An Energy Model for a Low Income Rural African Village

Energy use is closely linked to quality of life in rural Africa. The gathering of fuel-wood and other traditional fuels is a strenuous and time consuming task mainly performed by women; indoor exposure to particulate matter, mainly from cooking and heating with traditional fuels, causes about 2.5 million deaths each year in developing countries (Bruce et al. 2002). Modern fuels and appliances allow households to reduce their exposure to smoke from biomass cookers and heaters. Yet modern fuels are costly for income-poor households and often carry their own external costs. For example, numerous children are poisoned from ingesting paraffin, and whole villages have burned from fires triggered by paraffin stoves and lamps. This paper reports on efforts to extend a MARKAL energy model for South Africa to include rural energy choices, allowing for computation of optimal energy systems in a typical (non-electrified) rural village. A previous study (Howells et al. 2002) highlighted deficiencies in earlier efforts to build models of rural household energy behaviour, such as inadequate calibration against surveys of actual energy use in rural settings as well as limited representation of time resolution within the model. The present study incorporates a new village energy survey. It also deploys TIMES, an extension of the MARKAL computational framework that allows explicit modelling of timeof-day load curves, for demand side management analysis, and the representation of storage devices and end-use technologies (“appliances”) that meet more than one energy service concurrently. With TIMES, for example, it is possible to account for the fact that open braziers are typically used in a flexible manner to supply hot water, cooking and household heating in low-income rural settings. Past failures to model the multi-functional nature of such appliances may account for why earlier studies often over-stated rates at which new single-function energyuse devices, such as electric appliances, would diffuse and displace the old. Not accounting for the multi service 1 For an overview of the MARKAL family, please see Goldstein, G.A., L.A. Greening, and the Partners in International Energy Agency – Energy Technology Systems Analysis Programme (IEA-ETSAP), 1999, Energy Planning and the Development of Carbon Mitigation Strategies: Using the MARKAL Family of Models; available from the ETSAP website, www.etsap.org. The model is fully described in User’s Guide for MARKAL (BNL/KFA) Version 2.0,” Fishbone, et. al., BNL 51701, July 1, 1983. 2 TIMES is a new model under development by the IEA-ETSAP that possesses all the features of MARKAL, plus added flexibility in the definition of time periods, time slices, and technologies. supply from appliances results in understating the services met by the appliance and understating its economic performance. We report load curves for energy demand activities such as cooking, heating and lighting and identify least cost supply options. The model reproduces the phenomenon, known anecdotally from household surveys, of declining total fuel use that accompanies the shift from traditional to modern, more efficient appliances—for example, the switch from inexpensive candles and wick kerosene devices to much more efficient (lumens per fuel use) but more capital-intensive pressurized stoves. We also investigate scenarios in which villagers are able to procure electricity (via grid connection, decentralized stand-alone generators and photovoltaics), and we examine the effects on energy choices if household pollution from appliances is internalized as health-related externality costs. Internalisation of pollution costs leads households to select at least a low volume of electricity, for lighting; when grid connections are available the shift to electricity is more extensive and less costly (to households and perhaps also to society). An early product from a collaborative international effort, this model establishes a framework that allows for substantially improved future models based on recent and planned energy surveys in South Africa; the framework is also extendable to neighbouring countries and perhaps other world regions. This tool may also be useful in aiding efforts to establish baselines and counter-factual scenarios that are essential to making workable schemes such as the Kyoto Protocol’s Clean Development Mechanism (CDM).