On-line economic load dispatch based on fuel cost dynamics

Yoshikawa, Motonobu; Toshida, Norikazu; Nakajima, Hiroshi; Harada, Yasushi; Tsurugai, Mitsuo; Nakata, Yuji

doi:10.1109/59.574953

Cited by 17 publications

(3 citation statements)

References 3 publications

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“…Most of the research in this area however, has been on the optimal allocation of electrical generators rather than steam generation [3][4][5][6][7][8][9][10][11][12][13]. The research that has been done on power boilers in the utility industry has been on the modeling and control aspects [5][6][7][8][9][10] rather than the boiler allocation aspect. In [3] for instance, the authors find an operating level and fuel mixture ratio that minimizes fuel costs in a boiler-turbine generator combination by using the Simplex method to address certain types of nonlinear problems.…”

Section: E-mail Addressmentioning

confidence: 99%

Optimal load allocation of multiple fuel boilers

Dunn¹,

2009

ISA Transactions

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Section: E-mail Addressmentioning

confidence: 99%

Optimal load allocation of multiple fuel boilers

Dunn¹,

2009

ISA Transactions

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“…If in addition to linear constraints, the objective function is quadratic, the optimization problem is called quadratic programming (QP). This specific branch of NLP has also found widespread applications in the electric power field, in such areas as economic dispatch [46], [47], reactive power control [48]- [50], optimal power flow [48], [51], dc load flow [51], transmission system operation and planning [52], and unit commitment [53].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Valle

Venayagamoorthy

Mohagheghi

et al. 2008

IEEE Trans. Evol. Computat.

1,992

851

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Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.Index Terms-Classical optimization, particle swarm optimization (PSO), power systems applications, swarm intelligence.

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“…A literature survey is carried out for the chosen problem and it is elaborated as follows. Initially in the 20th century, different mathematical [1] and heuristic solution techniques such as iterative method [2], sorted table method [3], dedicated projection method [4], dynamic programming (DP) [5, 6], least‐squares method [7], Newton's method (NM) [8], evolutionary programming (EP) [9], quadratic programming [10] and composite cost function (CCF) [11] have been developed and applied successfully to ED problems. Recently in 21th century, meta‐heuristic and hybrid techniques such as DP – sequential and truncated combination [12], Hopfield neural network [13], partition approach algorithm (PAA) [14], pattern search (PS) algorithm [15] and λ ‐logic method [16] have been effectively applied to ED problems with cubic cost functions.…”

Section: Introductionmentioning

confidence: 99%