Mountain gazelle optimizer for standalone hybrid power system design incorporating a type of incentive-based strategies

The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating d...

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Vydané v:Neural computing & applications Ročník 36; číslo 12; s. 6839 - 6853
Hlavní autori: Abdelsattar, Montaser, Mesalam, Abdelgayed, Fawzi, Abdelrahman, Hamdan, I.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.04.2024
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Carbon dioxide
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Demand side management
Design optimization
Diesel engines
Electricity
Emissions
Energy consumption
Hybrid systems
Image Processing and Computer Vision
Load fluctuation
Mathematical models
Mountains
Optimization algorithms
Optimization techniques
Original Article
Performance enhancement
Photovoltaic cells
Pollutants
Power supply
Probability and Statistics in Computer Science
Profitability
Radiation
Sizing
Systems design
Temperature
Wind turbines
Mountain gazelle optimizer
Standalone hybrid power system
Multi objectives optimization
Optimal design
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí:The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO 2 ) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $.
Bibliografia:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-09433-3