A bottom-up weather-sensitive residential demand model for developing countries. A case study of Abuja, Nigeria

Aristides E Kiprakis,Adriaan H Van Der Weijde,Oluwadamilola Oluwole,Adam J Collin,Gareth P Harrison

doi:10.1016/j.esd.2020.08.009

Aristides E Kiprakis, Adriaan H Van Der Weijde + Show 3 more

Open Access

https://doi.org/10.1016/j.esd.2020.08.009

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Abstract

The frequency of power outages being experienced in Sub-Saharan Africa mean that traditional methods of electricity demand forecasting which rely on directly observed demand data are inadequate for use in projections. Nevertheless, accurate forecasting methods are urgently required to ensure efficient power system operations and expansion planning. To address this gap, we develop a novel method to estimate unsuppressed electricity demand for developing countries. This follows a bottom-up approach based on socioeconomic data and a time-use database developed from a householder survey, which are used to generate household profiles using a Markov Chain approach. These profiles are then converted into electrical load time series by a series of appliance models, using reanalysis weather data to accurately represent ambient conditions for the generation of cooling demand profiles. We apply our method to a Nigerian case study, obtaining the first time series of unsuppressed residential electricity demand for the country using the first Time-of-Use Survey (TUS) for Nigerian households. We validate our model outputs using the results of a small-scale residential metering trial, which yielded a correlation coefficient of 0.97, RMSE of 0.04, and percentage error of 6% between measured and model data. This evidences that our method is a credible and practical tool for electrical demand studies in developing countries. Using the model, the forecasted domestic demand for Abuja Electricity Distribution Company ranges between 345 and 575 MW, while that of Nigeria ranges between 3829 and 6605 MW.

Highlights

Historical underinvestment due to inadequate funding and inefficient power sector operations, along with a rapidly growing population in developing countries, in sub-Saharan Africa, have resulted in a significant population of about 620 million people without access to grid electricity [1]
To aggregate the demand profiles generated from the simulated households, we develop a bottom-up model that allows for flexibility in the aggregation grouping criteria, including socioeconomic and spatial information, using the after diversity maximum demand (ADMD) method
We have presented a weather-sensitive bottom-up stochastic demand estimation model which relies on socioeconomic information, survey results of household activity patterns in and reanalysis weather data, instead of measured demand data which is often problematic in developing countries

Summary

Introduction

Historical underinvestment due to inadequate funding and inefficient power sector operations, along with a rapidly growing population in developing countries, in sub-Saharan Africa, have resulted in a significant population of about 620 million people without access to grid electricity [1]. To manage the limited energy supply, network operators resort to load shedding to manage inadequate energy allocations from the grid. This load shedding, which can either be pre-planned or arbitrary, occurs at different transmission voltage levels of the network by connecting and disconnecting customers. The inability of generation to meet demand results in an essentially flat measured load duration curve that does not reflect the actual spatial and temporal variations in demand. In northern Africa, a study has shown that a 1% increase in average temperature will increase electricity consumption by 1.32% [12]

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