Journal of Energy Research and Reviews

Renewable Energy for Sustainable Irrigation and Agricultural Production: Technologies, Trade-offs and Pathways for Integrated Deployment

2026-09-02T12:11:56+00:00

Agricultural production depends on reliable energy for water lifting, pressurised irrigation, protected cultivation and other farm operations, yet conventional diesel and electricity supply can expose producers to fuel-price volatility, unreliable service and greenhouse-gas emissions. Renewable energy offers a credible route to reduce these constraints, but technology substitution alone does not ensure sustainability. This critical narrative review evaluates renewable energy technologies for irrigation and agricultural production, with particular emphasis on solar photovoltaic pumping, wind and hybrid pumping, bioenergy, agrivoltaics, and renewable thermal energy for greenhouses. The literature was selected from open scholarly indexes and authoritative institutional sources and critically appraised for technical validity, economic assumptions, environmental boundaries, water-resource implications and implementation context. The evidence is strongest for solar photovoltaic pumping, whose modularity, low operating expenditure and alignment with daytime irrigation demand make it attractive across off-grid and weak-grid settings. Its principal limitation is not energy conversion but system integration: inappropriate pump sizing, weak maintenance, financing barriers and the removal of marginal pumping costs can undermine both reliability and groundwater sustainability. Evidence from India and Bangladesh demonstrates that groundwater outcomes are institutionally mediated rather than technologically predetermined; ownership, water pricing, irrigation service models and crop choice strongly shape rebound effects. Wind, biomass and hybrid systems can be effective where local resources justify them, but their comparative advantages are more site-specific. Agrivoltaic evidence indicates potential land- and water-use synergies, particularly in hot and water-limited environments, while crop-yield responses remain heterogeneous and sensitive to shading geometry. Renewable greenhouse heating and cooling can reduce fossil-energy dependence, although long-term commercial evidence is thinner than engineering studies suggest. Sustainable deployment therefore requires integrated design across water availability, hydraulic demand, renewable resources, finance, lifecycle impacts, farmer capability and governance. Future research should prioritise multi-season field trials, basin-aware groundwater accounting, harmonised lifecycle assessment and business models that preserve incentives for water conservation while broadening smallholder access.

Biophysical Economics of Uganda: A Comprehensive Review of Energy Resources and Sustainable Development Pathways

2026-09-18T09:33:49+00:00

Uganda is a nation endowed with an estimated 52,000 MW of energy potential across seven distinct resource categories, yet more than half of its population lacks access to electricity, and nearly 90% of national energy consumption derives from traditional biomass. This review surveys Uganda’s full energy resource matrix—hydroelectricity, biomass, solar, geothermal, wind, fossil fuels, and nuclear—alongside the mineral resource base that underpins energy infrastructure development. The analysis is organised through a three-dimensional evaluation framework encompassing resource endowment, infrastructure and governance capacity, and socio-economic demand alignment. The review finds that Uganda’s energy poverty is not due to a deficit of natural resources but to a failure of development capacity and institutional coordination across all three dimensions. The progressive layering of governance fragmentation, ageing grid infrastructure with 22.8% transmission and distribution losses, and a critical shortage of trained energy professionals forms a closed-loop bottleneck that blocks investment even where resource potential is high. Uganda’s Vision 2040 targets of expanding installed capacity to 51,200 MW and increasing per capita electricity consumption to 3,668 kWh are ambitious but achievable only if policy interventions address all three dimensions simultaneously, with governance reform serving as the prerequisite for unlocking investment in both infrastructure and demand-side interventions. The review identifies a critical research gap in quantitative biophysical-economic modelling applied to the Ugandan context and proposes that biophysical economics offers a productive analytical lens for future energy-policy evaluation in comparable sub-Saharan African settings.

Multi-Objective Particle Swarm Tuning of Dual-Active-Bridge Control for Bidirectional Wireless Vehicle-to-Grid Interfaces: A Critical Review of Concurrent Harmonic, Switching-Loss and Dynamic-Response Optimisation

2026-09-29T14:00:57+00:00

Bidirectional wireless vehicle-to-grid interfaces combine a loosely coupled inductive link with high-frequency bridges on both the infrastructure and the vehicle side, producing a converter that is structurally equivalent to a dual-active-bridge operating through a resonant network with a variable, load-dependent coupling coefficient. Control of such an interface must satisfy several requirements at once: grid and coupler current waveforms must remain within harmonic limits, conduction and switching losses must stay low enough to justify the thermal and economic penalty of wireless transfer, and the power-flow reversal that defines vehicle-to-grid service must be executed without excessive transient excursions. These requirements are not independent, and the modulation freedoms that improve one frequently degrade another. Multi-objective particle swarm optimisation has been proposed as a tuning framework capable of exposing the trade-off surface rather than concealing it inside a weighted scalar cost, yet its application to this specific converter class remains fragmentary. This review examines how total harmonic distortion, switching loss and dynamic response have been formulated, optimised and validated in the dual-active-bridge and bidirectional inductive power transfer literature, and assesses the extent to which population-based multi-objective tuning has actually delivered on its stated promise. Literature retrieved through open scholarly indexes to 21 July 2026 was appraised for methodological adequacy, evidential strength and the fidelity of the models used inside the optimisation loop. The evidence supports three conclusions: objective conflict is real and is repeatedly demonstrated in single-objective studies that report collateral degradation; harmonic distortion is defined inconsistently and is frequently omitted from the optimisation vector altogether; and dynamic response is the least well represented objective, usually assessed after the fact rather than optimised concurrently. Confidence in reported improvements is limited by low-fidelity loss models, simulation-dominant validation and the near-absence of coupling-variation robustness testing. Research priorities are identified accordingly.

Techno-Economic Assessment of a Solar PV–Battery–Diesel Hybrid Microgrid for Enhancing Energy Management in The Andaman and Nicobar Islands

2026-09-05T12:45:08+00:00

The Andaman and Nicobar Islands face significant energy-management challenges due to geographical isolation, limited grid connectivity, high fuel transportation costs, and continued dependence on diesel-based electricity generation. This study assesses the technical reliability and economic viability of a solar PV–battery–diesel hybrid microgrid for enhancing energy management in the Andaman and Nicobar Islands. The study adopts a quantitative, simulation-based techno-economic approach using secondary energy data compiled from the World Development Indicators (WDI), Energy Statistics India published by the Ministry of Statistics and Programme Implementation (MoSPI), Government of India, and supporting technical sources. HOMER Pro is employed to simulate and optimise the hybrid configuration, while MATLAB is used for supplementary data processing, statistical comparison, and hypothesis testing. The modelling framework assumes solar PV as the principal renewable source, battery storage to balance generation and demand, and diesel generation as backup during renewable and storage shortfalls. Technical performance is evaluated through annual electricity generation, renewable fraction, battery autonomy, outage hours, diesel consumption, and CO₂ emissions, while economic performance is assessed using Net Present Cost (NPC), Levelised Cost of Energy (LCOE), operating cost, and payback period. The results indicate that the hybrid system reduces annual outage hours by 77.4%, achieves a renewable fraction of 48%, reduces diesel consumption by 56.5%, and lowers CO₂ emissions by 54.3%. NPC decreases by 36.5%, while LCOE declines from ₹32.4/kWh to ₹18.7/kWh. Sensitivity analysis further evaluates variations in diesel prices, battery costs, and solar-resource conditions. The findings indicate that, under the evaluated modelling conditions, the solar PV–battery–diesel hybrid microgrid can improve supply reliability, increase renewable-energy utilisation, reduce diesel dependence and emissions, and enhance lifecycle economic performance, providing useful evidence for sustainable energy planning in remote island communities.

Psychosocial Impact of Irregular Electricity Power Supply: A Case Study of Nigeria

2026-09-08T11:24:25+00:00

Aim: The aim of the study was to evaluate the psychosocial, economic, and health impacts of prolonged power outages in Nigeria.

Study Design: A convenience sample was used for the study.

Materials and Methods: A semi-structured questionnaire was used to explore the psychosocial effects of electricity power outages in Nigeria. The questionnaire was exported to Google Forms for responses from Nigerians living within the country. NCSS 2020 statistical software was used for the analysis.

Results: The mean (±SD) age of respondents was 26.0 (12.3) years. Most respondents were in the 20-29-year age group (94, 51.1%), were female (117, 63.6%), were students (103, 56.0%), were mainly from the Southwest zone (162, 88.4%), lived in urban residences (112, 60.9%), and had residences with electricity supply (169, 91.8%). The mean duration of sleep (hours) without electricity supply (4.77±1.83) was significantly shorter (t-test=12.41, P-value<0.00001) than when electricity was supplied (6.48±1.53). Overall, 67.9% of respondents incurred various losses as a consequence of electricity power outages. Approximately 31% of respondents reported a continuous power-outage duration of 72-96 hours, most frequently among urban residents (35.1%). Heat was the major factor (85.3%) affecting respondents during power outages, while 70.7% of respondents felt frustrated by the power outages and 62.0% strongly agreed that their productivity at work was reduced during power outages.

Conclusion: Irregular and erratic power outages (PO), which may last up to 96 hours or more, severely impact people's livelihoods. Sleep duration is reduced, and productivity at work or school is severely affected. Heat was the factor that made respondents most uncomfortable during power outages in Nigeria's hot environment, and air pollution from generator fumes compounds the problem.

Offshore Wind Energy Generation from Vertical Axis Wind Turbines (VAWT) in Guyana

2026-09-11T12:05:06+00:00

Background: Offshore wind energy may provide a complementary renewable-energy option for Guyana, particularly in areas with substantial offshore industrial activity.

Aims: To use offshore wind data collected from a ship offshore Guyana to estimate potential energy generation using vertical axis wind turbines (VAWTs).

Place and Duration of Study: University of the West Indies, Mechanical and Manufacturing Engineering, St Augustine Campus. Data were collected from the Stena Carron drillship offshore Guyana from January 2025 to December 2025.

Methodology: Offshore Guyana was chosen as the study area because of the availability of wind data and the high level of offshore activity. The data were collected offshore Guyana in the oil and gas fields leased by ExxonMobil Guyana. They were gathered at different offshore sites, depending on the area in which the exploration well was being drilled. The recorded wind speeds varied throughout the year. The highest monthly average of 10.38 m/s was observed in February 2025, whilst the lowest monthly average was 5.71 m/s in July 2025. The wind turbine used for the modelling had three MI-VAWT1 blade profiles, a blade length of 10 metres, and a diameter of 9 metres. These physical characteristics were chosen to limit the turbine footprint whilst still producing sufficient power. The Paraschivoiu double streamtube model was used for turbine modelling.

Results: The results indicate that offshore Guyana has considerable potential as a renewable wind resource. The modelled VAWT produced daily power outputs ranging from 72.75 kWh to 437.13 kWh. The highest monthly power output corresponded to the highest mean monthly wind speed and occurred in February. Installing multiple wind turbines could produce clean energy that may complement the high level of activity currently occurring offshore Guyana. The use of this wind resource could support Guyana's rapidly growing economy in a sustainable manner by diversifying its energy dependence from oil and gas towards an alternative clean resource.

Conclusion: The vertical axis wind turbine modelled in this study showed that it could generate an average daily power output of 203.42 kWh over the year. A VAWT array would yield higher power output but would require a larger footprint.

AI/ML-Assisted Inverse Alloy Design of Multi-Principal Element Alloys for High-Temperature Energy Systems: Comparative Tree Ensembles and Deep Neural-Network Surrogates

2026-09-14T09:49:34+00:00

High-temperature energy systems require structural materials that retain strength while preserving sufficient deformation capacity under thermal exposure. The compositional design space of multi-principal element alloys (MPEAs) is, however, too large for exhaustive trial-and-error exploration. This work develops a reproducible artificial-intelligence/machine-learning (AI/ML) workflow for forward property prediction and inverse alloy design of MPEAs using the expanded experimental database reported by Borg et al. The original database contains 1,545 literature-derived records; the present analysis isolates tensile data and represents alloy chemistry through normalised elemental atomic fractions together with test temperature, grain size, processing route, microstructure, phase class, and composition-derived descriptors. Four supervised regressors were compared: Random Forest (RF), Extremely Randomized Trees (Extra Trees), Extreme Gradient Boosting (XGBoost), and a feed-forward deep neural network (DNN). Leakage was reduced by splitting data by normalised alloy composition rather than by random row. Independent surrogates were trained for yield strength and elongation. Under the verified grouped holdout configuration, the best yield-strength result was obtained by RF (R² = 0.164, MAE = 173 MPa, RMSE = 251 MPa), whereas XGBoost gave the highest elongation R² (0.292, MAE = 15.21%, RMSE = 20.63%) and the DNN gave the lowest elongation MAE (14.58%). The modest R² values demonstrate that heterogeneous literature data contain substantial variance not captured by nominal chemistry and coarse processing descriptors. Nevertheless, surrogate-assisted screening at T ≥ 500 ◦C using target constraints of predicted yield strength ≥ 500 MPa and predicted elongation ≥ 15% identified a literature-supported Al-Co-Cr-Fe- Ni-Ti composition at 700 ◦C as the highest-ranked condition, with predicted yield strength of 521.7 MPa and predicted elongation of 15.72%. The study therefore positions AI/ML not as a replacement for experiment, but as a transparent decision-support layer for narrowing high-dimensional alloy spaces and prioritising candidates for experimental validation in high-temperature energy applications.

Effect of Solar Incidence Angle on Photovoltaic Module Power Output: An Experimental Study in Afikpo, Nigeria

2026-09-16T11:25:44+00:00

Aims: This study examined the effect of solar incident angle on the power output of a single 350 W photovoltaic (PV) module in Afikpo, Ebonyi State, Nigeria.

Study Design: The study employed an experimental research design to determine the influence of solar incident angle on the power output of a single 350 W PV panel. This approach was adopted because the incident angle was varied while the corresponding electrical parameters were measured under the prevailing environmental conditions.

Place and Duration: The study was carried out in Afikpo, Ebonyi State, Nigeria, over eight months, comprising January, February, March, April, May, June, November, and December 2025.

Methodology: The measuring instruments used were an arc for adjusting the slope angle, a voltmeter (Alda Model AV 890C digital multimeter) for measuring voltage, and a lux meter for measuring light intensity. A single 350 W PV panel was mounted on an adjustable table to change its orientation, thereby varying the incident angle, while the corresponding effects on current and voltage were measured and recorded.

Results: The experimental results showed that optimum power output values varied among the sampled months as the incident angle changed under different environmental conditions. The optimum power outputs reported for the selected months were 32.88 W, 40.77 W, 29.92 W, 29.92 W, and 27.24 W at incident angles of 40°, 0°, 0°, 0°, and 20°, respectively. The experimental results also showed that the PV panel produced its highest and most consistent power outputs when the incident angle was maintained between 0° and 40°, with an average optimum angle of approximately 30°.

Conclusion: The study therefore shows that incident angle is an important parameter in PV power performance and provides an experimental basis for developing mathematical and machine-learning models capable of predicting power output and identifying appropriate panel orientations under varying conditions.

Laboratory Production of Biogas from Apple Pomace

2026-09-28T12:10:58+00:00

Apple processing generates substantial quantities of pomace waste, comprising approximately 25–30% of the processed fruit dry mass. This inexpensive, non-food-competing residue retains a substantial fermentable carbohydrate content, making it an attractive feedstock for biogas production through anaerobic digestion. This study investigated the effect of total solids (TS) concentration on biogas yield from apple cider pomace (APC) combined with cow manure inoculum (CM), using a laboratory benchtop anaerobic fermentation (LBAF) system. APC/CM mixtures were prepared at three TS loadings—1.5%, 2%, and 3%—and digested in duplicate at 39°C ± 2°C. Cumulative biogas volume was measured over time using a barrier-fluid displacement method.

Biogas production followed a sigmoidal trend, with an initial lag phase (0–20 h), a steep exponential phase (approximately 20–60 h), and a subsequent plateau as substrate became limiting. The 2% TS condition yielded the highest cumulative biogas volumes and the greatest biogas fraction measured after CO₂ removal, reaching approximately 240–298 mL over approximately 108 h, compared with 104–182 mL for 1.5% TS and 70–77 mL for 3% TS. The lower yield at 3% TS suggests possible substrate overloading that inhibited methanogenic activity, whereas the intermediate performance at 1.5% TS and higher yield at 2% TS indicate that 2% TS was the more favourable loading under the tested conditions. Notable variability was observed between duplicate reactors, particularly at 1.5% and 2% TS, possibly because of inoculum heterogeneity or inconsistent feedstock particle size, whereas the 3% TS replicates showed closer agreement.

These findings support the potential of apple pomace as a biogas feedstock and indicate that 2% TS was the more favourable loading under the tested conditions. Future work should address feedstock homogenisation, extend trial durations to allow direct yield comparisons, and characterise methane content to better evaluate process efficiency.