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Optimal sampling plan for clean development mechanism lighting projects with lamp population decay

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  • Ye, Xianming
  • Xia, Xiaohua
  • Zhang, Jiangfeng

Abstract

This paper proposes a metering cost minimisation model that minimises metering cost under the constraints of sampling accuracy requirement for clean development mechanism (CDM) energy efficiency (EE) lighting project. Usually small scale (SSC) CDM EE lighting projects expect a crediting period of 10years given that the lighting population will decay as time goes by. The SSC CDM sampling guideline requires that the monitored key parameters for the carbon emission reduction quantification must satisfy the sampling accuracy of 90% confidence and 10% precision, known as the 90/10 criterion. For the existing registered CDM lighting projects, sample sizes are either decided by professional judgment or by rule-of-thumb without considering any optimisation. Lighting samples are randomly selected and their energy consumptions are monitored continuously by power meters. In this study, the sampling size determination problem is formulated as a metering cost minimisation model by incorporating a linear lighting decay model as given by the CDM guideline AMS-II.J. The 90/10 criterion is formulated as constraints to the metering cost minimisation problem. Optimal solutions to the problem minimise the metering cost whilst satisfying the 90/10 criterion for each reporting period. The proposed metering cost minimisation model is applicable to other CDM lighting projects with different population decay characteristics as well.

Suggested Citation

  • Ye, Xianming & Xia, Xiaohua & Zhang, Jiangfeng, 2014. "Optimal sampling plan for clean development mechanism lighting projects with lamp population decay," Applied Energy, Elsevier, vol. 136(C), pages 1184-1192.
    • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:1184-1192
    DOI: 10.1016/j.apenergy.2014.07.056
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    References listed on IDEAS

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    1. Carstens, Herman & Xia, Xiaohua & Ye, Xianming, 2014. "Improvements to longitudinal Clean Development Mechanism sampling designs for lighting retrofit projects," Applied Energy, Elsevier, vol. 126(C), pages 256-265.
    2. Ye, Xianming & Xia, Xiaohua & Zhang, Jiangfeng, 2013. "Optimal sampling plan for clean development mechanism energy efficiency lighting projects," Applied Energy, Elsevier, vol. 112(C), pages 1006-1015.
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    Citations

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    Cited by:

    1. Ye, Yuxiang & Koch, Steven F. & Zhang, Jiangfeng, 2018. "Determinants of household electricity consumption in South Africa," Energy Economics, Elsevier, vol. 75(C), pages 120-133.
    2. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    3. Lin, Boqiang & Jia, Zhijie, 2017. "The impact of Emission Trading Scheme (ETS) and the choice of coverage industry in ETS: A case study in China," Applied Energy, Elsevier, vol. 205(C), pages 1512-1527.
    4. Du, Feng & Zhang, Jiangfeng & Li, Hailong & Yan, Jinyue & Galloway, Stuart & Lo, Kwok L., 2016. "Modelling the impact of social network on energy savings," Applied Energy, Elsevier, vol. 178(C), pages 56-65.
    5. Ikuzwe, Alice & Ye, Xianming & Xia, Xiaohua, 2020. "Energy-maintenance optimization for retrofitted lighting system incorporating luminous flux degradation to enhance visual comfort," Applied Energy, Elsevier, vol. 261(C).
    6. Ye, Xianming & Xia, Xiaohua, 2016. "Optimal metering plan for measurement and verification on a lighting case study," Energy, Elsevier, vol. 95(C), pages 580-592.
    7. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
    8. Olinga, Zadok & Xia, Xiaohua & Ye, Xianming, 2017. "A cost-effective approach to handle measurement and verification uncertainties of energy savings," Energy, Elsevier, vol. 141(C), pages 1600-1609.

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