Simulating hydropower reservoir operations of the Yamula Dam with machine learning

Year 2023, Volume: 12 Issue: 4, 1426 - 1435, 15.10.2023

Mustafa Şahin Doğan

https://doi.org/10.28948/ngumuh.1314793

Abstract

Large-scale reservoirs provide operational flexibility to water managers by storing water during times with higher surface water availability and releasing water when it is most needed. Most large-scale reservoirs serve for multipurpose demands, such as water supply for agricultural, urban and environmental users, hydropower, recreation, fisheries and transportation. Due to its low operating cost, hydropower generation is often maximized in energy systems with mixed hydro and thermal sources. Hydropower generation is also used to meet peak demand by advantage of operating in short notice. This study aims to simulate reservoir operations, including release schedule and hydropower operations of the Yamula Dam and hydropower plant using machine learning. Located on the Kızılırmak River, the Yamula Dam is a large-scale multipurpose reservoir with its 3476 million cubic meters of storage capacity. Turbine release decisions are learned with Random Forests algorithm using only reservoir inflow and upstream streamflow conditions. The developed model successfully predicts reservoir releases between 2006 and 2015, with a coefficient of determination value of 0.87. Model prediction results are provided, and then hydropower load, generation and revenue are calculated and results are presented. Based on simulation results, the Yamula Dam generates about 362.3 gigawatts hour of energy per year, with an annual average revenue of 14.1 million Dollars. With the developed model, reservoir operations under different upstream hydrological conditions can also be simulated.

Keywords

reservoir operations, water management, hydropower, machine learning, random forests

Yamula Barajının hidroelektrik rezervuar işletiminin makine öğrenimi ile simülasyonu

Abstract

Büyük depolama kapasiteli rezervuarlar yüzey suyunun fazlaca bulunduğu zamanlarda suyu depolayarak ve su ihtiyacının en yüksek olduğu zamanlarda bu depolanan suyu sisteme vererek suyu yönetenlere işletim esnekliği sağlar. Büyük kapasiteli rezervuarlar çoğunlukla tarımsal, kentsel ve çevresel su ihtiyaçlarının temini, hidroelektrik, rekreasyon, balıkçılık ve ulaşım gibi birden çok amaca hizmet ederler. Düşük işletim maliyetinden dolayı hidroelektrik üretimi, hidro ve termik karışık enerji sistemlerinde genellikle maksimize edilir. Hidroelektrik üretim kısa sürede işletime alınma avantajından dolayı pik saatlerdeki talebi karşılamak için de kullanılır. Bu çalışma Yamula Barajı ve hidroelektrik santralinin türbin akış zamanlaması ve hidroelektrik operasyonlarını içeren rezervuar işletimini makine öğrenimini kullanarak simüle etmeyi amaçlamaktadır. Kızılırmak Nehri üzerinde yer alan Yamula Barajı 3476 milyon metreküp depolama kapasitesiyle birden çok amaca hizmet eden büyük ölçekli bir barajdır. Rastgele Karar Ormanları algoritması ile sadece rezervuara giren akım ve memba akım koşullarına göre türbin akımı kararları öğrenilmiştir. Geliştirilen model 2006 ve 2015 yılları arasındaki türbin akımlarını, 0.87 korelasyon katsayısı ile, başarılı bir şekilde tahmin edebilmektedir. Model tahmini sonuçları gösterilmiş ve ayrıca hidroelektrik enerjisi üretimi ve getirisi hesaplanmış ve sonuçlar sunulmuştur. Simülasyon sonuçlarına göre Yamula barajı yılda yaklaşık 362.3 gigawatt saat enerji üretmekte ve 14.1 milyon dolar gelir sağlamaktadır. Geliştirilen model ile farklı memba hidrolojik durumlarına göre rezervuar işletim simülasyonları da yapılabilmektedir.

Keywords

rezervuar işletimi, su yönetimi, hidroelektrik, makine öğrenimi, rastgele karar ormanları

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