Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems (2024)

Buckland, H., Eales, A., Frame, D. (2018). Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems. Paper presented at IEEE PES Power Africa Conference 2018, Cape Town, South Africa.

Buckland, Hannah ; Eales, Aran ; Frame, Damien et al. / Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems. Paper presented at IEEE PES Power Africa Conference 2018, Cape Town, South Africa.6 p.

@conference{4d7cb461504b4801bbed422016943064,

title = "Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems",

abstract = "This paper outlines a statistical modelling methodology for predicting sustainability of PV systems installed in Sub-Saharan Africa, with a sustainability analysis of an example PV system in Malawi, 3.5 years after installation. Social and economic risks to project sustainability are identified (through expert survey and community consultation) and the methodology for including these qualitative risks is described. The project sustainability results are given in terms of probable system operational time (as a fraction). For scenario modelling, the P10 result (P10 can be considered as a measure for the best case; only 10% of cases are better than this) 3.5 years after installation shows how social and economic impacts are predicted to reduce the fraction of time the system is operational to over ¼ of the expected P10. The caveat to this conclusion, is that social and economic risks (such as component theft/tampering, lack of management, possibility of the grid extending to the area, community leadership and structure) are usually mitigated against at the project planning stage. However, the level and type of mitigation is sporadic and inconsistent and should be given significantly more consideration at project conception.",

keywords = "statistical modelling methodology, PV systems, Sub-Saharan Africa, sustainability",

author = "Hannah Buckland and Aran Eales and Damien Frame and Scott Strachan",

year = "2018",

month = jun,

day = "26",

language = "English",

note = "IEEE PES Power Africa Conference 2018 ; Conference date: 26-06-2018 Through 29-06-2018",

}

Buckland, H, Eales, A, Frame, D 2018, 'Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems', Paper presented at IEEE PES Power Africa Conference 2018, Cape Town, South Africa, 26/06/18 - 29/06/18.

Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems. / Buckland, Hannah; Eales, Aran; Frame, Damien et al.
2018. Paper presented at IEEE PES Power Africa Conference 2018, Cape Town, South Africa.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems

AU - Buckland, Hannah

AU - Eales, Aran

AU - Frame, Damien

AU - Strachan, Scott

PY - 2018/6/26

Y1 - 2018/6/26

N2 - This paper outlines a statistical modelling methodology for predicting sustainability of PV systems installed in Sub-Saharan Africa, with a sustainability analysis of an example PV system in Malawi, 3.5 years after installation. Social and economic risks to project sustainability are identified (through expert survey and community consultation) and the methodology for including these qualitative risks is described. The project sustainability results are given in terms of probable system operational time (as a fraction). For scenario modelling, the P10 result (P10 can be considered as a measure for the best case; only 10% of cases are better than this) 3.5 years after installation shows how social and economic impacts are predicted to reduce the fraction of time the system is operational to over ¼ of the expected P10. The caveat to this conclusion, is that social and economic risks (such as component theft/tampering, lack of management, possibility of the grid extending to the area, community leadership and structure) are usually mitigated against at the project planning stage. However, the level and type of mitigation is sporadic and inconsistent and should be given significantly more consideration at project conception.

AB - This paper outlines a statistical modelling methodology for predicting sustainability of PV systems installed in Sub-Saharan Africa, with a sustainability analysis of an example PV system in Malawi, 3.5 years after installation. Social and economic risks to project sustainability are identified (through expert survey and community consultation) and the methodology for including these qualitative risks is described. The project sustainability results are given in terms of probable system operational time (as a fraction). For scenario modelling, the P10 result (P10 can be considered as a measure for the best case; only 10% of cases are better than this) 3.5 years after installation shows how social and economic impacts are predicted to reduce the fraction of time the system is operational to over ¼ of the expected P10. The caveat to this conclusion, is that social and economic risks (such as component theft/tampering, lack of management, possibility of the grid extending to the area, community leadership and structure) are usually mitigated against at the project planning stage. However, the level and type of mitigation is sporadic and inconsistent and should be given significantly more consideration at project conception.

KW - statistical modelling methodology

KW - PV systems

KW - Sub-Saharan Africa

KW - sustainability

M3 - Paper

T2 - IEEE PES Power Africa Conference 2018

Y2 - 26 June 2018 through 29 June 2018

ER -

Buckland H, Eales A, Frame D, Strachan S. Sources of uncertainty for Monte Carlo modelling with Bayesian analysis for Sub-Saharan off-grid solar PV systems. 2018. Paper presented at IEEE PES Power Africa Conference 2018, Cape Town, South Africa.