Soft-linking energy demand and optimisation models for local long-term electricity planning: An application to rural India

Fabio Riva, Francesco Gardumi, Annalisa Tognollo and Emanuela Colombo

Energy, 2019, vol. 166, issue C, 32-46

Abstract: Rural electricity plans are usually designed by relying on top-down rough and aggregated estimations of the electricity demand, which may fail to capture the real dynamics of local contexts. This study aims at soft-linking a bottom-up approach for short- and long-term forecasts of load profiles with an energy optimisation model in a more comprehensive rural energy planning procedure. The procedure is applied to a small Indian community, and it is based on three blocks: (i) a bottom-up model to project households' electrical appliances, which adopts socio-economic indicators to make long-term projections; (ii) a stochastic load profile generator, which employs correlations and users’ habits for assessing the coincidence and load factors; (ii) an energy optimisation model based on OSeMOSYS to find the economic optimum. The simulations show that demand models based on socio-economic indicators lead to more structured and less arbitrary scenarios. The soft-link with the energy optimisation model confirms that when accounting for short- and long-term variabilities of electricity demand together, the optimal capacities and costs can vary up to 144% and 50% respectively. Integrating optimisation tools to bottom-up models based on socio-economic indicators for forecasting electricity demand is therefore pivotal to set more reliable investments plans in rural electrification.

Keywords: Rural electricity planning; Electricity demand model; Optimisation; Energy modelling; LoadProGen; OSeMOSYS (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:166:y:2019:i:c:p:32-46

DOI: 10.1016/j.energy.2018.10.067

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