iterating the following two steps: (1) solve Eq.…”

Section: Lagrangian Relaxation Methodsmentioning

confidence: 99%

“…Step 2 can be solved effectively by dynamic programming [1,2,8], where the minimum up-and down-times in Eq. (5) are bundled in the transition diagram (see Appendix 1).…”

Section: Lagrangian Relaxation Methodsmentioning

confidence: 99%

“…8. We assumed that the fuel cost functions were quadratic and that the start-up costs were affine functions of the down-time…”

Section: Test Datamentioning

confidence: 99%

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Cutting plane methods for Lagrangian relaxation‐based unit commitment algorithm

Murai

Kato

2002

Electrical Engineering Japan

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SUMMARYIn this paper, we study cutting plane methods for a Lagrangian relaxation-based unit commitment algorithm. In the algorithm, nondifferentiable optimization methods can be applied to optimize the dual function, and a subgradient method which needs parameter tuning and has some drawbacks such as computational inefficiency and oscillating behavior is commonly used. The cutting plane method and the central cutting plane method are applied to the algorithm and implemented using reoptimization techniques. A numerical example shows that both methods are accelerated by the reoptimization techniques and have good convergence without parameter tuning.

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“…The Lagrangian relaxation method solves the unit commitment problem approximately by solving dual problem (8). This is done by iterating the following two steps: (1) solve Eq.…”

Section: Lagrangian Relaxation Methodsmentioning

confidence: 99%

“…Step 2 can be solved effectively by dynamic programming [1,2,8], where the minimum up-and down-times in Eq. (5) are bundled in the transition diagram (see Appendix 1).…”

Section: Lagrangian Relaxation Methodsmentioning

confidence: 99%