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Journals / Karadeniz Fen Bilimleri Dergisi / 2022 / Cilt: 12 - Sayı: 2

Energy Management and Optimization of a Hybrid Energy System by Particle Swarm Optimizing Algorithm-Genetic Algorithm and Gray Wolf Optimizing Algorithm Technique: A case study for Yalova University

Bir Hibrit Enerji Sisteminin Parçacık Sürüsü Optimizasyon Algoritması- Genetik Algoritma ve Gri Kurt Optimizasyon Algoritma Tekniği ile Enerji Yönetimi ve Optimizasyonu: Yalova Üniversitesi için bir vaka çalışması

Aykut Fatih GÜVEN
Nuran YÖRÜKEREN
Pages
853–879
Publication Date
2022-12-15
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results clearly show that the scheme that is proposed in this study can achieve a smooth flow of power using the identical optimal configuration.

📌 3 Key Takeaways

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Objective & Hypothesis:This article presents a detailed feasibility investigation of a Hybrid Renewable Energy System (HRES) designed to meet the energy needs of a university campus.
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Key Findings & Data:An energy management strategy is presented in a way to minimize the Levelized Cost of Energy and Total Net Present Cost, and it is thought that the Loss of Power Supply Probability validates the reliability of the operation.
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Conclusion & Impact:The results clearly show that the scheme that is proposed in this study can achieve a smooth flow of power using the identical optimal configuration.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Enerji yönetimi · Algoritmalar · Optimal planlama

Abstract

This article presents a detailed feasibility investigation of a Hybrid Renewable Energy System (HRES) designed to meet the energy needs of a university campus. The HRES includes Wind Turbine (WT), Photovoltaic (PV), Diesel Generator, Battery, and inverter components. Based on the constraint of power balance, different optimization techniques are applied to reduce the Annual Cost of the System and determine the optimum WT power, PV panel power, and number of batteries. An energy management strategy is presented in a way to minimize the Levelized Cost of Energy and Total Net Present Cost, and it is thought that the Loss of Power Supply Probability validates the reliability of the operation. To find the optimum sizing of the components, results are obtained using the HOMER and MATLAB software. The Genetic Algorithm (GA) outperforms during the simulation process, delivering quick and dependable results. Using GA in the best system configuration, 3.407975x103 kW PV, 50 kW WT and 951.5493 kW Battery, $3.9808 x105 annual system cost (ACS), $6.4580 x106 net present cost (NPC), and $0.1998/kWh, respectively. Solar panels cover the entire system, and the Renewable Energy Fraction (REF) is 100%. The results clearly show that the scheme that is proposed in this study can achieve a smooth flow of power using the identical optimal configuration.

Özet

Bu makale, bir üniversite kampüsünün enerji ihtiyacını karşılamak üzere tasarlanmış bir Hibrit Yenilenebilir Enerji Sisteminin (HRES) detaylı bir fizibilite araştırmasını sunmaktadır. HRES, Rüzgar Türbini (WT), Fotovoltaik (PV), Dizel Jeneratör, Batarya ve invertör bileşenlerini içerir. Güç dengesi kısıtlamasına bağlı olarak, Sistemin Yıllık Maliyetini azaltmak ve optimum WT gücünü, PV panel gücünü ve pil sayısını belirlemek için farklı optimizasyon teknikleri uygulanır. Seviyelendirilmiş Enerji Maliyeti ve Toplam Net Bugünkü Maliyeti en aza indirecek şekilde bir enerji yönetimi stratejisi sunulmakta ve Güç Kaynağı Kaybı Olasılığının operasyonun güvenilirliğini doğruladığı düşünülmektedir. Bileşenlerin optimum boyutlandırılmasını bulmak için HOMER ve MATLAB yazılımı kullanılarak sonuçlar elde edilir. Genetik Algoritma (GA), simülasyon sürecinde daha iyi performans göstererek hızlı ve güvenilir sonuçlar sunar. GA'yı en iyi sistem konfigürasyonunda kullanmak, sırasıyla 3.407975x103 kW PV, 50 kW WT ve 951.5493 kW Batarya, 3.9808$ x105 yıllık sistem maliyeti (ACS), 6.4580$ x106 net mevcut maliyet (NPC) ve 0.1998$/kWh. Güneş panelleri tüm sistemi kaplar ve Yenilenebilir Enerji Fraksiyonu (REF) %100'dür. Sonuçlar, bu çalışmada önerilen şemanın, aynı optimal konfigürasyonu kullanarak düzgün bir güç akışı sağlayabileceğini açıkça göstermektedir.

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Cite This Article

GÜVEN, A. F., YÖRÜKEREN, N. (2022). Energy Management and Optimization of a Hybrid Energy System by Particle Swarm Optimizing Algorithm-Genetic Algorithm and Gray Wolf Optimizing Algorithm Technique: A case study for Yalova University. Karadeniz Fen Bilimleri Dergisi, Cilt: 12 - Sayı: 2, 853–879. https://ajindex.com/articles/860741

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