Solar photovoltaic (PV) forecasting supports secure grid operation, yet advanced machine learning models are hard to trust when their decisions are opaque. This PRISMA 2020 guided systematic review examines explainable artificial intelligence (XAI) in solar forecasting. Of 951 retrieved records, 173 met strict eligibility, requiring quantitative forecasting evaluation plus an explicit explanation output. A sensitivity analysis of the decisive screening rule, which excluded 299 records using attention only as an architectural component, supported its precision, and dual independent re-screening of 75 excluded records found no false exclusions. Long short-term memory (LSTM) models dominated (48 studies), ahead of transformers (39) and convolutional neural networks (CNN, 32) used mainly for image-based nowcasting. SHAP prevailed among explainers (69 studies), ahead of feature importance and permutation methods (39), attention visualization (31), and LIME (14), and 47 studies combined several methods. Quality-stratified synthesis showed these rankings are robust, with the ordering unchanged among the 114 high-quality studies. Irradiance ranked first across targets, while temporal encodings and ambient temperature were stable secondary contributors. Humidity, wind, and cloud proxies shifted with season and climate zone, making global rankings conditional. Horizon mattered, with longer horizons using tabular weather and post hoc attribution and ultra-short horizons using sky images. Cross-study benchmarking remains fragile, and quantitative evaluation of explanation fidelity, stability, and runtime is rare. A critical appraisal of the dominant explainers documents computational cost, instability, sensitivity to correlated inputs, and unverified faithfulness, alongside a reproducibility gap, with only 1.7% of studies releasing both code and data.
Rising global energy demands necessitate efficient solar thermal technologies; however, conventional Solar Air Heaters (SAHs) suffer from inherently limited thermal efficiency and complete operational cessation after sunset. This study aims to systematically evaluate recent SAH advancements and propose a novel, highly efficient synergistic conceptual framework to overcome existing thermal limitations. A comprehensive systematic review of contemporary literature was conducted, critically analyzing geometrical modifications, thermal energy storage (TES), and various performance enhancement strategies. The analysis reveals that utilizing a hybrid geometry integrated with a 45° wavy absorber plate maximizes convective heat transfer. Furthermore, while paraffin wax is identified as a superior Phase Change Material (PCM), its prolonged charging time remains a critical challenge. To resolve this, integrating a waste heat recovery (WHR) system (compressed air unit) for inlet air pre-heating is proposed to drastically accelerate the PCM melting process. The distinct novelty of this article lies in synthesizing these historically fragmented technologies geometry, TES, and WHR into a single pioneering architecture. This integrated approach can reach a cumulative daily thermal efficiency of 87% while ensuring continuous useful energy output well into the evening. Ultimately, this review establishes a new actionable thermoeconomic benchmark for future SAH designs.
Earth Air Heat Exchangers (EAHE) are passive earth contact heating/cooling systems. They are the most environmentally friendly and easy to operate and install. This study evaluates the performance of an open-loop EAHE, cooling a solar building (greenhouse) at Ege University Solar Energy Institute in Turkey. Using experimental data, it compares the practicality and environmental implications with conventional cooling methods. EAHE performance tests were realized at the Bornova Site during 138 days of the cooling season between April 26 and September 15, 2024. The system achieved an average COP of 6.5, which demonstrated its high efficiency The EAHE’s exergy efficiency of 74.8% verifies the design applicability. The system used just 0.76 kW of electrical power to meet the average cooling load of 5 kW. A system operating for 12hrs/day would reduce the CO2 emissions by >2 kg/day based on a global average 475 g/kWh CO2 emissions. This shows a great potential to reduce CO2 emissions operating year-round in all kinds of buildings. The system's performance demonstrates the high potential of EAHE systems when compared with conventional cooling methods. Since building cooling demand has been increasing globally, additional research is warranted in the design of EAHE systems for all kinds of buildings.
This paper presents a new software tool for the optimal sizing of an autonomous hybrid system combining photovoltaic, wind, and hydrogen sources, designed to power a seawater desalination unit. Using an iterative optimization strategy, the tool identifies the most cost-effective hybrid system configuration that reliably meets freshwater demand year-round. Compared with conventional hybrid energy system sizing tools, the proposed software provides an automated optimization framework that improves decision-making efficiency, reduces design complexity, and enables rapid evaluation of multiple system configurations while maintaining competitive techno-economic performance. The software integrates cybersecurity mechanisms to ensure the protection of sensitive project data. Its user-friendly interface offers a clear and structured visualization of all stages in the system sizing process. Users input relevant meteorological and technical parameters. After simulation, the tool generates multiple configurations and automatically selects the optimal solution, displayed in a dedicated results window. A case study conducted on a desalination unit in Bekalta (Monastir) demonstrates the tool’s effectiveness. The optimal configuration consists of a 200 kW wind turbine, 750 PV panels, a 77.5 kW fuel cell, and a 75 kW electrolyzer, achieving the lowest total project cost ($2,688,847), with a Levelized Cost of Energy of $0.194/kWh and a Levelized Cost of Hydrogen of $14.225/kg. This software provides a secure, integrated, and efficient solution for renewable-powered desalination systems, adaptable to various geographic and climatic contexts, thereby supporting sustainable water production.
A complete three-dimensional computer-aided simulation has been accomplished by COMSOL Multiphysics software to examine the optical 3D photogeneration rate, electric field response, thermal distribution, impact of physical parameters on PV parameters, resistance, temperature, and surface recombination velocity on ZnGa2Te4 (ZGT) solar cell, where ZnSe and GeS are utilized as window or emitter and back surface field (BSF) layers, respectively. In the course of this analysis, the physical parameters, including layer thickness, carrier and bulk defect concentration, have been analyzed to determine the optimized condition of the n-ZnSe/p-ZnGa2Te4/p+-GeS solar structure, which has been evaluated through a systematic modeling approach. Without the GeS layer, the values of photovoltaic (PV) parameters are power conversion efficiency (PCE) of 20.87% with VOC = 0.90 V, JSC =28.05 mA/cm2, and FF =82.68%. With the GeS BSF layer, the optimized device parameters yield a PCE of 30.22% for the developed device with a VOC = 1.04 V, JSC = 33.11 mA/cm2, and FF = 87.77%. The promising performance of the proposed earth-abundant ZGT PV device demonstrates its considerable potential not only to meet the energy crisis but also as a possible alternative to the existing solar compounds.
In this research, we investigate the structural and optoelectronic properties of Rb2AgInF6 and Rb2AgIrF6 by using Density Functional Theory. The Goldschmidt tolerance factor, phonon dispersion and formation energy demonstrate their thermodynamic stability in the cubic structure. The obtained lattice constants a, by structural optimization, are 9.1070 Å and 8.9155 Å for Rb2AgInF6 and Rb2AgIrF6 compounds, respectively. The examination of the electronic properties shows that the compounds exhibit a direct band gap of 1.81 eV and 1.45 eV for Rb2AgInF6 and Rb2AgIrF6, respectively, making them promising for photovoltaic applications. In addition, the optical properties analysis, performed over the entire calculated energy spectrum ranging from 0 to 12 eV, shows strong ultraviolet absorption, a high refractive index at low energy, and an improved plasmonic response for Rb2AgIrF6. These results confirm the potential of the studied materials as high-performance active layers in photovoltaic solar cells. Furthermore, the study of various parameters of CdS/Rb2AgIrF6/MgCuCrO2 heterojunction solar cell was investigated using the SCAPS-1D simulator. Under optimized conditions, corresponding to an absorber thickness of 0.6 μ and an acceptor concentration of 1017 cm−3 , the device achieves a power conversion efficiency (PCE) of η=30.18%, with an open-circuit voltage (Voc) of 1.3661 V, a short-circuit current density (Jsc) of 28.0146 mA/cm2, and a fill factor (FF) of 78.85%. These findings demonstrate the promising potential of Rb2AgIrF6 as a lead-free absorber material for next-generation photovoltaic applications.
The conversion chain forms the core of the photovoltaic (PV) system upon which the algorithms used are crucial for optimizing the aforementioned conversion chain. This article addresses an exploration of the optimization of the energy produced by the PV panel by comparing the use of artificial intelligence, that is, artificial neural networks (ANN), with our previously proposed fast convergence approach (FC). The utilization of ANN demonstrates their efficiency and stability for maximum power point tracking (MPPT) against varying irradiation and temperature. Both methods demonstrate their robustness and offer high performance under varying weather conditions; however, the results verify that they are different in certain instances. Subject to varying irradiation but stable temperature scenarios, the FC technique exhibits a mean absolute percentage error (MAPE) of 1.08%, compared to that of 1.12% by the ANN. That indicates that both algorithms can track effectively the maximum power point but the FC technique tracks more, despite varying weather conditions especially variable irradiation. Similarly, subject to stable irradiation and varying temperature scenarios, the FC technique verifies a MAPE of 2.18%, also lower than that of the ANN of 2.67%. These results indicate that the ANN algorithm present a good stability in permanent regime when the FC technique offers a more precision despite the presence of oscillations in its steady-state response.
Microscopic pinholes in perovskite solar cells can create local shunting paths when the metallization grid crosses the defective regions. This problem becomes more important in large-area and roll-to-roll fabrication, where fixed metal grids cannot avoid local film defects. In this work, a defect-aware adaptive metallization framework is proposed for perovskite solar cells using FPGA edge-AI. SEM images are used for pinhole sensing, and each 416 × 416 image is divided into 32 × 32 patches by the MicroBlaze processor on a Xilinx Artix-7 FPGA. The patches are processed using an INT8-quantized TinySegNet_FPGA_v3 accelerator synthesized through hls4ml and stored within on-chip BRAM. The segmented defect map is then converted into C-swerve routing coordinates to avoid pinhole regions during metallization. The model processes each 32 × 32 patch in 5.647 ms at 100 MHz, and the complete 416 × 416 image is processed in 954.3 ms. The electrical effect of the proposed routing is evaluated using a distributed electrical model that measures the actual finger-defect overlapping area directly from the routed metallization geometry, rather than assuming a fixed shunted-area fraction. For a representative 25% nominal defect-area case, the simulated power conversion efficiency improves from 19.95% under conventional straight-line metallization to 20.34% under adaptive C-swerve routing, with the benefit growing at higher defect severities. This result was extended to a full parameter sweep that co-varies finger width (10-80 µm) with defect-area fraction (0-40%), re-optimizing the cell geometry at each finger width. Adaptive routing improves PCE at every tested condition, with the recovery plateauing at +0.045-0.046 percentage points across a 40-80 µm finger-width window at 25% defect area identifying this range as the practical manufacturing window where shunt suppression consistently outweighs the added series-resistance cost of the detour, while the benefit grows further with defect severity at every width. These results suggest that FPGA edge-AI can provide a possible route to connect defect sensing, adaptive metallization, and geometry-aware electrical modelling for scalable perovskite solar-cell manufacturing.
Water scarcity is a pressing concern in arid regions, and Morocco is no exception. Solar stills offer a low-cost off-grid desalination option, but their output is limited by poor thermal retention after peak solar hours. This study builds a six-node transient model for a single-slope solar still and compares four configurations — CSS, SWR, SWPCM, and SWR-PCM — under real Rabat meteorological data (14 July 2023). The model is validated against published experimental data and an analytical solution. SWR-PCM achieves the highest productivity (6.1 kg/m²/day, +66% vs CSS), the highest latent efficiency (48%), and the highest exergy efficiency (ηex = 3.2%). The resistance heats the basin to 72°C in the morning while the PCM (Tm = 56°C) extends evaporation into the evening. The PCM contributes more to exergy improvement (+0.7 pp) than the resistance alone (+0.5 pp), confirming the thermodynamic advantage of passive thermal storage. These results demonstrate the synergistic benefit of combining active heating with PCM storage for solar desalination in water-stressed regions.
Renewable energy technologies (RETs) provide many potential ways to genuinely meet the goal of sustainable agricultural development, access to better rural livelihoods, and climate protection in India. This study examines the key drivers shaping the willingness of rural farmers to use renewable energy technologies in their farming operations and assesses the impact on productivity and the financial health of rural farmers. Main focus of the research is the renewable energy use such as a solar-powered irrigation system for agricultural use. The study, rooted in the concepts of sustainable development and rural energy transition, looks at government support, barriers to adoption and social engagement in driving up adoption of renewable energy by farmers.The study has been based on the primary data obtained from the 251 farmers using a structured questionnaire survey in rural agricultural areas of India. The research method for studying the proposed conceptual framework and testing of the hypothesized relations among research constructs was Structural Equation Modelling (SEM) analysis method implemented by SmartPLS. Results showed that the adoption of RE positively responded to government support, financial incentives, social involvement, while financial/technical constraint had negative effect on the adoption. This shows how widely using renewables can jumpstart productivity gains that can boost financial stability and economic resilience of farmers.This study is valuable to the literature of echos on renewable energy uptake and sustainable farming, as it combines policy, social and economic aspects within one analytical framework. The results are also cross-referenced to relevant Sustainable Development Goals (SDGs), such as SDG 2 (Zero Hunger), SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Overall the study provides policy recommendations for a strengthening of subsidy-based instrument, increasing access to rural financing, enhancing technical awareness programs, and promoting renewable energy use in the community in order to achieve sustainable agricultural transformation in the rural areas of India.
Postharvest onion storage remains a major challenge in hot-climate regions, where limited access to refrigeration results in substantial storage losses. However, the thermo-aerodynamic performance of passive storage systems has not yet been fully characterised. This study addresses this gap through an integrated experimental and numerical investigation of the thermo-aerodynamic behaviour of a passive onion storage system ventilated by natural buoyancy-driven airflow. A multiphysics model incorporating heat transfer in solid, fluid, and porous media, together with laminar airflow, was developed and solved using COMSOL Multiphysics. The results demonstrate a significant attenuation of external thermal loads. Despite external wall surface temperatures reaching 56.3°C, the temperature fluctuation inside the storage chamber was limited to 1.5°C experimentally and 2.3°C numerically. Storage rack temperatures ranged from 28.45 to 32.5°C, with exceedances of the recommended 25–30°C range remaining mainly localised and transient. Relative humidity remained within 60–75% for most of the monitoring period, while no conditions conducive to condensation were observed. Solar heating enhanced the buoyancy-driven airflow, increasing the air velocity from 0.028 to 0.273 m/s and the volumetric airflow rate up to 0.410 m³/s demonstrating favourable airflow performance compared with most comparable configurations reported in the literature. Statistical validation yielded RMSE values below or close to 1°C, CV(RMSE) values ranging from 2.64 to 3.45%, and NMBE values between −3.52 and −1.65%, confirming the overall predictive capability of the model. These findings demonstrate that coupling the thermal inertia of the storage chamber with solar chimney-induced natural ventilation provides a stable storage environment suitable for onion preservation under hot-climate conditions.
Accurately predicting solar irradiation intervals remains a critical challenge for decision-making in the energy management of solar-powered smart microgrids. To cope with the resulting uncertainties, models capable of providing accurate and reliable prediction intervals are essential. This paper proposes a novel hybrid approach combining long short-term memory (LSTM), extreme gradient boosting (XGBoost), and an adaptive kernel density estimator (AKDE), whose bandwidth is optimized via differential evolution (DE). By optimizing both the local and global bandwidths of the kernel density estimator, the proposed method generates highly accurate and reliable prediction intervals, demonstrating enhanced robustness. Comparative analyses across multiple evaluation sites reveal that the hybrid model (LSTM–XGBoost–DE–AKDE) outperforms existing models reported in the literature. Specifically, the average PINAW value achieved by the proposed model is substantially lower than that of the KDE–PSO–LSTM model. At the 95% confidence level, the proposed model achieves a PINAW of 0.122 compared to 0.301; at the 90% level, 0.101 versus 0.256; and at the 85% level, 0.083 versus 0.258.
Integrating renewable energy (RE) systems into the national grid can lead to intermittent supply, degrade power quality, and pose economic challenges. A smart grid (SG) technology is optimized to identify the most efficient plan for incorporating alternatives. This paper examines the techno-economic feasibility of a hybrid RE system connected to the national grid. Large-scale cases are taken to predict the economic statement that a country needs to upgrade, utilizing the same infrastructure. The HOMER Grid tool is used in the current work to simulate three load cases at the country and city levels. The integration of RE sources has participated in up to 19.4%, 219.4%, and 18.24% in Case A, Case B, and Case C, respectively. The affordable cost of energy (COE) was determined at the highest penetration of RE sources. The COE has likely remained close to that of the national grid when it provides the electricity; the value of COE has ranged between $0.0686/kWh and $0.0706/kWh. This research recommends utilizing RE sources in conjunction with the national grid in a country with an abundance of non-renewable energy sources. The future of this study is to extend the simulation for a design software to implement the suggested systems with local weather station.
Tin-based perovskite solar cell (PSC) has drawn significant attention due to its bandgap, low fabrication cost, and remarkable power conversion efficiency. However, the PSC has not yet achieved the efficiency of Si solar cells, and the maximum efficiency obtained from a MAPbI3-based perovskite solar cell contains a toxic element (Pb). Therefore, to obtain an environmentally friendly and efficient PSC, a lead-free FASnI3 perovskite solar cell is designed in which FASnI3, TiO2, and Cu2O are considered as the absorber layer, ETL, layer and HTL layer, respectively, and the effects of doping on each layer are studied using SCAPS-1D. Here, it is observed that certain limits of doping concentration and thickness of the absorber layer have effects on performance parameters of the solar cell. However, almost no effect on VOC is observed due to the change in thickness of the absorber layer. In the case of HTL, the doping concentration and thickness have no effect on VOC, but within 1014 to 1016 doping concentration and above 0.4 µm thickness JSC, FF, and PCE reduce. The ETL layer thickness has almost no effect on the performance parameters, but the increase in doping concentration shows better performance. The effects on current-voltage characteristics, QE, generation rate, and recombination rate are also observed due to the doping of each layer.
Solar energy has emerged as one of the most scalable and sustainable solutions in a global landscape still dominated by fossil fuel-dependent economies. In particular, Concentrated Solar Power (CSP) is of interest due to its ability to provide dispatchable renewable energy in the form of a combined Thermal Energy Storage (TES) system, thus allowing stable support of the grid and delivery of power during peak hours. The situation in India reflects the stagnant deployment of CSP despite the high Direct Normal Irradiance (DNI) in several regions, especially Rajasthan and Gujarat, due to high capital costs, water scarcity, unpredictable bidding schemes, and limited domestic manufacturing capacity. This paper discusses global CSP trends and India’s technological advancements, policy development, cost-reduction measures, and water-efficient cooling solutions, as well as hybridization strategies such as coal-CSP and CSP-PV integration. The analysis recommends hybridization with existing fossil infrastructure, adoption of advanced molten salt and packed-bed thermal storage, and policy support, including tariff certainty, domestic manufacturing incentives, and grid-service payments as viable pathways for a CSP revival in India.
Accurate photovoltaic (PV) power prediction is hindered by environmental stochasticity. Existing physical models often overlook "atmospheric disturbers"—such as wind-induced cooling and humidity-based spectral absorption—while black-box deep learning models lack physical interpretability and stability. To address this gap, this study proposes an Atmospheric-Aware Residual Deep Learning (AARDL) framework. This "Grey-box" approach integrates a calibrated 8-parameter physical model with a 1D-ResNet to map and compensate for systematic residuals. By incorporating wind speed and humidity as input features, the model captures previously unmodeled dynamics. Validated on seasonal datasets from September 2018 and April 2019, the AARDL framework achieved significant error reductions, lowering RMSE from 36.11 V to 3.04 V (91.59%) and 36.78 V to 2.09 V (94.32%), respectively. Sensitivity analysis confirms that while temperature is the primary driver, atmospheric features are vital for high-fidelity precision. This research offers a scalable, computationally efficient solution for real-time IoT monitoring and high-fidelity PV digital twins.
Hospitals require reliable energy supplies while facing increasing pressure to reduce greenhouse gas emissions and grid dependence. This study presents an hourly techno-economic and environmental assessment of a hybrid photovoltaic-proton exchange membrane electrolyser (PV-PEMEL) system for electricity supply and co-production of hydrogen and oxygen across five Indian locations: Guwahati, New Delhi, Jodhpur, Bengaluru, and Srinagar. Location-specific solar resources, hospital electricity demand, and a priority-based energy management strategy were considered. Annual PV generation ranged from 507.54 to 588.56 MWh, directly supplying 50.5-52.4% of hospital electricity demand. Annual hydrogen and oxygen production ranged from 5.8-7.1 tonnes and 46-56 tonnes, respectively. The levelized cost of electricity (LCOE) ranged from 0.027-0.078 $ kWh⁻¹, the levelized cost of hydrogen (LCOH) from 4.56-5.99 $ kg⁻¹, and the payback period from 12.84-15.47 years. LCOE remained below applicable grid tariffs across all locations, while LCOH for Bengaluru and Jodhpur was at or within the upper boundary of the reported Indian green hydrogen cost range. Annual CO₂ mitigation ranged from 182.88-198.64 tonnes year⁻¹, while hydrogen production required only 1.5-1.8% of total hospital water demand. Additional analyses for Guwahati showed limited sensitivity to load-profile variations and strong economic sensitivity to co-product valuation, hydrogen price, capital cost, and financing conditions. Scaling from 50 to 200 beds reduced LCOE and LCOH by 57.69% and 29.7%, respectively, while component degradation increased the lifetime LCOE and LCOH by 24.35% and 12.19%, respectively. Overall, Bengaluru and Jodhpur exhibited the most favourable combined economic and environmental performance.
This study examines the interconnections among Foreign Direct Investment (FDI), Gross Domestic Product (GDP) growth, renewable energy (REE) adoption, and innovation (INN) in Poland. Using an Autoregressive Distributed Lag (ARDL) model covering 1990 to 2023, this analysis examines how these factors influence Poland's sustainable development trajectory. The analysis highlights the critical role of FDI in driving GDP growth and fostering INN, particularly in technology-intensive sectors. REE adoption is a vital element of Poland's transition to a low-carbon economy, though constrained by its reliance on coal. The study also identifies INN as a crucial mediator, enhancing FDI's impact on economic growth (EG) and accelerating the adoption of REE. Policy recommendations emphasize aligning FDI strategies with INN goals, accelerating REE transitions, and addressing regional disparities to ensure inclusive development. The findings underscore the need for an integrated approach to leveraging economic, environmental, and technological synergies for sustainable growth in Poland. Leveraging empirical data and econometric modeling, it identifies the synergistic effects of these factors on sustainable development. The findings suggest that Poland's unique economic transition from a centrally planned economy to a market-based system has created fertile ground for examining this nexus. The paper underscores the importance of strategic policy alignment to ensure that EG, driven by FDI and INN, supports Poland's REE targets and sustainable development goals.
This study presents a rigorous experimental investigation into the thermal and hydraulic performance of three single-pass solar air heater (SAH) configurations: flat (FPSAH), 45° corrugated (CPSAH), and 45° wavy (WPSAH) absorber plates. Testing was conducted simultaneously in Mosul, Iraq, to ensure identical environmental conditions, evaluating the collectors under both natural and forced convection modes (mass flow rates up to 0.01847 kg/s). The methodology focuses on the novelty of using 45° optimized geometries as a simple yet effective modification to enhance heat transfer without the need for complex internal baffles. Performance was assessed using key metrics: outlet air temperature, temperature difference (ΔT), and net effective efficiency, which incorporates parasitic electrical losses from the fans. Results revealed that the WPSAH significantly outperformed other designs; under natural convection, it achieved a peak outlet temperature of 101.8°C and a ΔT of 79°C, compared to 83.7°C and 60°C for the FPSAH. In forced convection, the WPSAH reached a peak net daily efficiency of 70.25%, surpassing the CPSAH (60.59%) and FPSAH (50.15%). While the wavy profile incurred a 21% higher pressure drop, its enhanced thermal gain (20% higher than FPSAH) justified the hydraulic trade-off. These findings confirm the WPSAH as the optimal design for domestic space heating in semi-arid climates, providing a manufacturing-friendly solution capable of reducing household electricity consumption by over 20%.
Providing reliable, affordable and renewable energy to remote communities remains a challenge. Hybrid Renewable Energy Systems (HRES), combining solar, wind, and energy storage technologies, offer a promising solution. But designing the best system can be challenging given the variability of renewable energy sources, and the need to consider both low cost and high reliability. This challenge needs to be tackled by a step-by-step multi-objective approach that can weigh up these issues while being optimised, rather than the traditional single-objective methods. This paper proposes a comprehensive new multi-objective approach for designing an independent PV-wind-battery hybrid system. It's based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The system's operation is simulated for a year with weather information (sunshine, ambient temperature, wind speed) and the usage of a typical community for the correct calculation of the energy balance and reliability. One focus is on making a well-distributed Pareto front, showing how the Net Present Cost (NPC) is traded for the likelihood of losing power supply (LPSP). The investigation reveals LPSP can be reduced with a small increase in initial cost, but getting close to 100% reliability requires a very high cost. Optimum designs are identified for several requirements, from a least-cost to a most-reliable system. This study demonstrates that NSGA-II is useful for supporting sustainable design decisions in HRES planning. It provides the system developer or leader with the best solutions based on the money and technical requirements.
