  Small privately-purchased off-grid renewable energy systems (RESs) are
increasingly used for energy generation in remote areas. However, such
privately-purchased stand-alone RESs are often unaffordable for households with
lower incomes. While considerable attention has been devoted to a range of
off-grid microgrid sizing methods, leveraging the potential of data-driven,
artificial intelligence-based metaheuristic optimisation algorithms is less
well-explored. Importantly, data-driven metaheuristics have the potential to
produce the nearest solution to the globally optimum solution in microgrid
sizing applications, which have been recognised as non-deterministic,
polynomial time-hard (NP-hard) problems. Furthermore, there is a general lack
of electrified transportation interventions considered during long-term
grid-independent microgrid planning phases. In response, this paper introduces
a novel metaheuristic-based strategic off-grid microgrid capacity planning
optimisation model that is applicable to associated integrated energy and
e-mobility resource plans. The formulated general off-grid microgrid sizing
model is solved using a competitively selected state-of-the-art metaheuristic,
namely moth-flame optimisation. To test the effectiveness of the proposed
model, three independent microgrid development projects have been considered
for three communities residing on Aotea-Great Barrier Island, namely Tryphena,
Medlands, and Mulberry Grove. The sites of interest have different demand
profiles and renewable energy potentials, with consequent changes in the
technologies considered in the associate candidate pools.
