Design of an off-grid energy kiosk in rural Zambia

Louie, Henry; Shields, Matt; Szablya, Steve; Makai, Likonge; Shields, Kimberly

doi:10.1109/ghtc.2015.7343946

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…Third, the community expected a major benefit due to closer access to cell phone charging, whereas previously they would have to travel long distances. Finally, the community expressed interest in achieving higher tiers of electricity access, namely, TVs, radios and other appliances [4].…”

Section: Current Filibaba System and Designed Load Modelmentioning

confidence: 99%

“…Expected capacity factor was 19.5% with an average daily production of 8.41kWh. Peak generation was during the spring months of September and October and was expected to be 0.4kW while the lowest production was during December to February at 0.3kW [4].…”

Section: B Current Systemmentioning

confidence: 99%

“…The 100W wired connection to the school/community center was planned for six hours every evening after the energy kiosk closes. Lastly it was assumed that the system would see 10W continuous miscellaneous load thus bringing the total nominal load to 5.9kWh per day [4].…”

Section: Expected Load Modelmentioning

confidence: 99%

“…As briefly stated before, based on the pre-design data collection, 79% of the community stated they were willing to pay more than US$8 per month for access to electricity or more than twice their current average energy expenditure [4].…”

Section: Expected Load Modelmentioning

confidence: 99%

“…The Filibaba system was designed and installed keeping a number of parameters in mind, including a business-model based on an expected load of 20 Portable Battery Kits (PBKs) among other loads. The team conducted extensive pre-design data collection work in the form of surveys, a focus group meeting as well as a community meeting and based on that designed/sized the system and designed the business-model for the system [4].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing sub-optimal rural microgrids and methods for improving the system capacity and demand factors: Filibaba microgrid case study examined

Kumar

Makai²,

Singh

et al. 2017

2017 IEEE Global Humanitarian Technology Conference (GHTC)

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Solar energy kiosks in developing countries are commonly designed with battery storage as daytime energy production does not coincide with an evening peak consumption. Curtailment of excess solar energy production can occur when current load and battery storage charging is not high enough during peak solar generation hours. Valuation of the options for coping with this phenomena, after a system is already built, is important for kiosk operators to continue to improve technical and economic performance. Furthermore, little real-world data is available to analyze the extent and impact of this issue, much less the available decisions for the manager of such systems when it occurs. This paper analyzes some of these phenomena and the decisions that kiosk operators can make to improve such performance. Furthermore it analyzes data-sets from a 1.8 kW solar-battery energy kiosk in rural Filibaba, Zambia to determine the level of lost energy production/curtailing that occurred in that system. Finally, potential strategies, including demand response strategies are proposed to both increase as well as shift consumption to daytime hours and ultimately increase the capacity factor of the system. Such strategies could potentially help reduce the lost production of almost 1.7MWh that was witnessed in 11 months of system usage. These strategies could also increase the revenue of the system by approx. US$810 annually. Such strategies include pricing incentives, manual demand response, and system redesign options. In the general context of operations of rural solar kiosks, this work advocates for the need to continuously improve operational as well as hardware strategy based on fieldevidence.

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Section: Current Filibaba System and Designed Load Modelmentioning

confidence: 99%

Section: B Current Systemmentioning

confidence: 99%

Section: Expected Load Modelmentioning

confidence: 99%

Section: Expected Load Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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