Adaptive bacterial foraging and genetic algorithm for unit commitment problem with ramp rate constraint

Elattar, Ehab E.

doi:10.1002/etep.2164

Cited by 3 publications

(4 citation statements)

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“…In turn, these algorithms can be combined with each other to achieve a better performance, or even with commercial solvers if the problem is divided into multiple resolution stages. Some examples in the unit commitment literature are in [95][96][97][98][99]. Furthermore, an excellent state-of-art on EO is presented in [100].…”

Section: Optimization Algorithmsmentioning

confidence: 99%

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Montero

Bello

2022

Energies

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Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most.

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Section: Optimization Algorithmsmentioning

confidence: 99%

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Montero

Bello

2022

Energies

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“…In the fourth case, the combination of two variants is implemented, to further amplify the performance of this proposed approach. A hybrid adaptive bacterial foraging, with GA (HABFGA) method was examined by Elattar [233], with the combination of BFA and GA, with adaptive stopping criterion.…”

Section: Uc Problem Incorporated With Hybrid Evolutionary Optimizatio...mentioning

confidence: 99%

A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

Muralikrishnan¹,

Jebaraj²,

Rajan

2020

IEEE Access

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Unit Commitment (UC) is a key task in electric power system operation, aiming at minimizing the total cost of power generation. It is essential to monitor wide range of activities and practices of UC necessary to determine the operating plan of generating units. The UC problem is particularly crucial, when the behavior of loads at every hour interval, is oscillatory and with different operational constraints and environments. Many works have been proposed, with different optimization methods to solve the UC problem. This paper gives a detailed review of the evolutionary optimization techniques, employed for solving UC problem, by collecting them from lots of peer reviewed published papers. This review was carried out under many sections, based on various evolutionary optimization techniques, to help new researchers, dealing with modern UC problem solutions, under different situations of power system.

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“…The classical UC problem aims to minimize the total cost of system while selecting generators availability schedule, satisfying load demand, ramping, and other constraints over given time horizon as follows:

italicMin C_{total} = \sum_{t = 1}^{T} \sum_{n = 1}^{{normalN}_{normalG}} ({}, f_{n} ((), P_{((), n, t)}^{normalG}) U_{(n, t)} + {SU}_{(n, t)} + {SD}_{(n, t)}),

\begin{matrix} true \\ U_{((), n, t)} = 1, if {false\sum}_{h = t - {italicup}_{n}}^{t - 1} U_{((), n, h)} < {italicMUT}_{n}, \\ U_{((), n, t)} = 1, if {false\sum}_{h = t - {italicdown}_{n}}^{t - 1} (1 - U_{((), n, h)}) < {italicMDT}_{n} . \end{matrix}

For the total period of M intervals and N units in UC, the maximum number of possible combinations is 2 N × M .…”

Section: Description Of C‐scucmentioning

confidence: 99%

Section: Security Constrained Unit Commitment (Scuc) Problemmentioning

confidence: 99%

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

Nawaz

Wang

et al. 2019

Int Trans Electr Energ Syst

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Summary Modern power system requires optimally coordinated operation between transmission and distribution systems due to large‐scale integration of distributed energy resources (DERs). Commonly, transmission system operator (TSO) solves its own cost optimization problem and evaluates required targets for each distribution system operator (DSO). Thus, TSO problem becomes more complex and highly scaled with increase in number of connected distribution systems, which requires more computational time and resources. Therefore, this paper proposed a method to reduce computational burden on TSO and solve coordinated security constrained unit commitment (C‐SCUC) problem by involving a coordinator in upper level hierarchy. Coordinator is required to solve system‐wide problem to adjust targets for transmission system (TS) and all distribution systems (DS). Besides, TSO and DSO only need to solve their own cost optimization problems. In this paper, we have proposed analytical target cascading (ATC) along with hybridized particle swarm optimization with differential evolution (PSO‐DE) for solving increased complexity of the coordinated problem. ATC provides coordination between transmission and distribution sections of power system while hybrid PSO‐DE provides better accuracy of optimization in ATC layers. For evaluation of efficacy of proposed method, this paper has taken IEEE 118‐bus system as TS and IEEE 33‐bus system as DS. Results has depicted that proposed method could help TSO in reducing computational resources and improving computational time.

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Adaptive bacterial foraging and genetic algorithm for unit commitment problem with ramp rate constraint

Cited by 3 publications

References 55 publications

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

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