  In remote or islanded communities, the use of microgrids (MGs) is necessary
to ensure electrification and resilience of supply. However, even in
small-scale systems, it is computationally and mathematically challenging to
design low-cost, optimal, sustainable solutions taking into consideration all
the uncertainties of load demands and power generations from renewable energy
sources (RESs). This paper uses the open-source Python-based Energy Planning
(PyEPLAN) tool, developed for the design of sustainable MGs in remote areas, on
the Alderney island, the 3$^{rd}$ largest of the Channel Islands with a
population of about 2000 people. A two-stage stochastic model is used to
optimally invest in battery storage, solar power, and wind power units.
Moreover, the AC power flow equations are modelled by a linearised version of
the DistFlow model in PyEPLAN, where the investment variables are here-and-now
decisions and not a function of uncertain parameters while the operation
variables are wait-and-see decisions and a function of uncertain parameters.
The $k$-means clustering technique is used to generate a set of best
(risk-seeker), nominal (risk-neutral), and worst (risk-averse) scenarios
capturing the uncertainty spectrum using the yearly historical patterns of load
demands and solar/wind power generations. The proposed investment planning tool
is a mixed-integer linear programming (MILP) model and is coded with Pyomo in
PyEPLAN.
