John Holdren states there are three responses to climate change: mitigation, adaptation, and suffering. Research abounds in framing mitigation and adaptation policies. Furthermore, many suffering criteria exist in the health care literature as a physiological and psychological component to patients' care, but not in the technological aspects to hospitals. In this paper, the socio-technological challenges of renewable electricity in Uganda are explored to frame the reality of unreliable electricity within a suffering criteria. For this paper, we define suffering criteria as avoidable damage (unmet power load) which is highly dependent on balancing variability and uncertainty. For example, there is a measurable variable solar radiance profile. On any specific day, dispatch microgrid control algorithms consider the measured variability as an uncertainty, but here we define uncertainty as adding random noise to the measured variability using Monte-Carlo simulations. By doing this, we have a renewable energy system defined within a suffering criteria that is clearly illustrated within bounds (no variability, measured variability, and simulated additional variability from uncertainty). Unmet load data is generated using HOMER Energy. This study can help further understand variability and uncertainty in renewable energy sources in hybrid microgrids as it is framed within a suffering criteria -avoidable damage on health care due to unmet power load. It is vitally important due to renewable energy system trade-offs between overdesign (levelized cost of electricity > $1/kWh) and underdesign (capacity shortage > 50%). It leads to motivations for redundancy in microgrids similar to redundancy in largescale centralized grids.
Background Surgery risks increase when electricity is accessible but unreliable. During unreliable electricity events and without data on increased risk to patients, medical professionals base their decisions on anecdotal experience. Decisions should be made based on a cost-benefit analysis, but no methodology exists to quantify these risks, the associated hidden costs, nor risk charts to compare alternatives. Methods Two methodologies were created to quantify these hidden costs. In the first methodology through research literature and/or measurements, the authors obtained and analyzed a year’s worth of hour-by-hour energy failures for four energy healthcare system (EHS) types in four regions (SolarPV in Iraq, Hydroelectric in Ghana, SolarPV+Wind in Bangladesh, and Grid+Diesel in Uganda). In the second methodology, additional patient risks were calculated according to time and duration of electricity failure and medical procedure impact type. Combining these methodologies, the cost from the Value of Statistical Lives lost divided by Energy shortage ($/kWh) is calculated for EHS type and region specifically. The authors define hidden costs due to electricity failure as VSL/E ($/kWh) and compare this to traditional electricity costs (always defined in $/kWh units), including Levelized Cost of Electricity (LCOE also in $/kWh). This is quantified into a fundamentally new energy healthcare system risk chart (EHS-Risk Chart) based on severity of event (probability of deaths) and likelihood of event (probability of electricity failure). Results VSL/E costs were found to be 10 to 10,000 times traditional electricity costs (electric utility or LCOE based). The single power source EHS types have higher risks than hybridized EHS types (especially as power loads increase over time), but all EHS types have additional risks to patients due to electricity failure (between 3 to 105 deaths per 1,000 patients). Conclusions These electricity failure risks and hidden healthcare costs can now be calculated and charted to make medical decisions based on a risk chart instead of anecdotal experience. This risk chart connects public health and electricity failure using this adaptable, scalable, and verifiable model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.