The study’s focus on a rural clinic near Polokwane reflects a broader challenge across sub-Saharan Africa, where healthcare facilities frequently operate without dependable grid electricity. In such settings, interruptions to power supply are not merely inconvenient; they can compromise the cold chain that keeps vaccines viable, disrupt diagnostic procedures, and leave emergency services without lighting or communications. By anchoring the analysis to a specific location with coordinates of −23.9045° latitude and 29.4689° longitude, the researchers were able to draw on monthly renewable resource availability data that captures the seasonal character of solar irradiance and wind speeds in the Limpopo region, rather than relying on annual averages that can mask important month-to-month variability.

The choice of HOMER Pro as the modeling platform is significant for understanding how the results were produced. The software performs hourly simulations across an entire year, testing thousands of possible combinations of component sizes and dispatch strategies. For each configuration, it calculates how the system would respond to every hour of the modeled load profile, tracking when batteries charge, when they discharge, when surplus solar generation is curtailed, and when the system fails to meet demand. It then ranks the feasible configurations by net present cost, which aggregates all capital, replacement, operation, and maintenance expenditures over the project lifetime, discounted to present value. This ranking approach is what allows the analysis to identify the least-cost design rather than simply evaluating a handful of pre-selected options.

Importantly, the authors did not rely solely on the simulation output. They conducted independent energy balances to cross-check the reported annual figures, a verification step that adds transparency to the pre-feasibility assessment. This kind of cross-validation matters because simulation tools can embed assumptions about component efficiency, dispatch logic, and losses that may not be immediately visible to the reader. By reproducing the energy accounting separately, the researchers provided a check on whether the headline numbers, such as the 0.1% unmet load, were artifacts of the software’s internal assumptions or robust outcomes of the underlying physics of generation and storage.

The base-case load of 85 kWh per day with a peak demand of 13.8 kW represents a modest but clinically meaningful electricity requirement. For context, this scale of demand is consistent with a facility that must refrigerate vaccines, power interior and exterior lighting, run basic diagnostic equipment, pump water, and maintain communications, without supporting energy-intensive services such as large-scale sterilization or air conditioning. The peak-to-average ratio embedded in these figures, roughly 3.9 kW of average continuous draw against a 13.8 kW peak, illustrates why battery sizing is so consequential: the storage system must absorb midday solar surplus and then deliver power through evening and overnight hours when clinic demand persists but generation ceases.

The optimal configuration identified, comprising 70 kW of photovoltaic capacity, 200 kWh of lithium-ion battery storage, and a 35 kW bidirectional converter, achieves a net present cost of $210,075 and a levelized cost of electricity of $0.519 per kilowatt-hour. The absence of wind turbines in this least-cost design is a direct consequence of Limpopo’s solar resource profile. The region experiences high solar exposure throughout much of the year, which allows photovoltaic panels to generate substantial energy at relatively low cost per kilowatt-hour. Wind turbines, by contrast, require sustained wind speeds at hub height to be economically competitive, and without site-level measurement at the appropriate height, their capacity factors remain uncertain enough that including them raises overall system cost without a commensurate reliability benefit under base-case conditions.

The alternative configuration, which pairs 55 kW of photovoltaic capacity with 20 kW of wind capacity and 240 kWh of battery storage, was retained in the analysis not as an economic recommendation but as a stress test case. Its net present cost of $287,546 and levelized cost of $0.711 per kilowatt-hour make it roughly 37% more expensive than the base case, but its value lies in demonstrating how source diversity affects system behavior under adverse conditions. Hybrid systems with multiple generation technologies can, in principle, buffer against correlated failures, such as extended cloudy periods that suppress solar output, provided the secondary resource is genuinely available when the primary one falters. The authors’ framing of this configuration as illustrative rather than optimal underscores an important distinction in off-grid system planning between least-cost design and resilient design.

This distinction has practical consequences for healthcare infrastructure planning. A least-cost system minimizes expenditure under expected conditions but may carry higher risk if conditions deviate from assumptions, for instance through prolonged poor solar resource, faster-than-expected battery degradation, or demand growth that outpaces projections. A resilient design accepts higher upfront and lifecycle costs in exchange for redundancy and robustness against such deviations. The framework presented in the study gives planners a structured way to evaluate this trade-off quantitatively rather than relying on intuition, by comparing the cost premium of diversified configurations against the reliability improvements they deliver under stress scenarios.

Demand growth is a central uncertainty in rural healthcare electrification. Clinics that begin with vaccine refrigeration and lighting often expand services over time, adding diagnostic equipment, laboratory capacity, staff facilities, and digital health infrastructure, each of which increases electricity consumption. The study addresses this through deterministic scenario analysis, in which load growth is modeled as specific defined trajectories rather than as a single fixed assumption, alongside sensitivity analysis that examines how variations in key parameters propagate through to system performance and cost. This dual approach allows the researchers to identify which design choices remain robust across a range of plausible futures and which are fragile to particular assumptions.

The treatment of battery ageing deserves particular attention because storage is typically the most expensive and most performance-critical component in off-grid renewable systems. The researchers modeled degradation using a semi-empirical correction that links useful capacity and the timing of replacement to discharge depth, cycling frequency, and temperature. Semi-empirical models of this kind combine physically motivated relationships with parameters fitted to experimental data, offering a middle ground between simple calendar-life assumptions and fully mechanistic electrochemical models that are computationally expensive. In practical terms, the model captures the reality that lithium-ion cells degrade faster when routinely discharged to greater depths, when cycled more frequently, and when operated at elevated temperatures, the last of which is especially relevant in a hot climate such as Limpopo’s.

By tying replacement schedules to these degradation drivers, the analysis produces life-cycle cost estimates that reflect how the battery fleet actually ages rather than assuming a fixed lifetime regardless of operating conditions. This matters for the comparison between configurations: a system that cycles its batteries deeply every day will require earlier and more frequent replacements than one that operates them more gently, and these differences compound over a multi-decade project horizon. Transparent replacement scheduling also improves the usefulness of the results for budgeting purposes, since healthcare administrators and funders can see not only the initial capital requirement but the anticipated cadence of major component reinvestment.

The bidirectional converter, sized at 35 kW in both configurations, plays a quiet but essential role in these systems. It manages power flow in both directions, converting direct current from the photovoltaic array and battery into alternating current for clinic loads, and routing surplus generation back into storage. Converter sizing must accommodate the peak simultaneous demand of the facility while also handling charging currents, and an undersized converter becomes a bottleneck that forces unmet load even when generation and storage are nominally sufficient. The fact that the same 35 kW converter appears in both the least-cost and the stress-test configurations suggests that power conversion capacity is driven by the load profile rather than by generation mix.

The unmet load figure of 0.1% for the optimal design indicates that the system serves essentially all modeled demand, with shortfalls amounting to roughly one hour of missed supply per thousand hours of operation. For a healthcare facility, even this small residual can matter if it coincides with critical moments, which is why the reliability requirement was treated as an explicit constraint in the analysis rather than as an afterthought. The study’s approach of specifying healthcare reliability requirements as part of the optimization framework, rather than simply reporting reliability as an output, ensures that the least-cost design is selected from among configurations that already meet the clinical standard of service.

More broadly, the methodological structure of the study, combining hourly simulation, independent verification, scenario analysis, sensitivity testing, and semi-empirical degradation modeling, offers a template that can be adapted to other rural healthcare electrification projects. The specific numerical results are tied to the resource conditions and load characteristics of the Polokwane-area clinic, but the analytical workflow is transferable. Sites with different solar and wind profiles, different load trajectories, or different reliability requirements would yield different optimal designs, yet the process of comparing least-cost and resilient alternatives under demand uncertainty remains applicable. The authors’ emphasis on transparency, from the disclosure of resource data granularity to the explicit treatment of battery ageing, supports this transferability by making the assumptions visible and therefore contestable and adjustable by future practitioners.

The research also contributes to a growing body of work on energy access in health systems, where the reliability of electricity supply is increasingly recognized as a determinant of healthcare quality. Vaccine cooling in particular depends on uninterrupted refrigeration, and lapses in the cold chain can render entire vaccine stocks unusable, with public health consequences that extend well beyond the immediate cost of the lost doses. By quantifying the cost of achieving near-complete reliability at a rural clinic, the study provides decision-makers with concrete figures against which to weigh the consequences of underinvestment. The open access publication of the work further supports its potential utility, allowing practitioners, policymakers, and other researchers to examine the full analysis and adapt its framework to their own contexts without access barriers.

Subject of Research: …

Article Title: Least-cost sizing of PV-battery systems and resilience assessment of PV-wind-battery alternatives for rural healthcare under demand-growth scenarios in Limpopo South Africa

Article References: Least-cost sizing of PV-battery systems and resilience assessment of PV-wind-battery alternatives for rural healthcare under demand-growth scenarios in Limpopo South Africa. (n.d.). https://doi.org/10.1007/s43621-026-05044-1

Image Credits: AI Generated

DOI: 10.1007/s43621-026-05044-1

Keywords: Least-cost, sizing, PV-battery, systems, resilience, assessment, PV-wind-battery, alternatives, rural, healthcare, under, demand-growth