A Genetic Algorithm solution to the Unit Commitment problem based on real-coded chromosomes and Fuzzy Optimization

Ademovic, Alma; Bisanovic, Smajo; Hajro, Mensur

doi:10.1109/melcon.2010.5476238

“…Genetic Algorithm (GA) is a parallel search based optimization technique modelled after the biological theories of natural selection and genetic recombination [22]. Over the past decade, there have been vast improvements in the performance of the method [23].…”

Section: Review Of the State Of The Artmentioning

confidence: 99%

“…are defined (such as initialize population, copy, mutation, crossover, shift, intelligent mutation, and evaluation) [22] [23] and used throughout the process. A unique aspect of GA is the use of the mutation operator that is able to identify local minima and prevent the algorithm from getting stuck [22].…”

Section: Review Of the State Of The Artmentioning

confidence: 99%

Unit commitment using complementarity

Craig¹

2021

Preprint

0

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A need exists to optimally dispatch power generation to meet per-hour requirements on the power grid. This is a well documented and established problem called Unit Commitment (UC). It is commonly formulated as a Mixed Integer Linear Program (MILP), which utilizes intelligent solvers to produce a solution with speed and accuracy. The linear nature of MILP requires linear approximations of nonlinear constraints. This work introduces the Theory of Complementarity in order to remove integer variables, resulting in a continuous rather than a discontinuous solution space. This permits use of classical solution techniques, as well as nonlinear constraints, thereby increasing accuracy. A formulation is developed to demonstrate a proof of concept of the complementarity theory as used in UC. A subset of constraints will be used and the results will be compared against an MILP optimization, for 10-and 26-generator configurations. Similar trends in generator status and total cost are noted.

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“…A detailed example can be found in [22] or [23]. The algorithm begins with a randomized popula- Bender's Decomposition (BD) is a solution method that divides the problem into a master problem and subproblems.…”

Section: Review Of the State Of The Artmentioning

confidence: 99%

Unit commitment using complementarity

Craig¹

2021

Preprint

0

View full text Add to dashboard Cite

A need exists to optimally dispatch power generation to meet per-hour requirements on the power grid. This is a well documented and established problem called Unit Commitment (UC). It is commonly formulated as a Mixed Integer Linear Program (MILP), which utilizes intelligent solvers to produce a solution with speed and accuracy. The linear nature of MILP requires linear approximations of nonlinear constraints. This work introduces the Theory of Complementarity in order to remove integer variables, resulting in a continuous rather than a discontinuous solution space. This permits use of classical solution techniques, as well as nonlinear constraints, thereby increasing accuracy. A formulation is developed to demonstrate a proof of concept of the complementarity theory as used in UC. A subset of constraints will be used and the results will be compared against an MILP optimization, for 10-and 26-generator configurations. Similar trends in generator status and total cost are noted.

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“…The results demonstrate that this technique provides a low cost but slow solutions compared to ANN-TS. Alma et al [27] proposed a hybrid approach combining fuzzy logic and the genetic algorithm (FL-AG) to solve the Unit Commitment problem. Genetic algorithm uses the actual coded chromosomes in contradiction with the most commonly used scheme which is binary coding.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Investigation of Combined Use of PSO, Tabu Search and Lagrangian Relaxation methods to solve the Unit Commitment Problem

Marrouchi¹,

Amor²,

Hessine³

et al. 2018

Adv. sci. technol. eng. syst. j.

2

0

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Solving the Unit Commitment problem (UCP) optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed a new strategy based on the combination three optimization methods: Tabu search, Particle swarm optimization and Lagrangian relaxation methods in order to develop a proper unit commitment scheduling of the production units while reducing the production cost during a definite period. The proposed strategy has been implemented on a the IEEE 9 bus test system containing 3 production unit and the results were promising compared to strategies based on metaheuristic and deterministic methods.

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A Genetic Algorithm solution to the Unit Commitment problem based on real-coded chromosomes and Fuzzy Optimization

Cited by 17 publications

References 10 publications

Unit commitment using complementarity

Unit commitment using complementarity

Unit commitment using complementarity

Theoretical Investigation of Combined Use of PSO, Tabu Search and Lagrangian Relaxation methods to solve the Unit Commitment Problem

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