Automated evaluation of levelized cost of energy of isolated micro-grids for energy planning purposes in developing countries

Countries around the world are preparing to give the last leap to accomplish a 100
% of rural energy access. Nonetheless, country-wide electrification planning requires
the analysis of hundreds of un-electrified villages with different social, economical
and geographical backgrounds. State-of-the-art planning models typically handle
this computationally challenging task relying on highly-simplified technological char-
acterizations, at the expense of a proper estimation of the cost-optimal potential of
off-grid technologies, particularly micro-grids. In this paper, we propose a machine-
learning method to improve such technological characterization while keeping the
computational tractability of the problem under control. Firstly, field surveys from
rural un-electrified villages in Bolivia are used as an input for a stochastic load
generator model, creating several demand scenarios for a set of different village
archetypes; secondly, renewable energy time series for representative locations of
Bolivia are created using the NASA database. For each demand and renewables
potential combination, a two-stage stochastic sizing model is adopted to obtain the
corresponding cost-optimal micro-grid configuration. Finally, these data are used
to train a Gaussian process regression with the levelized cost of energy (LCOE) as
dependent variable and the daily average demand, renewable energy, and techno-
economic characteristics of the components as independent variables. The results
show that the trained algorithm is ultimately able to identify the LCOE of micro-
grids in given conditions, out of the training dataset, with satisfying accuracy and
limited computational effort.