COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems

Garcia, Jesus Luis; Monroy, Raúl; Hernández, Víctor Adrián Sosa; Coello, Carlos A. Coello

doi:10.1016/j.swevo.2021.100983

Cited by 17 publications

(3 citation statements)

References 53 publications

(88 reference statements)

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“…Nevertheless, there are numerous measures for convergence and diversity preservation. The following papers go into detail on each one [76,79,80,82,83]. Among the metrics, hypervolume is used in this paper since, according to a thorough review by [83], it is the most frequently used in the literature.…”

Section: Multiobjective Optimization Approachesmentioning

confidence: 99%

Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method

Abubakar

Borkor

Amoako-Yirenkyi

2023

International Journal of Photoenergy

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Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid’s operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system’s operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid’s operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system.

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Section: Multiobjective Optimization Approachesmentioning

confidence: 99%

Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method

Abubakar

Borkor

Amoako-Yirenkyi

2023

International Journal of Photoenergy

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“…The evaluation of Multi-objective Evolutionary Algorithms (MOEAs) centers around their ability to converge, generate a diverse array of solutions, and the time they take to accomplish the task. A comprehensive examination of established performance measures can be found in existing literature, as documented in references such as [62,68,69,71,72].…”

Section: Performance Assessment With Hypervolume Metricmentioning

confidence: 99%

Stochastic Optimal Harmonic Suppression with Permissible Photovoltaic Penetration Level for Grid-Linked Systems using Monte Carlo-Based Hybrid NSGA2-MOPSO

Abubakar,

Borkor,

Amoako-Yirenkyi

2023

Preprint

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Integrating a high Penetration level of Solar Photovoltaic (SPV) power in the electricity grid could enhance the system’s sustainability, reliability, and affordability. Nevertheless, at higher Penetrations, the intermittency, non-dispatchable nature of the SPV generation, and the extensive utilization of inverter-based interfaces generate excessive harmonic distortions that damage power system devices and interrupt the smooth operation of the power system. Thus, the severity of the harmonic distortion impacts varies as a function of the degree of the SPV Penetration level in the grid-connected system. Though the problem is highly nonlinear stochastic programming with multiple conflicting power quality criteria, no existing study holistically captures the randomness, the contradictory nature of the objectives, and the grid’s technical limitations simultaneously. This study proposes a novel Monte-Carlo-based Hybrid multi-objective methodology to scale up the Photovoltaic Penetration level with a minimum Total Harmonic Distortion (THD) for multilevel SPV inverters in grid-connected systems without violating the system’s standard operational limitations. Six state-of-the-art Multi-Objective Evolutionary algorithms were implemented and compared using hypervolume indica- tor, execution time, and parametric statistical analysis to obtain a quality solution. The results showed that the Hybrid NSGAII-MOPSO outflanked the rest in terms of convergence, diversity, and execution time. It could be inferred that even under variable weather conditions, this harmonic suppression design approach could accurately optimize the SPV Penetration level and mitigate the THD without degrading the grid’s standard operational constraints. In comparison, the stochastic design technique creates a far more reliable SPV grid-connected system than the deterministic approach.

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“…e performance of multi-objective EAs (MOEAs) is evaluated using the following criteria [69,73]: convergence, diversity preservation, and actual execution times. Many metrics exist and detailed discussions on them can be found in the following studies [66,69,70,[73][74][75].…”

Section: Performance Assessment With Hypervolume Metricmentioning

confidence: 99%

Stochastic Optimal Design of Household-Based Hybrid Energy Supply Systems Using Sample Average Approximation

Abubakar

Borkor

Amoako-Yirenkyi

2022

Mathematical Problems in Engineering

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In terms of energy production, combining conventional and renewable energy sources prove to be more sustainable and cost-effective. Nevertheless, efficient planning and designing of such systems are extremely complex due to the intermittency of renewable sources. Many existing studies fail to capture the stochasticity and/or avoid detailed reliability analysis. This research proposes a practical stochastic multi-objective optimization tool for optimally laying out and sizing the components of a grid-linked system to optimize system power at a low cost. A comparative analysis of four state-of-the-art algorithms using the hypervolume measure, execution time, and nonparametric statistical analysis revealed that the nondominated sorting genetic algorithm III (NSGA-III) was more promising, despite its significantly longer execution time. According to the NSGA-III calculations, given solar irradiance and energy profiles, the household would need to install a 5.5 (kWh) solar panel tilted at 26.3° and orientated at 0.52° to produce 65.6 (kWh) of power. The best battery size needed to store enough excess power to improve reliability was 2.3 (kWh). The cost for the design was $73520. In comparison, the stochastic technique allows for the construction of a grid-linked system that is far more cost-effective and reliable.

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COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems

Cited by 17 publications

References 53 publications

Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method

Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method

Stochastic Optimal Harmonic Suppression with Permissible Photovoltaic Penetration Level for Grid-Linked Systems using Monte Carlo-Based Hybrid NSGA2-MOPSO

Stochastic Optimal Design of Household-Based Hybrid Energy Supply Systems Using Sample Average Approximation

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