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.
|Number of pages||6|
|Publication status||Published - 26 Jun 2018|
|Event||IEEE PES Power Africa Conference 2018 - Cape Town, South Africa|
Duration: 26 Jun 2018 → 29 Jun 2018
|Conference||IEEE PES Power Africa Conference 2018|
|Period||26/06/18 → 29/06/18|
- statistical modelling methodology
- PV systems
- Sub-Saharan Africa
Buckland, H., Eales, A., Frame, D., & Strachan, S. (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.