Electricity demand in populations gaining access: Impact of rurality and climatic conditions, and implications for microgrid design

Almost 800 million people currently lack access to reliable electricity, for many of whom solar microgrid systems are expected to be the most cost-effective solution. Quantifying current and future electricity demand is crucial for cost-effective design of reliable microgrids. However, electricity usage is connected to a wide range of social and economic factors alongside climatic conditions, making estimation of demand challenging. This paper presents a framework facilitating each stage of solar microgrid design from demand estimation through to cost-optimal sizing of the microgrid and its economic and environmental characterisation. Household demand is simulated based upon (1) climatic conditions, (2) appliance ratings and usage patterns, and (3) rates of growth in appliance ownership based upon the Multi-Tier Framework for measuring household electricity access. Microgrid demands are simulated based on the combination of these with (4) nondomestic demand based upon locally available data. The framework is demonstrated across four rates of domestic demand growth and two climatic conditions (‘tropical savanna’ and ‘humid subtropical’), alongside nondomestic demand based upon two operational microgrids (one rural and one peri-urban). When growth rates are high, newly introduced appliances tend to dominate, with differing impacts on the demand profile depending on power and usage times. Cooling represents a modest contribution to demand in the tropical savanna climate. However, in the hotter and more seasonally varying humid subtropical climate, cooling becomes the dominant driver of demand, increasing seasonality and proportion of demand at night. Nondomestic demand in the rural microgrid is primarily agricultural, and exhibits more seasonality and better alignment with daylight hours than demand in the peri-urban setting, which is more service-based. Across cases, increased seasonality and proportion of demand at night lead to poorer alignment with PV generation, increasing cost and GHG emissions per unit of electricity used in a cost-optimised microgrid system.