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Parshall, L., Pillai, D., Mohan, S., et al. (2009) National Electricity Planning in Settings with Low Pre-existing Grid Coverage: Development of a Spatial Model and Case Study of Kenya. Energy Policy, 37, 2395-2410.
http://dx.doi.org/10.1016/j.enpol.2009.01.021

has been cited by the following article:

• TITLE: Modelling Impacts of Socio-Economic Factors on Temporal Diffusion of PV-Based Communal Grids

  AUTHORS: Nicholas Opiyo

  KEYWORDS: PV Diffusion, Developing Community, Economic Factors, Social Factors, Political Factors

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.6 No.12, December 30, 2015

  ABSTRACT: Impacts of socio-economic factors on temporal diffusions of solar electricity microgeneration systems in a rural developing community are modelled and simulated using an agent-based model (ABM). ABMs seek to capture the overall macro-effects of different micro-decisions in a virtual world; they model individual entities within a complex system and the rules that govern them to capture the overall effects of their interactions. Results showed that falling PV costs coupled with generally increasing grid electricity costs would lead to increased uptake of PV systems in such communities. On the other hand, high lending rates in most developing nations would stifle use of credit facilities in purchases of PV systems and thus diminishing their uptakes. Results also showed that introduction of favourable government policies in forms of subsidies would strongly stimulate PV installations in such communities. Social acceptance is important for diffusion of any new technology into a given market and more so with solar systems; results show that neighbourhood influence plays major roles in PV diffusions with many households installing PV systems if their neighbours within a given sensing radius do the same. Results also showed that requiring a certain percentage of neighbours to have installed PV before a household considered doing the same could have negative effects on PV installations as decisions to install PV are influenced by many independent and dependent factors and not by neighbourhood threshold alone.