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Soft-linking energy demand and optimisation models for local long-term electricity planning: An application to rural India
Politecn Milan, Dept Energy, Via Lambruschini 4, Milan, Italy..
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Energy Systems Analysis.
Politecn Milan, Dept Energy, Via Lambruschini 4, Milan, Italy..
Politecn Milan, Dept Energy, Via Lambruschini 4, Milan, Italy..
2019 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 166, p. 32-46Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 166, p. 32-46
Keywords [en]
Rural electricity planning, Electricity demand model, Optimisation, Energy modelling, LoadProGen, OSeMOSYS
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-242991DOI: 10.1016/j.energy.2018.10.067ISI: 000455694300003Scopus ID: 2-s2.0-85055020905OAI: oai:DiVA.org:kth-242991DiVA, id: diva2:1285346
Note

QC 20190204

Available from: 2019-02-04 Created: 2019-02-04 Last updated: 2019-02-04Bibliographically approved

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Gardumi, Francesco

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