The analysis was based on operational data from the active manufacturing facility rather than a generic industrial load. Researchers used measured electricity demand, hourly and monthly load patterns, solar irradiation, wind conditions, fuel assumptions and other site information to model an off-grid microgrid. The plant operates throughout the year and uses electricity for raw-material preparation, pressing, cutting, slotting, painting and packing. Compressors, pressing units, boiler and chiller systems, and dust extraction were its largest consumers, while lighting and administrative loads made smaller contributions. Shiraz was selected as the case-study location because of its substantial solar resource and usable wind conditions, while wood residue generated by the factory was treated as a biogas feedstock.

The modeled system combined photovoltaic panels, wind turbines, a controllable biogas generator, batteries, power converters and a PEM electrolyzer with hydrogen storage. HOMER Pro version 3.14 evaluated feasible configurations at hourly intervals over a year and optimized them primarily for net present cost and levelized cost of energy. A cycle-charging strategy governed dispatch: renewable electricity first served the industrial load, then charged the battery once surplus power became available. After the battery reached its maximum state of charge, the electrolyzer could operate when excess power exceeded its minimum load ratio and remained below its rated capacity. Hydrogen was stored and sold rather than used inside the factory. Any electricity that could neither be stored nor converted was curtailed.

Without an electrolyzer, the economically strongest arrangement combined photovoltaics, one wind turbine, the biogas generator and a battery bank. It had a levelized cost of energy of $0.1130 per kilowatt-hour and a net present cost of $653,012.20, but 37.1% of its generated electricity went unused. Adding a wind turbine to a similar solar-biogas-battery case barely changed either cost or curtailment, indicating that the added wind contribution was limited under the modeled conditions. A wind-biogas-battery system reduced excess electricity to 10.4%, but its levelized cost rose to $0.1756 per kilowatt-hour. The poorest surplus-management outcomes occurred in configurations with 53.1% to 56% excess electricity, while the biogas-and-battery case eliminated surplus generation but depended heavily on fuel availability and carried a levelized cost of $0.2118 per kilowatt-hour.

Adding a 100-kilowatt PEM electrolyzer and hydrogen tank altered both the dispatch and the economics of the renewable systems. In the configuration with photovoltaics, wind, biogas and batteries, excess electricity fell from 37.1% to 9.36%, while annual hydrogen output reached 3,869 kilograms. Its net present cost increased to $919,084.80 and its levelized cost to $0.1590 per kilowatt-hour. Removing the wind turbine produced a closely matched solar-biogas-battery system: excess electricity declined slightly further to 9.19%, annual hydrogen production was 3,762 kilograms, and the net present cost was $919,874.30. A wind-biogas-battery configuration nearly eliminated curtailed electricity, at 0.0059%, but generated only 934 kilograms of hydrogen per year and had a levelized cost of $0.2216 per kilowatt-hour.

The hydrogen pathway also changed how the factory’s resources were used during operation. In the modeled solar-wind-biogas-battery case, photovoltaic output peaked at 272 kilowatts in summer and totaled 501,981 kilowatt-hours per year. The electrolyzer generally activated when solar output exceeded 150 kilowatts, averaging 20.5 kilowatts of input and 0.422 kilograms of hydrogen per operating hour. Because the electrolyzer absorbed power that otherwise would have been curtailed, the biogas generator concentrated its operation in nighttime and other periods of weak renewable availability. It averaged 110 kilowatts of electrical output and consumed an annual overall energy input of 184,375 kilowatt-hours. The authors interpret this flexible operation as a reduction in fuel use and generator wear, although the study did not test long-term component degradation.

An exergy analysis was used to distinguish the quantity of energy produced from the quality of useful work or chemical energy available in that energy. Across the electrolyzer-based scenarios, the solar-biogas-battery system selected by the multi-criteria ranking had an exergy efficiency of 39.22%, while the solar-wind-biogas-battery design reached 39.91%. Without the electrolyzer, the reported overall exergy efficiency was 37.72%. Photovoltaic conversion accounted for approximately 86% of exergy destruction in that baseline system, compared with about 13% for the biogas generator and 1% for the wind turbine. The electrolyzer itself introduced 2.46% of the modeled destruction, but redirecting surplus electricity into hydrogen increased the product exergy and raised the overall efficiency by about 2.2 percentage points.

The seasonal exergy results showed why maximum output cannot be treated as maximum thermodynamic performance. Solar irradiation and photovoltaic production increased toward summer, yet the gap between incident solar energy and available solar exergy widened as temperatures rose, reducing the potential for useful work. The wind turbine reached its highest electricity production in spring and summer but recorded higher exergy efficiency in autumn, indicating that stronger generation was accompanied by additional irreversibility. The electrolyzer produced the most hydrogen when electrical input was plentiful, but its highest exergy efficiency occurred in January, when output was comparatively low. The biogas generator showed only slight seasonal variation, making it less sensitive to changing ambient conditions than the solar and wind components.

Because cost alone could not distinguish configurations with different levels of curtailment, emissions and thermodynamic performance, the researchers combined an analytic hierarchy process with the Technique for Order of Preference by Similarity to Ideal Solution. The decision matrix weighed cost of energy at 30%, carbon dioxide emissions at 32%, excess electricity at 19% and exergy efficiency at 19%. The solar-biogas-battery-electrolyzer configuration ranked first, with a closeness coefficient of 0.65883, narrowly ahead of the solar-wind-biogas-battery system at 0.65765. Its estimated annual carbon dioxide emissions were 2.04 kilograms in the reported scenario table. The wind-biogas-battery system ranked third despite 58.52% exergy efficiency and almost no excess electricity, while the oversized solar-wind-battery configuration ranked fifth because its $0.3718 per kilowatt-hour cost and 31.95% exergy efficiency offset its zero reported emissions.

Sensitivity analysis showed that local solar availability had the strongest influence on both cost and emissions, whereas wind speed mattered more for emissions than for levelized cost because it could displace nighttime generator operation. Across the tested range of 3 to 7 kilowatt-hours per square meter per day of global horizontal irradiance, the reported levelized cost declined from $0.187 to $0.103 per kilowatt-hour. In a combined resource test, moving from 3 kilowatt-hours per square meter per day and a 2-metre-per-second wind speed to 7 kilowatt-hours per square meter per day and 6 metres per second corresponded to a cost reduction of 33% and an 87.5% reduction in carbon dioxide emissions. Raising photovoltaic capital costs had a larger effect than wind-turbine cost changes because the modeled system was centered on the solar array. These results remain model estimates for one industrial site and depend on the assumed $5 per kilogram hydrogen price, equipment costs, discount and operating assumptions, biomass fuel data, and the 20-year lives assumed for selected components such as the photovoltaic modules, wind turbine and electrolyzer; the authors recommend broader market testing and degradation models before wider deployment.

Subject of Research: Techno-economic and exergy assessment of a hydrogen-integrated renewable energy system for an industrial site in Shiraz, Iran.

Article Title: Energy-oriented design and comprehensive exergy-based evaluation of a hybrid renewable system with green hydrogen production from excess electricity for industrial energy demand

Article References: Mousavi, S. A., Abbasi, Y., Asadi, A., Safarpour, M., & Vaziri Rad, M. A. (2026). Energy-oriented design and comprehensive exergy-based evaluation of a hybrid renewable system with green hydrogen production from excess electricity for industrial energy demand. Results in Engineering, 32, Article 113583. https://doi.org/10.1016/j.rineng.2026.113583

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113583

Keywords: renewable energy, green hydrogen, industrial microgrid, PEM electrolyzer, exergy analysis, biogas, solar photovoltaic, wind power, battery storage, multi-criteria decision analysis, Energy-oriented, design

The first major contribution of the research is a fuzzy-adaptive maximum power point tracking algorithm. Traditional methods often struggle to maintain optimal efficiency when solar irradiance changes rapidly due to cloud cover or other atmospheric factors. The new algorithm dynamically adjusts its perturbation step based on the local sensitivity of power to voltage. This adaptive approach allows the system to track the maximum power point with greater accuracy and speed. In tests across fifteen randomized irradiance scenarios, the algorithm achieved a tracking efficiency of 98.1 percent, with a margin of error of only 0.1 percent. This performance significantly outperforms fixed-step methods, which averaged 95.0 percent efficiency, and linear-adaptive laws that required re-tuning for each specific scenario.

Statistical analysis confirms the superiority of the fuzzy-adaptive method. A Welch’s t-test comparing the fuzzy-adaptive algorithm against a re-tuned linear-adaptive law yielded a t-value of 5.2 with a p-value less than 0.0001. This indicates that the improvement in tracking efficiency is not due to chance but is a statistically significant result of the adaptive control strategy. The ability to maintain high efficiency with a single fixed parameter set is crucial for practical deployment, as it eliminates the need for complex recalibration procedures in the field. This robustness makes the technology more viable for widespread adoption in commercial charging stations where maintenance and tuning resources may be limited.

The second contribution focuses on the accurate estimation of the battery state of charge, a critical parameter for managing energy storage in charging stations. The researchers employed an extended Kalman filter to fuse data from Coulomb counting with a second-order Thevenin battery model. This fusion technique leverages the strengths of both methods to provide a more reliable and precise estimate of the remaining energy in the battery. The observability of this estimation pair was verified using a Popov–Belevitch–Hautus test, ensuring that the system’s internal states can be accurately inferred from its outputs. Across twenty Monte Carlo trials, this method reduced the mean root mean square error of the state of charge estimation from 2.45 percent to just 0.118 percent.

Such a dramatic reduction in estimation error has profound implications for the longevity and safety of battery storage systems. Accurate state of charge data allows the energy management system to avoid deep discharges or overcharging, both of which can degrade battery health over time. By maintaining the battery within its optimal operating range, the system can extend the lifespan of the storage units, reducing long-term operational costs. Furthermore, precise state of charge information enables the charging station to better predict its available capacity, allowing for more reliable service to electric vehicle users. This level of precision is essential for integrating battery storage into the grid, where accurate forecasting of available energy is required for grid stability and planning.

The third contribution involves the implementation of a model predictive control energy management system. This system replaces static, rule-based dispatch strategies with a receding-horizon optimization approach. Model predictive control allows the system to anticipate future energy demands and supply conditions, optimizing the flow of energy between the solar array, battery storage, grid, and electric vehicle in real-time. Under the assumption of a perfect forecast, this approach lowered the operating cost of the charging station by 6.8 percent. However, the study also reveals an explicit trade-off between cost optimization and grid independence. While the system can minimize costs by leveraging grid power when it is cheap, it can also prioritize solar and battery usage to reduce reliance on the grid, depending on the specific operational goals of the station.

The fourth contribution addresses the stability of the system’s power electronics. The researchers conducted a switched-system stability analysis, constructing mode-dependent Lyapunov functions to analyze the dynamics of the power converters. They derived an average-dwell-time bound of 2.31 milliseconds, which certifies the exponential stability of the linearized converter dynamics for any mode-switching sequence that respects this bound. This theoretical guarantee is crucial for ensuring that the system remains stable and does not oscillate or fail when switching between different operating modes, such as charging, discharging, or grid interaction. The use of Lyapunov stability theory provides a rigorous mathematical foundation for the control design, ensuring that the system behaves predictably and safely under all expected operating conditions.

When all components are integrated, the complete simulation of the charging station demonstrates a system efficiency of 93.6 percent. The DC bus voltage is regulated within a tight tolerance of plus or minus 5 percent, ensuring stable power delivery to the electric vehicle. The system achieves a grid dependency of 14.2 percent, indicating that the majority of the energy used for charging is sourced from the local solar array and battery storage. This high level of self-sufficiency reduces the load on the electrical grid and lowers the carbon footprint of the charging process. The ability to maintain high efficiency and stability while significantly reducing grid dependency makes this technology a promising candidate for sustainable urban energy infrastructure.

To validate the theoretical findings, the researchers implemented the fuzzy-adaptive MPPT and extended Kalman filter state of charge layers on an ESP8266 hardware prototype. The hardware-measured system efficiency was 91.2 percent, which agrees with the simulation result of 93.6 percent to within 2.4 percentage points. This close agreement between simulation and hardware testing provides strong evidence that the control algorithms are effective in a real-world environment. However, the study notes that the model predictive control dispatch results and the Lyapunov stability proof remain simulation-level findings. Hardware validation of these components is identified as a priority for future work, as it will be necessary to confirm that the theoretical stability guarantees hold in physical implementations with real-world noise and component variations.

This research represents a significant step forward in the development of smart, solar-integrated electric vehicle charging stations. By combining advanced control techniques with rigorous stability analysis, the authors have demonstrated a system that is both efficient and reliable. The ability to accurately track solar power, estimate battery state, and manage energy flow in real-time addresses key challenges in the integration of renewable energy with electric mobility. As the adoption of electric vehicles continues to grow, the need for efficient and sustainable charging infrastructure becomes increasingly important. This work provides a robust framework that can be adapted and scaled to meet the demands of a rapidly evolving energy landscape, contributing to the broader goal of decarbonizing the transportation sector.

Subject of Research: Control systems for solar-integrated electric vehicle charging stations

Article Title: Fuzzy-adaptive MPPT and Kalman-filter SOC estimation for Lyapunov-stable predictive energy management in solar-integrated EV charging stations

Article References: Santhoshkumar, R., Suresh, S., Peramanan, A., & Sathishkumar, P. (2026). Fuzzy-adaptive MPPT and Kalman-filter SOC estimation for Lyapunov-stable predictive energy management in solar-integrated EV charging stations. Scientific Reports. https://doi.org/10.1038/s41598-026-75396-1

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75396-1

Keywords: solar energy, electric vehicles, energy management, Kalman filter, fuzzy logic, battery storage, power electronics, model predictive control, stability analysis, renewable energy, Fuzzy-adaptive, MPPT

The plant, located in Bukuzindu village about 17 kilometers from Kalangala town, combines a 0.6-megawatt solar photovoltaic array with 1.0 megawatt of diesel generation, battery storage, and a distribution network stretching across the island. It was built at an estimated cost of 17 million US dollars and currently supplies more than 5,523 domestic customers and 67 commercial connections, including hotels and small enterprises. Its solar array consists of 2,832 modules rated at 235 watts peak each, split into units feeding grid-connected inverters and MPPT charge controllers, while a battery bank of two strings of 240 lead-acid cells, each rated at 686 ampere-hours at 2 volts, provides roughly 658.56 kilowatt-hours of storage.

The analysis revealed that the plant generated an average of 203.1 megawatt-hours of electricity per month, equivalent to roughly 2,436 megawatt-hours annually. Of that total, 174.1 megawatt-hours on average reached consumers, 22.4 megawatt-hours were lost in generation and transmission, 0.96 megawatt-hours were consumed by the plant’s own auxiliary equipment, and 5.4 megawatt-hours went into charging the battery bank. In other words, about 86 percent of everything the plant produced was actually delivered to the island’s homes and businesses, a figure the researchers consider acceptable for a system of this scale and configuration.

Beneath those headline numbers lies a more granular picture of how the two generation sources shared the burden. The solar array produced between 49.2 and 83.3 megawatt-hours per month, averaging 65.5 megawatt-hours, while the diesel generators ranged from 85.02 to 240.18 megawatt-hours, averaging 137.15 megawatt-hours. Solar’s monthly share of total generation swung from 23.67 percent in November 2020 to 86.12 percent in June 2020, with an average contribution of 49.69 percent. The researchers observed that solar’s contribution was inversely proportional to diesel output, exactly the complementary behavior a hybrid design is meant to achieve: when the sun does not deliver, the generators step in.

The demand pattern on the island explains much of this balance. Most consumers are residential households, so electricity demand peaks in the evening after sunset, precisely when solar output falls to zero. The diesel generators therefore carry the evening load, while the solar array covers daytime demand. Notably, the variability coefficient, the ratio of standard deviation to average output, was 10.29 percent for solar, 18.48 percent for diesel, and just 11.57 percent for the combined hybrid system, indicating that the plant as a whole delivered remarkably stable output across the study period. That stability matters for forecasting and for planning future expansion on an island whose electricity demand continues to grow.

The study also documented a striking demand spike in November 2020, when transformer-metered energy peaked. The authors attribute this partly to Uganda easing COVID-19 restrictions on 9 November 2020, which allowed businesses, particularly in the tourism sector that anchors the island’s economy, to resume operations. The plant manager also reported that a palm oil processing factory requested continuous supply after its own steam turbine broke down, though the researchers note there is no publicly available evidence confirming that event drove the increase.

On the technical performance side, the solar array achieved an annual average performance ratio of 65.6 percent, meaning roughly two-thirds of the theoretical energy available from incident sunlight was converted into useful electricity after all system losses. Its capacity factor stood at 13.4 percent, constrained by the fundamental intermittency of solar resources: generation is limited to daylight hours and further shaped by cloud cover, seasonal variation, module temperature, and system losses. The diesel generators posted a capacity factor of 18.88 percent, reflecting their role as a dispatchable backup rather than the primary energy source, an operating strategy that reduces fuel consumption, cuts emissions, and extends generator lifetime. The mini-grid as a whole achieved a capacity factor of 17.39 percent.

Losses emerged as a key finding with direct policy implications. The combined generation and transmission loss averaged 11.03 percent, ranging from 8.66 percent in November to 13.21 percent in February. Breaking this down, generation losses averaged 3.13 percent while distribution losses averaged 7.90 percent. The distribution figure is notably efficient compared with Uganda’s national grid, which recorded average distribution losses of 17.74 percent between 2021 and 2025. However, the mini-grid’s transmission line loss of 7.9 percent was nearly double the national grid’s average transmission loss of 4.42 percent over the same period, and the researchers trace this to a structural mismatch: the island’s 33-kilovolt network stretches approximately 144 kilometers, while published studies suggest 33 kV lines are typically suitable for distances of roughly 30 to 60 kilometers. As demand grows, higher loading on the low-voltage transmission line drives up current flow and, with it, resistive losses.

The authors recommend that the Ugandan government and electricity-sector stakeholders conduct a detailed feasibility study to upgrade the island’s principal transmission network from 33 kV to 66 kV, supported by load-flow, voltage-drop, fault-level, and economic analyses. Raising the transmission voltage would reduce the current needed for a given power transfer, cutting resistive losses, improving voltage regulation, and providing headroom for the island’s projected demand growth. They frame this not as a quick fix for current loss levels but as part of a long-term transmission reinforcement strategy to keep the island’s infrastructure reliable as its economy expands.

Compared with other systems, the Kalangala plant holds its own. Its performance ratio and capacity factor exceed those of an off-grid mini-grid studied in southern Ethiopia, which achieved 42 percent and 13.0 percent respectively, though they fall short of Uganda’s own Soroti solar plant, which recorded a performance ratio of 75.84 percent and a capacity factor of 19.07 percent. The researchers attribute the gap partly to lower average daily solar radiation at Kalangala, 4.94 kilowatt-hours per square meter per day versus 5.85 at Soroti, and partly to dust accumulation from the plant’s proximity to a busy road, inverter conversion losses, battery storage losses, and additional power electronics losses inherent in the hybrid configuration. The study’s limitations include the inability to assess the levelized cost of electricity or payback period, since the plant’s distribution tariff has only been declared through the first quarter of 2022. The authors call for updated tariff data and further techno-economic analyses of hybrid renewable energy systems across Uganda, arguing that such evidence is essential for planning, designing, and scaling the decentralized systems that will carry the country toward its Vision 2040 target of 52.4 gigawatts of installed capacity, with solar photovoltaics expected to supply roughly 12 gigawatts of that total.

Subject of Research: Technical performance evaluation of a solar PV-diesel-battery hybrid mini-grid in Kalangala, Uganda

Article Title: Performance of a solar PV-diesel generator-battery mini-grid using actual data: a case of Kalangala power plant in Uganda

Article References: Oloya, I. T., & Adaramola, M. S. (2026). Performance of a solar PV-diesel generator-battery mini-grid using actual data: a case of Kalangala power plant in Uganda. Frontiers in Energy Research, 14, Article 1785084. https://doi.org/10.3389/fenrg.2026.1785084

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1785084

Keywords: solar PV, mini-grid, hybrid energy system, Uganda, rural electrification, performance ratio, capacity factor, transmission losses, battery storage, diesel generator, Kalangala, renewable energy

The research addresses one of the most pressing coupled challenges of our era: the simultaneous scarcity of freshwater and electricity in arid and semi-arid regions such as the Middle East, North Africa, and parts of South Asia. Rapid population growth, limited natural water resources, and rising energy demand have intensified the need for solutions that tackle both supplies at once. While solar and wind power have become increasingly affordable, each technology on its own suffers from intermittency — photovoltaic generation vanishes after sunset and dips under cloud cover, while wind output depends on fluctuating wind speeds. The study exploits the complementary behavior of the two resources: winds in Zabol often blow strongest at night and during the region’s famous 120-day wind season, precisely when solar panels produce nothing. What is a liability for a single technology becomes a synergy advantage for a hybrid configuration.

The case study is a 23-story residential building with 161 apartments housing 408 residents, whose electricity demand was simulated hour by hour across a full year — 8,760 hours — using meteorological data for Zabol, including solar irradiance, ambient temperature, and wind speed profiles. Summer dry-bulb temperatures in the city reach 43.1 degrees Celsius, driving heavy cooling loads, while its proximity to the Oman Sea makes seawater desalination a plausible water source. Two system configurations were compared. Scenario A consists of photovoltaic panels, a wind turbine, and battery storage supplying only the building’s electrical load. Scenario B extends this by integrating a reverse osmosis desalination unit that converts excess renewable generation into freshwater for the occupants.

The technical modeling is notably detailed. The photovoltaic unit was simulated with a five-layer thermal model — glass, two ethylene vinyl acetate layers, silicon, and a backsheet — solving coupled energy balance equations to capture how cell temperature degrades power output. Each 580-watt panel operates at a reference efficiency of 22.47 percent with a temperature coefficient of 0.3 percent per degree Celsius, a critical consideration in a city where summer heat is punishing. Wind turbine output was calculated from hub-height wind speeds adjusted from reference measurements, with cut-in, rated, and cut-out velocities of 3, 12, and 25 meters per second respectively. The battery model accounts for self-discharge, an 85 percent charging efficiency, and a 91.55 percent inverter efficiency, while the reverse osmosis model tracks feed-water pumping power, concentrate disposal energy, and the recovery ratio that determines how much freshwater each cubic meter of seawater yields.

Optimization was performed with the Non-dominated Sorting Genetic Algorithm II, or NSGA-II, a widely used evolutionary algorithm that generates a diverse set of Pareto-optimal solutions when objectives conflict. Here, three objectives were minimized simultaneously: the levelized cost of electricity, the levelized cost of water, and the carbon footprint, expressed in kilograms of carbon dioxide equivalent using life-cycle emission factors for each component. Notably, battery storage dominates the environmental accounting at 64.30 kilograms of CO2-equivalent per kilowatt-hour of capacity, dwarfing the 0.08 for photovoltaics and 0.01 for wind. The algorithm ran with a population of 200 over 500 generations, and decision variables included the number of photovoltaic modules, wind turbines, battery capacity, and reverse osmosis capacity, subject to the strict constraint that both electricity and freshwater demand be fully satisfied at all times.

What distinguishes this study from prior work is its decision-making layer. Rather than assuming fixed priorities, the researchers applied the Technique for Order of Preference by Similarity to Ideal Solution — TOPSIS — under multiple weighting schemes reflecting different stakeholder preferences. For the electricity-only system, three weighting cases traded off cost against carbon; for the integrated system, four cases distributed priorities among electricity cost, water cost, and environmental impact. The results show that these weights directly reshape the optimal configuration: prioritizing cost objectives drives larger photovoltaic arrays and lower levelized costs, while environmental prioritization steers the solution toward smaller carbon footprints. Battery capacities across the optimized cases ranged from roughly 3,200 to 3,630 kilowatt-hours, and reverse osmosis capacities from about 203 to 231 cubic meters.

The generation profiles reveal why the hybrid design works so well in this location. On a representative July day, wind generation held near maximum throughout all 24 hours, delivering 144 kilowatt-hours in the electricity-only scenario. Photovoltaic output, by contrast, follows a predictable bell-shaped curve, peaking around solar noon — on a representative February day it reached 640 kilowatt-hours at 13:00, aided by strong irradiance and cooler temperatures. Combined daily production peaked at 8,053 kilowatt-hours in the integrated scenario during the mid-year months, when maximum solar irradiance constructively overlaps with the 120-day winds, and fell to roughly 1,540 to 1,674 kilowatt-hours at the year’s edges. Monthly totals in the integrated scenario climbed as high as 233,955 kilowatt-hours in August against a peak demand of 116,214 kilowatt-hours in July.

The battery management strategy is deliberately conservative. Stored energy builds during daylight hours and discharges overnight, but the energy management system restricts depth of discharge to no more than 20 percent, protecting battery health and extending service life — a meaningful consideration given that storage is both the most carbon-intensive and one of the costlier components at 200 dollars per kilowatt-hour. During a May morning in the electricity-only scenario, stored energy fell to 1,338 kilowatt-hours after a windless, sunless stretch, while on a September day in the integrated scenario the battery sustained a steady 3,390-kilowatt-hour plateau from 10:00 to 17:00, buoyed by late-summer irradiance and consistent wind inflow.

The economics tell the most compelling story. Because the system must be sized to reliably cover the lean winter period, summer generation in the electricity-only scenario vastly outstrips demand, leaving substantial surplus energy unutilized. Integrating reverse osmosis absorbs much of that summer spike, converting potential curtailment into freshwater and improving overall system utilization — which is why the integrated scenario achieves a lower levelized cost of electricity despite requiring more total renewable capacity, with a larger photovoltaic array but a smaller wind turbine fleet. Water costs respond strongly to weighting choices, with the lowest levelized costs of water appearing when that objective is prioritized in the decision matrix. The carbon footprint, meanwhile, rises in the integrated scenario because the desalination plant adds embodied and operational emissions, but environmental weighting demonstrably pushes the optimizer toward lower-emission designs.

The authors acknowledge limitations: the model assumes ideal component availability, neglects degradation of batteries, panels, and reverse osmosis membranes, and reflects a single location and building type. Future work includes degradation modeling, Monte Carlo uncertainty analysis for weather and prices, validation across other climates and building types, and exploration of hydrogen or biomass options for added flexibility. Still, the framework itself is deliberately generic — the mathematical models and optimization structure are site-independent and transferable to any water-stressed region by updating local resource and demand data. For the growing number of communities where the water tower and the power grid are one and the same problem, the message is striking: the surplus energy you would otherwise throw away may be exactly what your taps need.

Subject of Research: Multi-objective optimization of an off-grid hybrid renewable energy and reverse osmosis desalination system

Article Title: Multi-objective optimization of an off-grid photovoltaic-wind turbine-battery-reverse osmosis system using a water-energy-economy-environment nexus approach

Article References: Morasa, M. K., Shahverdian, M. H., & Sayyaadi, H. (2026). Multi-objective optimization of an off-grid photovoltaic-wind turbine-battery-reverse osmosis system using a water-energy-economy-environment nexus approach. Results in Engineering, 32, Article 113243. https://doi.org/10.1016/j.rineng.2026.113243

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113243

Keywords: photovoltaics, wind turbine, battery storage, reverse osmosis, desalination, NSGA-II, TOPSIS, levelized cost of electricity, water-energy nexus, carbon footprint, off-grid power, Zabol Iran

The study, authored by Riad Mollik Babu, introduces the concept of a grid-unreliability regime, a statistical description of the interruption process captured by five independent quantities: the fraction of hours per year the grid is down, the mean duration of each outage, the variability of those durations, the predictability of outages in advance, and the timing bias of outages relative to solar availability. Rather than optimizing a single installation under one assumed condition, the analysis swept this five-dimensional space across 1,536 sampled regime points, sizing a distributed photovoltaic and battery system at each one against three objectives: net present cost, expected unserved critical energy, and life-cycle carbon intensity. The work is anchored to Dhaka, Bangladesh, where recurring load shedding exemplifies chronic grid unreliability.

To make results comparable across sites, the designs were expressed as two dimensionless ratios: a storage ratio, defined as battery energy per unit of daily critical load, and a PV ratio, defined as daily solar energy per unit of daily critical load. Outages were generated by an alternating-renewal process with durations drawn from a Gamma distribution, which allows the mean and the spread of outage durations to be set independently, a flexibility the conventional two-state Markov model lacks because it fixes the duration variability at one. A rule-based rolling-horizon controller operated each simulated year at hourly resolution, with predictability entering as the fraction of outage events revealed to the controller before they occur.

The variance decomposition, computed with second-order Sobol indices on the mean of five replicated optimizer runs, produced a striking hierarchy. The outage time fraction dominates both design ratios, with a total-order Sobol index of roughly 0.58 for each. Mean outage duration and duration variability follow. For the storage ratio, the response is almost purely additive: how much, how long, and how variably the grid is out together explain nearly all of the variance. Notably, the storage requirement grows fastest at low outage fractions, meaning the biggest design sensitivity sits in mildly unreliable grids rather than the most severe ones, a regime of diminishing structural return as outages intensify.

The most surprising result concerns predictability. Intuition would place advance knowledge of outages on the storage decision, since a controller that knows an outage is coming can pre-charge the battery. The evidence moves it almost entirely off that decision. Once residual stochastic variance from the optimizer and the synthetic outage years was removed, predictability’s total-order index on the storage ratio fell to about 0.02. What actually lowers the required PV capacity is not foresight but permission: the simple ability to charge the battery from the grid between outages. A blanket pre-charging policy holding no outage information whatsoever captured 87 to 109 percent of the PV reduction achieved by perfect foresight, and the paired difference between the two was not statistically resolved in any tested regime.

Predictability does matter, but through a different channel. At a fixed charging rule, moving from uninformed blanket pre-charging to full foresight cut annual imported grid energy by 67 percent in the severe regime and lowered life-cycle carbon intensity by 45 percent, from 145.6 to 79.6 grams of CO2-equivalent per kilowatt-hour. The advantage shrinks where outages fall after dark, because evening interruptions carry little solar overlap and the battery must be filled largely from the grid whether or not the controller anticipates them. Foresight, in other words, governs the efficiency of grid charging, not the size of the array.

The study also quantified the price of reliability. Median net present cost rose 51 percent from a 95 percent to a 99 percent critical-load availability target, and a further 32 percent to reach 99.9 percent. Every one of the 1,536 sampled regimes proved feasible even at 99.9 percent energy-based availability, demonstrating that chronic load shedding does not impose the survival limit of a rare multi-day outage. The framework was benchmarked against conventional sizing rules with sobering results: sizing the same Dhaka consumer as though the grid were absent inflated cost by 103 to 185 percent, while a days-of-autonomy rule overshot by 156 to 270 percent, because both ignore the energy the grid delivers between interruptions.

Robustness was tested extensively. The design rules transferred across a second solar year, an alternative double-peaked load profile, and two other cities, Karachi and Lagos, with the mean PV ratio moving by under 4 percent and the sensitivity structure statistically indistinguishable between sites. Correlated regime combinations, imperfect forecasts, and narrowed parameter ranges were also examined. When forecast skill degraded from perfect to useless, systems whose pre-charging depended on announced schedules saw PV requirements balloon by 68 to 196 percent, because the controller stopped importing and reverted to solar-only operation. The robust policy is to permit grid charging unconditionally and use any published schedule only to reduce imported energy.

The practical implications reach beyond engineering. Metering or tariff rules that block grid charging of home batteries quietly raise the capacity customers must install, while a reliable load-shedding schedule does not shrink that capacity but does cut the grid energy and emissions needed to hold a given reliability level. Both benefits remain invisible to design methods that treat predictability as a storage-side parameter. By reducing grid unreliability to five measurable quantities and showing how each shapes the cost-optimal design, the study offers a transferable playbook for the billions of people whose grids are present but perpetually imperfect.

Subject of Research: Optimal sizing of distributed PV and battery systems under chronic grid unreliability regimes

Article Title: Resilience value of distributed PV and battery systems under grid-unreliability regimes

Article References: Babu, R. M. (2026). Resilience value of distributed PV and battery systems under grid-unreliability regimes. Results in Engineering, 32, Article 113342. https://doi.org/10.1016/j.rineng.2026.113342

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DOI: 10.1016/j.rineng.2026.113342

Keywords: distributed PV, battery storage, grid unreliability, load shedding, resilience, Sobol sensitivity analysis, multi-objective optimization, Dhaka, energy access, predictability, techno-economic sizing, life-cycle carbon