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

Muralikrishnan, N.; Jebaraj, Luke; Rajan, C.

doi:10.1109/access.2020.3010275

Cited by 37 publications

(20 citation statements)

References 244 publications

(201 reference statements)

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“…Unit commitment constitutes a very well-known task in electricity industries and provides the ability to save a lot of money on an annual basis by making use of exact mathematical and/or (meta)heuristic mechanisms. Although it possesses a non-convex, multi-variate, mixed-integer and extremely non-linear objective, optimal UC schedules can lower the total production cost in terms of fuel avoidance costs and other expenses [25,26].…”

Section: Problem Formulation and Methodologymentioning

confidence: 99%

Increasing the Photovoltaic Hosting Capacity in Autonomous Grids and Microgrids via Enhanced Priority-List Schemes and Storage

Nikolaidis

Poullikkas

2021

Green Energy Sustain

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European Union has seen a rapid increase in renewable energy sources during the last decade. The variability and uncertainty caused by the increased penetrations of renewable generation must be properly considered in day-ahead unit commitment to retain the stable operation of conventional power plants. In this work, we present an enhanced method to determine the hosting capacity of photovoltaic energy in an autonomous grid. Based on optimal unit commitment schedules derived from priority list-schemes, we examine the potential of increasing the hosting capacity performing annual simulations for different scenarios in the presence of electricity storage. According to the obtained results, the application of storage eliminates the reliability expenses of load shedding and spinning reserve deficits. Hence, the actual hosting capacity is appropriately retrieved based on the renewable generation curtailment during each case study. However, sustainable solutions are achieved at higher penetration levels, reaching a near of 20% with respect to photovoltaic systems. The proposed solution could be efficiently utilized to determine the photovoltaic hosting capacity of microgrids in islanded or interconnected mode.

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Section: Problem Formulation and Methodologymentioning

confidence: 99%

Increasing the Photovoltaic Hosting Capacity in Autonomous Grids and Microgrids via Enhanced Priority-List Schemes and Storage

Nikolaidis

Poullikkas

2021

Green Energy Sustain

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“…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%

“…The unit commitment is a widely studied optimization problem, and, as a consequence, there are several good reviews on this topic. Recent publications are [100,[169][170][171][172][173][174][175]. There, comparisons about optimization techniques [169,170], uncertainty representation [171,172], and resolution techniques [100,173,174] are presented.…”

Section: Precise Description Of the Modeling Detail Adopted In The Li...mentioning

confidence: 99%

“…Recent publications are [100,[169][170][171][172][173][174][175]. There, comparisons about optimization techniques [169,170], uncertainty representation [171,172], and resolution techniques [100,173,174] are presented. However, advantages and disadvantages are usually exposed from a general perspective, as shown in Tables 1 and 2.…”

Section: Precise Description Of the Modeling Detail Adopted In The Li...mentioning

confidence: 99%

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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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“…A total power demand of 10.5GW is assumed for the given system. The real power generation output and fuel cost are calculated for the 40-unit test system by various methods, such as HGAFSA, GWO, OGWO [28], SDE [31], ORC-CRO, quasi-oppositional teaching-learning-based optimization (QOTLBO) [35], hybrid ant colony-genetic algorithm (GAAPI) [36], and krill herd algorithm (KHA) [37], and are tabulated in Table 4. The HGAFSA appraoch results in a reduced fuel cost (136,396.9 $/h) and minimum power loss (957.29 MW) compared to the approaches mentioned in Table 4.…”

Section: Test Systemmentioning

confidence: 99%

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

et al. 2021

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Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented.

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

Cited by 37 publications

References 244 publications

Increasing the Photovoltaic Hosting Capacity in Autonomous Grids and Microgrids via Enhanced Priority-List Schemes and Storage

Increasing the Photovoltaic Hosting Capacity in Autonomous Grids and Microgrids via Enhanced Priority-List Schemes and Storage

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

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

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