Lay summary of the research article published under the DOI: 10.1007/978-3-319-93438-9_9
Researchers say the remote Semonkong area of Lesotho could reduce electricity supply costs by 40% by boosting the renewable energy mix in the local grid from 66% to 98%, using wind, hydroelectric, battery and photovoltaic power. The remaining 2% would be covered by diesel generators.
The small town of Semonkong, in Maseru district, is situated in one of the many rugged hills and mountain ranges in Lesotho, which makes it expensive to provide electricity.
As such, Semonkong currently uses hydroelectric power generated by the local Maletsunyane River to supply 66% of its electricity. The other 34% comes from diesel generators.
This study aimed to use HOMER (Hybrid Optimization Model for Electric Renewable) software to calculate the maximum power that can be generated using renewable energy resources available in the region at the lowest possible cost.
The researchers used the HOMER software to simulate the local electricity grid of Semonkong in a computer by testing the effects of adding different amounts of renewable energy technologies such as photovoltaic (energy from solar panels), wind, hydroelectric, and battery storage.
The researchers calculated the Levelised Cost of Energy (LCOE) for each electricity grid mix to find out how much it would cost over the lifetime of the infrastructure.
The researchers found that the cheapest electric grid option for Semonkong is a renewable energy mix of wind, hydroelectric, and battery. This electric grid mix would be 94% renewable energy, with the last 6% covered by diesel generators.
The option with the highest possible share of renewable energy at 98% adds photovoltaic to the cheapest electric grid mix above, leaving only 2% to be covered by diesel generators.
If these HOMER software computer simulations were implemented, it would improve the current electric grid mix of renewable hydroelectric power from the Maletsunyane River, which covers 66%, with diesel generators covering 34%. The study shows that a grid with 98% renewable energy would lower costs by 40%.
The study only used data specific to Semonkong, which makes the results apply only to Semonkong.
However, this study shows that natural resources such as wind, rivers, and areas where sunlight is abundant can be used to supplement local and national electricity grids at lower costs.
Number of words: 347
Rugged hills and mountain ranges with sparsely populated rural villages characterize the vast majority of Lesotho’s landscape, making it prohibitively expensive and financially unviable to connect these remote villages to the national electricity grid. This lack of access to electricity has hampered many social and economic developments due to insufficient provision of much-needed power to homes, schools, police stations, clinics and local businesses. This paper proposes a renewable energy hybrid power generation system for one such remote town of Semonkong, in Maseru district, Lesotho. The study models, simulates and optimizes the hybrid power system using the load profile of Semonkong town and the available renewable resources data of solar radiation, wind speeds and water flow rates from the nearby Maletsunyane River. The HOMER software is used to provide an optimal system configuration in terms of the minimum levelized cost of electricity (LCOE) and the maximum renewable energy fraction, based on various renewable and alternative energy sources of solar photovoltaic, wind turbine, mini-hydro turbine, diesel generator and battery storage. Sensitivity analysis on solar radiation, wind speed, stream flow, diesel price and energy demand is undertaken to evaluate the feasibility of a completely-renewable power system suitable for this remote area application. Simulation results for the isolated optimized hydro/wind/PV/diesel/battery hybrid system configuration achieves LCOE of US$0.289/kW at a renewable energy fraction of 0.98. Thus, the diesel generator will always be required to augment the power supply for Semonkong especially during the dry and cold winter months of May to September when the energy demand is at its peak but the solar radiation and stream flow are at their lowest.
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