Background: The Stellenbosch Municipality has released the Guidelines for Small Scale Embedded Generation in Stellenbosch Municipality 2015/2016 that embedded a Feed in Tariff for small (residential) scale solar PV system owners to connect their solar PV systems to the municipal grid network and sell their excess electricity generated back to the grid (Stellenbosch Municipality). The uptake of embedded solar PV systems was limited before the release of the policy and the Centre for Renewable and Sustainable Energy Studies (CRSES), who played a significant role in the development of the policy, hoping that it will incentivise residents to invest in solar PV systems, thus further opening the market for the technology. Market research involving local Stellenbosch residents, to determine their response to the new policy and FiT has, however, not been done.
Objectives: This study aims take the first steps towards generating evidence to evaluate the potential effectiveness of the policy and feed in tariff in achieving the CRSES's aim to help open the market for small-scale embedded solar PV technology by conducting qualitative research into participants' opinions on the policy and to identify any the social and policy barriers to the uptake of embedded solar photovoltaic generation at residential scale in Stellenbosch.
Methods: Qualitative research in the form of a focus group and three interviews with key policy makers to determine the residents' opinions and expectations. The results are analysed from a perspective of complexity and triangulated with broader theory on sustainable development and complexity theory.
Results: There is a misalignment of expectations and goals of residents, policy makers and external organisations such as CRSES
Conclusions: This misalignment is due to a lack of research and is a barrier to uptake of solar PV systems. Relations between policy makers and residents are negatively affected. More inclusive and engaging research/data gathering is required to generate relevant evidence that captures the relevant complexity in order to better inform policy makers' effective decision making for sustainable development.