A Genetic Algorithm Based Optimal Sizing Strategy for PV/Battery/Hydrogen Hybrid System

Lazaar, Nouhaila; Fakhri, Eyman; Barakat, Mahmoud R.; Sabor, Jalal; Gualous, Hamid

doi:10.1007/978-3-030-53970-2_23

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…Higher reliability factors and economic benefits are considered to be important constraints for optimal battery sizing. In [18], a Genetic Algorithm (GA) was used for optimal sizing of the battery [18]. The main objectives of this research were to decrease the Net Present Cost (NPC) of the system and the consideration of the Equivalent Loss Factor for the index amount of reliability.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

et al. 2022

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Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.

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Section: Related Workmentioning

confidence: 99%

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

et al. 2022

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“…In research done by Nouhaila Lazaar, the Genetic Algorithm (GA) was used for optimal sizing of the battery [16]. The main objectives for this research done by Nouhaila was to decrease the Net Present Cost (NPC) of the system and the consideration of the Equivalent Loss Factor (ELF) for the index amount of reliability.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Khan¹,

Nizami²,

Qaisar³

et al. 2022

Preprint

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Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

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“…P j is the rated power of each component. In order to convert the replacement cost of a component at the end of its lifetime to present cost, K j is used; it can be calculated by (10) [25].…”

Section: A Proposed Objective Functionmentioning

confidence: 99%

“…PWA(. , R) is used to estimate the present value of maintenance and operation annual cost over the project lifetime, it is given by (11) [25].…”

Section: A Proposed Objective Functionmentioning

confidence: 99%

Optimal sizing of Marine Current Energy Based Hybrid Microgrid

Lazaar¹,

Fakhri²,

Barakat³

et al. 2020

REPQJ

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This paper presents an optimal sizing of a marine current energy-based hybrid microgrid. For this purpose, a particle swarm optimization (PSO) has been developed on Matlab software. An energy management strategy that takes into account the availability of renewable energy and energy consumption at all times has been implemented to meet demand throughout the year. In order to test the performance of the PSO, the results were compared to those obtained by the genetic algorithm (GA). The standard household load profile is selected to model an island residential loads variation. The Alderney Race (Raz-Blanchard in French) site is chosen as a case study for our marine energy system because its currents are among the highest in the world.

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A Genetic Algorithm Based Optimal Sizing Strategy for PV/Battery/Hydrogen Hybrid System

Cited by 5 publications

References 23 publications

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Optimal sizing of Marine Current Energy Based Hybrid Microgrid

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