Weather-sensitive, spatially-disaggregated electricity demand model for Nigeria

Abstract

The historical underinvestment in power infrastructure and the poor performance of power delivery has resulted in extensive and regular power shortages in Nigeria. As Nigeria aims to bridge its power supply gap, the recent deregulation of its electricity market has seen the privatisation of its generation and distribution companies. Ambitious plans have also been put in place to expand the transmission network and the total power generation capacity. However, these plans have been developed with essentially arbitrary estimates for prevailing demand levels as the network and generation limits mean actual demand cannot be measured directly due to a programme of almost constant load shedding; the managed and intermittent distribution of inadequate energy allocation from the system operator. Network expansion planning and system reliability analysis require time series demand data to assess generation adequacy and to evaluate the impact of daily and seasonal influences on the energy supply-demand balance. To facilitate such analysis this thesis describes efforts to develop a credible time series electricity demand model for Nigeria. The focus of the approach has been to develop a fundamental bottom-up model of individual households accounting for a range of dwelling characteristics, socioeconomic factors, appliance use and household activities. A householder survey was conducted to provide essential inputs to allow a portfolio of household demand models which can account for weather-dependence and other factors. A range of national and regional socioeconomic and weather datasets have been employed to create a regionally disaggregated time series demand model. The generated demand estimates are validated against metered data obtained from Nigeria. The value of the approach is highlighted by using the model to investigate the potential for future load growth as well as analyse the impact of renewable energy generation on the Nigerian grid.