Using Mobile Phone Data for Electricity Infrastructure Planning

Eduardo A. Martínez Ceseña · Pierluigi Mancarella · Mamadou Ndiaye · Markus Schläpfer

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Human Mobility and Location-Based Analysis Integrated Energy Systems Optimization Energy and Environment Impacts

Abstract

Detailed knowledge of the energy needs at relatively high spatial and temporal resolution is crucial for the electricity infrastructure planning of a region. However, such information is typically limited by the scarcity of data on human activities, in particular in developing countries where electrification of rural areas is sought. The analysis of society-wide mobile phone records has recently proven to offer unprecedented insights into the spatio-temporal distribution of people, but this information has never been used to support electrification planning strategies anywhere and for rural areas in developing countries in particular. The aim of this project is the assessment of the contribution of mobile phone data for the development of bottom-up energy demand models, in order to enhance energy planning studies and existing electrification practices. More specifically, this work introduces a framework that combines mobile phone data analysis, socioeconomic and geo-referenced data analysis, and state-of-the-art energy infrastructure engineering techniques to assess the techno-economic feasibility of different centralized and decentralized electrification options for rural areas in a developing country. Specific electrification options considered include extensions of the existing medium voltage (MV) grid, diesel engine-based community-level Microgrids, and individual household-level solar photovoltaic (PV) systems. The framework and relevant methodology are demonstrated throughout the paper using the case of Senegal and the mobile phone data made available for the 'D4D-Senegal' innovation challenge. The results are extremely encouraging and highlight the potential of mobile phone data to support more efficient and economically attractive electrification plans.

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References

2015World Development indicators 2008Understanding individual human mobility patterns 2014Dynamic population mapping using mobile phone data 2012Understanding individual mobility patterns from urban sensing data: A mobile phone trace example 2014D4D-Senegal: The Second Mobile Phone Data for Development Challenge

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2021Analysing the impact of electrification on rural attractiveness in Senegal with mobile phone data 2019Mobile phone data's potential for informing infrastructure planning in developing countries 2020How average is average? Temporal patterns in human behaviour as measured by mobile phone data -- or why chose Thursdays

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