Abstract
Population dispersion necessitates grid expansion to meet electricity demand. For many developing countries and
remote communities, meeting electricity demand is a challenge due to a power generation shortage and load variability
that is highly driven by weather uncertainty. Electric utilities’ practical planning solutions are to disable electricity access
from new residential regions, supply at least 10 percent of the non-electrified regions, or follow a rotating feeder
curtailment such that the new regions are electrified for few hours daily. This paper proposes an alternative framework to
plan electricity access more efficiently in developing countries. A probabilistic multi-stage optimization framework that first
incorporates in-depth analysis of appliance operational models, second accounts for AC grid codes of operation and third
anticipates consumers’ actions is deployed. The framework is formulated to account for climate/weather uncertainty
factors. Results show that energy efficiency can reach up to 97%, and the computation time can be improved by 99.6% with
respect to the existing current state of the art approaches.
remote communities, meeting electricity demand is a challenge due to a power generation shortage and load variability
that is highly driven by weather uncertainty. Electric utilities’ practical planning solutions are to disable electricity access
from new residential regions, supply at least 10 percent of the non-electrified regions, or follow a rotating feeder
curtailment such that the new regions are electrified for few hours daily. This paper proposes an alternative framework to
plan electricity access more efficiently in developing countries. A probabilistic multi-stage optimization framework that first
incorporates in-depth analysis of appliance operational models, second accounts for AC grid codes of operation and third
anticipates consumers’ actions is deployed. The framework is formulated to account for climate/weather uncertainty
factors. Results show that energy efficiency can reach up to 97%, and the computation time can be improved by 99.6% with
respect to the existing current state of the art approaches.
Original language | English |
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Pages (from-to) | 2572-2583 |
Journal | IET Generation, Transmission & Distribution |
Volume | 13 |
Issue number | 12 |
Early online date | 4 Apr 2019 |
DOIs | |
Publication status | Published - 18 Jun 2019 |