NSGA-II and MOPSO based optimization for sizing of hybrid PV/wind/battery energy storage system

Hlal, Mohamed Izdin; Ramachandaramurthya, Vigna K.; Padmanaban, Sanjeevikumar; Kaboli, Hamid Reza; Pouryekta, Aref; Abdullah, Tuan Ab Rashid Bin Tuan

doi:10.11591/ijpeds.v10.i1.pp463-478

Cited by 32 publications

(39 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The justification for selecting these three algorithms is due to their wide applicability and readily to be implemented in Matlab. These algorithms also have been proven to deliver good optimization accuracy, particularly in battery storage application in renewable energy sources [7,12,13,[21][22][23].…”

Section: Optimization Of Battery Parametersmentioning

confidence: 99%

Accurate battery model parameter identification using heuristic optimization

Jusoh¹,

Daud²

2020

IJPEDS

View full text Add to dashboard Cite

<span lang="EN-MY">This paper presents an accurate Lithium-ion battery model representation in Matlab/Simulink. The Tremblay's battery model was used as a BES model platform, where the determination of the model parameters was obtained based on heuristic optimization approach. This approach is simple but more accurate compared to the conventional method. In the classical method, it requires the user to manually select the battery model parameters from relevant points on the manufacturer discharge curves. However, this way of battery parameters extraction normally exposed to the human error and would easily result in an inaccurate selection of battery parameters for the BES simulation studies. Therefore, an easy and accurate approach using heuristic optimization for determining battery model parameters was introduced. The simulation studies utilized three different optimization algorithms for comparison purposes, i.e. 1) Particle Swarm Optimization (PSO), 2) Gravitational Search Algorithm (GSA), and 3) Genetic Algorithm (GA). The performance of BES model discharge accuracy with respect to the test data from three different algorithms was compared and the results showed that the GA approach gives the best results in terms of accuracy and execution time. </span><span lang="EN-MY">Finally, the validated results of GA-optimized battery model showed the accuracy of 98% compared to the conventional approach.</span>

show abstract

Section: Optimization Of Battery Parametersmentioning

confidence: 99%

Accurate battery model parameter identification using heuristic optimization

Jusoh¹,

Daud²

2020

IJPEDS

View full text Add to dashboard Cite

show abstract

“…A multi-objective approach for placement of multiple DGs in the radial distribution system is presented in [28]. Reference [29] stated that NSGA-II and MOPSO are the modern, random optimization methods that are able to find the Pareto frontier. Multi-objective PSO (MOPSO) was used for a distributed energy system integrated with energy storage in [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Sub-Transmission Network Expansion Planning Considering Regional Energy Systems: A Bi-Level Approach

2019

View full text Add to dashboard Cite

In order to facilitate the transformation of the existing generation and transmission networks’ structure into a scalable and competitive grid structure, this paper introduced regional energy systems (RGESs) that have the role of aggregating distributed energy resources (DERs) and flexible loads. The economic justification for the expansion of sub-transmission networks in the presence of regional energy systems was also investigated. To achieve this goal, multi-criteria optimization solutions were employed to find techno-economic solutions. While solving the proposed multi-criteria optimization problem, a Pareto front was determined to show the tradeoff between the criteria examined. In addition, fuzzy satisfying and the max-min method were used for finding equilibrium point. In order to demonstrate the performance and effectiveness of the proposed model, a realistic sub-transmission system in Guilan Province, Iran, was used as a test system and the results were compared to those from a traditional sub-transmission expansion planning model.

show abstract

“…us, they used a method that is to interfere and work with feasible solutions throughout the optimization. Hlal et al [8] conducted an optimization study based on NSGA-II and MOPSO for sizing a hybrid PV/wind/battery energy storage system. Kıran et al [9] applied artificial bee colony (ABC) and particle swarm optimization (PSO) techniques to forecast electricity energy demand in Turkey in two forms (linear and quadratic) by using selected socioeconomic and demographic variables that included gross domestic product (GDP), population, import, and export.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

Bahmani

Nejati

GhasemiNejad

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.

show abstract

NSGA-II and MOPSO based optimization for sizing of hybrid PV/wind/battery energy storage system

Cited by 32 publications

References 30 publications

Accurate battery model parameter identification using heuristic optimization

Accurate battery model parameter identification using heuristic optimization

Sub-Transmission Network Expansion Planning Considering Regional Energy Systems: A Bi-Level Approach

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

Contact Info

Product

Resources

About