Assessment of the Productive Efficiency of Large Wind Farms in the United States: An Application of Two-Stage Data Envelopment Analysis

Sağlam, Ümit

doi:10.2139/ssrn.3018845

Cited by 7 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Geleneksel enerji kaynakları ile karşılaştırıldığında yenilenebilir enerji (Popp, 2012;Bulavskaya ve Reynès, 2018); güneş enerjisi (Kabalcı, 2013;Chi ve diğerleri, 2018), rüzgâr enerjisi (Cheng ve Zhu, 2014;Miguez ve diğerleri, 2006;Sağlam, 2017), hidroelektrik enerji (Sipahutar ve diğerleri, 2013;Robertson ve diğerleri, 2020), jeotermal enerji (Fridleifsson, 2001) ve bio-malzeme enerjisi (Dias ve diğerleri, 2009;Chang ve diğerleri, 2003) olarak daha çekici görünmektedirler. Çünkü bu tür enerjiler yeşil, temiz, sürdürülebilir ve çevre dostu olmanın yanında hem enerji alanında dışa bağımlılığı azaltacak hem de enerji verimliliğini artıracak olan enerjilerdir (Türkmen ve diğerleri, 2018).…”

Section: Enerji Verimliliğini Artıran Arz Yönlü Yapısal Faktörler 211 Yenilenebilir Enerjiunclassified

An Overview on Energy Efficiency in View of Supply and Demand

NAİMOĞLU

Akal

2021

Verimlilik Dergisi

View full text Add to dashboard Cite

Bu çalışma, enerji verimliliğinin faydalarını ve enerji verimliliğinin tam potansiyelinin nasıl gerçekleştirilebileceğini arz ve talep yönlü anlamayı amaçlamaktadır. Yöntem: Bu çalışma hazırlanırken 1980-2021 döneminde JSTOR, ScienceDirect, Wiley, Taylor&Francis, Springer, SSRN ve DergiPark veri tabanlarında yayımlanmış makalelerle beraber birçok bilimsel bildiri ve raporlar için enerji verimliliği anahtar kelimesi kullanılmıştır. Enerjinin daha verimli kullanılmasına odaklanan 120'den fazla ilgili çalışmayla arz ve talep yönlü faktörler sunulmaktadır. Bulgular: Arz yanlı olarak literatürden elde edilen bulgulara göre hükümet tarafından alınan çevre odaklı enerji politikaları ödün verilmeden etkin ve başarılı bir şekilde uygulanarak, yenilenebilir enerji alanında yatırım ve teknolojik gelişmelerin artırılmasını sağlayarak ve enerji alanında teknolojik altyapı geliştirilerek enerji verimliliğinin artırılması sağlanacaktır. Talep yanlı literatürden elde edilen bulgulara göre ise devlet tarafından yapılacak olan teşvik, sübvansiyon ve vergi indirimleriyle tüketicilerin tasarruflu cihaz satın almaları sağlanabilecektir. Ayrıca enerji alanında verilecek eğitim ve reklamlar tüketicilere çevre ve enerji bilincinin sağlanmasıyla tüketici davranışlarına olumlu yansıması sağlanabilecektir. Özgünlük: Bu çalışmanın diğer çalışmalardan farkı, enerji verimliliğini artırmaya yönelik arz ve talep yönlü faktörleri belirleyerek, günümüz dünyasına ve gelecek nesillere daha temiz ve daha yaşanılabilir çevre oluşumuna katkı sağlayan farklı çalışmaları çok boyutlu olarak ele almasıdır.

show abstract

Section: Enerji Verimliliğini Artıran Arz Yönlü Yapısal Faktörler 211 Yenilenebilir Enerjiunclassified

An Overview on Energy Efficiency in View of Supply and Demand

NAİMOĞLU

Akal

2021

Verimlilik Dergisi

View full text Add to dashboard Cite

show abstract

“…He Y., Liu W. and Jiao J. applied the CCR-DEA model to analyze the efficiency of power grid projects [17]. Sağlam Ü. evaluated the efficiencies of the 236 large utility-scale wind farms using BCC models [18]. However, the traditional DEA model has some limitations.…”

Section: Introductionmentioning

confidence: 99%

Evaluating and Optimizing Transnational Grid Construction Based on Input-Output Analysis under the Background of Global Energy Interconnection

Jing

2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

In order to integrate the evaluation and optimization of transnational power grid projects and to quantify the adjustment of energy interconnection schemes, this paper improved the Min DS (minimum distance to strong efficiency frontier) DEA (data envelopment analysis) model and combined it with gray correlation analysis, which can measure the input-output efficiency and quantify and screen the optimization paths. Taking 10 transnational power grid projects and the Asian Energy Internet Program for case analysis, the study found that the input-output efficiency of the third schemes of China's Xinjiang-Pakistan Project is the highest (2.792). For the Southeastern Tibet-Bang Mumbai Project, there is 1047.368-million-yuan investment cost redundancy. The input-output efficiency of Northeast Asia, Southeast Asia and China in 2050 is lower than that of 2030. On the basis of the Asia Energy Interconnection Program in 2030, North-Central Asia, West Asia and South Asia should increase CO 2 emissions reduction by 0.750,0. 396 and 0. 236 million tons respectively. North-Central Asia, West Asia and South Asia should build more hydro-power, wind energy and solar energy power generation bases respectively in order to achieve this adjustment. This work can provide a tool for the evaluation and optimization of transnational power grid project decision makers and energy interconnection scheme planners.

show abstract

“…Wang et al [16] used the BCC model to analyze and evaluate the utilization efficiency of wind energy resources in 25 regions in China from 2014 to 2015 and analyzed the influencing factors of utilization efficiency. Saglam [17][18][19] used a two-stage DEA model to calculate the wind power utilization efficiency of large wind farms in the United States.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China

Wang

Zhou

2020

Complexity

View full text Add to dashboard Cite

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: (1) China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. (2) Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. (3) The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement.

show abstract

Assessment of the Productive Efficiency of Large Wind Farms in the United States: An Application of Two-Stage Data Envelopment Analysis

Cited by 7 publications

References 0 publications

An Overview on Energy Efficiency in View of Supply and Demand

An Overview on Energy Efficiency in View of Supply and Demand

Evaluating and Optimizing Transnational Grid Construction Based on Input-Output Analysis under the Background of Global Energy Interconnection

Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China

Contact Info

Product

Resources

About