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Cost/benefit analysis of an AMR system to reduce electricity theft and maximize revenues for Électricité du Liban

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  • Ghajar, Raymond F.
  • Khalife, Joseph

Abstract

One of the largest pitfalls for any distribution network is the level of energy losses suffered by the system. These losses fall into two categories: technical and non-technical. Technical losses depend largely on the physical properties of the network, while non-technical losses (sometimes a more significant form of losses) are the result of theft or fraud caused by meter tampering, false reading, illegal connections or unpaid bills. In Lebanon, the levels of total losses are around 50% resulting in an annual deficit of more than 225 million US dollars. Despite the frequent breakdowns of the system and evidently unsustainable financial-losses, political consideration makes the sustained pursuit of electricity thieves low on the list of priorities. To overcome these hurdles, the national electricity company in Lebanon, Électricité du Liban (EDL), studied the possibility of using automatic meter-reading (AMR) technology to modernize electricity metering, billing and collection, minimize fraud and maximize revenues. The results of this study and a cost/benefit analysis of the proposed system are summarized in this paper.

Suggested Citation

  • Ghajar, Raymond F. & Khalife, Joseph, 2003. "Cost/benefit analysis of an AMR system to reduce electricity theft and maximize revenues for Électricité du Liban," Applied Energy, Elsevier, vol. 76(1-3), pages 25-37, September.
  • Handle: RePEc:eee:appene:v:76:y:2003:i:1-3:p:25-37
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    Citations

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    Cited by:

    1. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    2. Mwaura, Francis M., 2012. "Adopting electricity prepayment billing system to reduce non-technical energy losses in Uganda: Lesson from Rwanda," Utilities Policy, Elsevier, vol. 23(C), pages 72-79.
    3. Wang, Xiaotong & Lu, Meijun & Mao, Wei & Ouyang, Jinlong & Zhou, Bo & Yang, Yunkai, 2015. "Improving benefit-cost analysis to overcome financing difficulties in promoting energy-efficient renovation of existing residential buildings in China," Applied Energy, Elsevier, vol. 141(C), pages 119-130.
    4. Yurtseven, Çağlar, 2015. "The causes of electricity theft: An econometric analysis of the case of Turkey," Utilities Policy, Elsevier, vol. 37(C), pages 70-78.
    5. Tasdoven, Hidayet & Fiedler, Beth Ann & Garayev, Vener, 2012. "Improving electricity efficiency in Turkey by addressing illegal electricity consumption: A governance approach," Energy Policy, Elsevier, vol. 43(C), pages 226-234.
    6. Tursunboev, Jamshid & Palakonda, Vikas & Kang, Jae-Mo, 2024. "Multi-Objective Evolutionary Hybrid Deep Learning for energy theft detection," Applied Energy, Elsevier, vol. 363(C).
    7. Wan, Nianfeng & Jiang, Jiexian & Ji, Xiangyun & Deng, Jianyu, 2009. "Application of analytic hierarchy process-based model of Ratio of Comprehensive Cost to Comprehensive Profit (RCCCP) in pest management," Ecological Economics, Elsevier, vol. 68(3), pages 888-895, January.
    8. Nora Stel, 2012. "Business by Generator The impact of fragility and hybridity on Lebanese entrepreneurship – A Case-Study of the Electricity Sector," Working Papers 2012/52, Maastricht School of Management.

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