Search Results (588)

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a transformative technology for optimizing the operation, control, monitoring, and maintenance of battery storage systems. This review provides a comprehensive overview of AI-driven BESS technologies for renewable energy applications. The study examines recent advances in machine learning, deep learning, reinforcement learning, and hybrid intelligent algorithms applied to battery state estimation, energy management, fault diagnosis, predictive maintenance, thermal management, and lifetime prediction. Furthermore, the integration of AI-based BESSs with photovoltaic systems, wind farms, microgrids, and smart grids is critically analyzed. The review highlights the advantages of AI techniques in improving system efficiency, reliability, adaptability, and decision-making capabilities under uncertain operating conditions. Current challenges, including data quality, model interpretability, computational requirements, cybersecurity concerns, and real-time implementation issues, are also discussed. Finally, emerging research directions such as digital twins, explainable artificial intelligence, federated learning, and edge intelligence are explored to provide insights into the future development of intelligent battery storage systems. This review aims to serve as a valuable reference for researchers, engineers, and practitioners working at the intersection of artificial intelligence, battery technologies, and renewable energy systems. Full article

Excess nitrogen and phosphorus in surface water can degrade water quality and increase eutrophication risk. However, how surface-water nitrogen and phosphorus concentrations vary across mixed land- and water-use settings remains poorly understood in highly connected plain river networks, particularly under emerging water uses such as floating photovoltaic (FPV) installations. We investigated surface-water nitrogen and phosphorus concentrations across different land- and water-use settings in the Yangtze River Delta, focusing on seasonal variation and environmental controls. Field measurements were combined with Sentinel-2-derived waterbody metrics within 500 m buffers around sampling plots. Aquaculture ponds had the highest overall nutrient concentrations, industrial land showed relatively high nitrate–nitrogen (NO₃⁻-N) and total nitrogen (TN), and aquatic solar farms exhibited a distinct nutrient pattern with relatively high total phosphorus (TP) but low TN. Nutrient forms showed contrasting seasonal patterns, with higher NO₃⁻-N in the dry season and higher TP in the wet season. Water-surface proportion was weakly related to nutrient concentrations, whereas edge density was significantly correlated with them. These findings indicate that nutrient patterns were shaped by direct inputs, particle-associated transport, ecological and physicochemical conditions, and land–water interface effects. Nutrient management should therefore consider seasonal hydrology, local waterbody configuration, and emerging water-use types such as aquatic solar farms. Full article

Grid-connected electric vehicles (EVs) function as distributed loads or energy storage units. The integration of electric vehicles (EVs) into microgrids can provide various services, including ancillary services, active power control, reactive power compensation, and most importantly, frequency regulation. Electric vehicles equipped with vehicle-to-grid (V2G) technology provide frequency regulation services to compensate for the intermittent production of renewable energy and achieve load balancing. Electric vehicles operating with microgrids play a vital role in integrating renewable energy sources (RES), such as wind and solar farms. The intermittent power generation from these renewable sources can lead to significant frequency fluctuations in microgrids. The microgrid can benefit from ancillary services such as frequency regulation due to the increasing number of electric vehicles in future networks and the improved management of their charging and discharging. Simulation results indicate that the proposed frequency support strategy based on electric vehicles significantly improves the dynamic performance of the microgrid. These results confirm the effectiveness of integrating electric vehicles through the V2G concept to enhance frequency stability in isolated microgrids that rely on renewable energy. Full article

The present study proposes a configuration of a solar PV-battery-powered knapsack sprayer (SPKS) equipped with a rear-mounted multi-nozzle boom to serve as a sustainable, reliable and decentralized spraying system for smallholder farmers. This design aims to significantly reduce the physiological strain and inconsistent performance associated with conventional manual lever sprayers (MLSs). The SPKS was evaluated against the MLS during onion crop spraying in terms of hydraulic performance, field capacity, spray deposit uniformity, and operator ergonomics and biomechanics, alongside an economic and environmental sustainability assessment. Results of hydraulic tests revealed that the SPKS achieved the optimal spray distribution uniformity of C.V = 16.67% at an operating pressure of 350 kPa and a boom height of 40 cm. Field experiments demonstrated that the SPKS more than doubled the effective field capacity to 0.36 ha/h compared to 0.16 ha/h for the MLS, achieving a field efficiency of 67.55%. Moreover, the SPKS achieved high spray deposit coverage (88%) compared to the MLS (52.6%), while the integrated PV panel effectively doubled operational runtime by maintaining >50% battery state of charge under continuous load. Biomechanics and ergonomics pilot analyses indicated that the MLS operation imposed high musculoskeletal (RULA score = 7) and cardiac strain, whereas SPKS operation was classified as low-risk (RULA score = 3) with minimal cardiac strain. Economically, the SPKS saves approximately $8.70 USD per hectare in labor costs. Environmentally, it prevents ~17.0 kg of CO₂ emissions annually and reduces pesticide application volume by 26.5%. By simultaneously addressing energy limitations, ergonomic hazards, and operational inefficiencies, the SPKS offers a holistic and superior solution for sustainable smallholder agriculture. Full article

Water scarcity in semi-arid regions makes efficient irrigation scheduling a priority, yet farm-level adoption of soil-moisture monitoring remains limited by the cost, low portability and installation complexity of commercial sensing systems. This study presents the development and field validation of WaziSense, a low-cost, solar-powered Internet-of-Things (IoT) smart tensiometer, developed within the OSIRRIS platform for soil-water monitoring and irrigation scheduling. The device couples a Watermark granular-matrix sensor and a DS18B20 temperature probe to an ATmega328P microcontroller (Arduino Pro-Mini, 3.3 V, 8 MHz) with long-range LoRa communication and a maximum-power-point-tracking (MPPT) solar-charging stage, logging soil matric potential and soil temperature every 15 min. An open-source edge/cloud stack (WaziGate, WaziApp) retrieves weather forecasts from an open API and runs an automated machine learning (AutoML) regression pipeline that forecasts soil-water dynamics and the time to a user-defined threshold, from which irrigation is scheduled and its applied volume verified by a flow meter. The system was deployed at three bioclimatic sites in Tunisia (durum wheat at Cherfech, citrus at Nabeul, apple at Sbeitla), with tensiometers installed at 20 and 40 cm depths, and validated against commercial 10HS capacitive probes coupled to a ZL6 data logger, with which the co-located readings were significantly correlated (r = 0.81). Calibrated readings showed a strong relationship between soil–water content and soil–water potential (R² = 0.99), and the edge forecasting model reproduced soil–water dynamics on unseen data (Sbeitla apple site, 5-day horizon) with R² = 0.73, RMSE = 0.35, MAE = 0.23 and MPE = 12.52%. With a material cost under about 90 EUR per node and fully open-source hardware and software, WaziSense is one to two orders of magnitude cheaper than commercial monitoring stations, offering an affordable, reproducible and scalable tool for data-driven irrigation in water-limited agriculture. Full article

Assessing the impacts of large-scale photovoltaic plants on soil properties is critical for sustainable land management in arid regions. This study examined a 50 MWp utility-scale PV facility in Siziwangqi, Inner Mongolia, northern China, a cold semi-arid desert steppe where renewable-energy development overlaps with fragile wind-eroded ecosystems. A spatially resolved sampling design contrasted soils at key micro-positions relative to the panels, namely Front, Under, and Behind, with adjacent natural Controls. By integrating laser diffraction analysis, multifractal modeling, and the EPIC erodibility equation, we evaluated soil particle-size probability redistribution and EPIC-estimated intrinsic erodibility. Compared with the silt-dominated surface soil of the natural steppe, soils within the photovoltaic plant exhibited fine-particle retention and lower information and correlation dimensions (D1 and D2), indicating stronger local clustering and a less even particle-size probability distribution. The EPIC-estimated erodibility factor decreased from 0.452 in the Control to approximately 0.361 in the PV micro-locations. These associations should be interpreted as texture- and SOC-based model estimates rather than direct measurements of wind erosion. Overall, multifractal parameters provide complementary proxy descriptors for detecting PV-induced particle sorting and potential changes in intrinsic soil erodibility, underscoring the need for field validation and adaptive management in dryland PV landscapes. These findings provide physical soil evidence for evaluating the environmental sustainability of dryland PV development and for supporting adaptive land management in solar farms. Full article

The rapid expansion of photovoltaic capacity in Poland has exposed distribution-grid constraints and increased the incidence of non-market curtailment. This paper examines a 1 MW ground-mounted photovoltaic plant in Zalesie, north-eastern Poland, using complete documentation. The study combines a spatial multi-criteria site-suitability assessment based on the Weighted Sum Model, a sensitivity analysis of criteria weights, an analysis of 28 curtailment compensation applications, and a transparently parameterised LCOE/NPV/IRR model under four curtailment-severity scenarios. The results indicate a high and weight-robust site-suitability score (S = 4.45/5.00; sensitivity range 4.35–4.55), with curtailment concentrated in spring: 68% in April and May. Economic modelling shows that, under merchant market conditions and without support schemes, profitability is already constrained in the baseline case (IRR 4.2–6.4%), while increasing the share of unsold energy from 0% to 10% raises LCOE by approximately 3–11% and deepens the already negative NPV by a further PLN 0.09–0.36 million. A Monte Carlo simulation confirms that the NPV stays negative across the whole plausible parameter range. This case study demonstrates that classical spatial suitability may be a necessary but insufficient condition for the economic security of PV investments unless grid-related and regulatory risks are incorporated into the planning stage. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

The integration of renewable energy into smart grids is beneficial for a sustainable future and the environment. Still, it has challenges such as energy optimisation, environmental conditions and power quality degradation. Existing Maximum Power Point Tracking (MPPT) techniques often focus on tracking efficiency under steady-state conditions, ignoring the impact of real-time variation in environmental conditions and load. The predictive power flow control (PPFC) algorithm is available with one or more fixed MPPT algorithms. No studies have reported on how the choice of MPPT affects PPFC harmonic mitigation. This paper addresses both concerns through a systematic comparative analysis of MPPT techniques integrated with a PPFC method to mitigate harmonics in renewable-integrated smart grid systems. To address this research gap, a comprehensive comparative analysis of various MPPT techniques, such as Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic Control (FLC), and hybrid Machine Learning (ML) techniques, integrated with PPFC to achieve effective harmonic mitigation in a smart grid environment is conducted. A 3 MW solar farm integrated with a battery storage system is modelled in MTALB/Simulink 2025b under real-time varying conditions, such as environmental and load variations over time in Auckland, New Zealand. The study focuses on key performance parameters such as total harmonic distortion (THD), power loss, stability and efficiency. The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller, integrated with forecast-based power flow control, achieved overall performance by providing higher efficiency (97.5%), effective harmonic mitigation, and enhanced system stability under the nonlinear behaviour of the photovoltaic system. The proposed ANFIS-based system ensured a stable and smooth power output under varying environmental conditions, outperforming conventional and other intelligent MPPT techniques. Full article

Dust accumulation on photovoltaic (PV) module surfaces significantly reduces energy yield in arid environments, yet traditional water-based cleaning techniques are unsustainable. This study presents a complete evaluation of electrodynamic screen (EDS) technology as a waterless cleaning solution, with a focus on dust removal efficiency and the critical trade-offs between energy gains and losses. Using printed circuit board (PCB) technology, a three-phase EDS system was designed and constructed, and its performance was simulated on COMSOL Multiphysics. Under optimal conditions of 50 Hz and a 66% duty cycle, the system achieves 52% dust removal efficiency while consuming less than one watt-hour per square meter during operation. The novelty of this study lies not merely in the hardware, but in the analytical lens applied and the holistic loss–gain methodology that quantitatively assesses, over a seven-day cycle, the reduction in dust-induced optical loss, the intrinsic optical transmission loss introduced by the EDS layer itself, and the operational energy cost of running the system. Rather than focusing on removal efficiency in isolation, this study enables a site-specific evaluation of whether, and when, EDS technology delivers a net energy benefit. By integrating material properties, local soiling conditions, and all major loss mechanisms, this study provides a low-cost and analytical tool for researchers and solar farm operators to assess the real-world feasibility of EDS cleaning before deployment. While the proposed PCB technology is not itself suitable for field deployment, it serves as a low-cost development platform for optimizing electrode geometry and excitation parameters prior to investment in transparent materials. Full article

With the increasing integration of wind and solar energy, power systems face growing challenges in supply–demand balancing, renewable energy accommodation, and reserve regulation, especially in future power systems with evolving generation mixes. To this end, a system adaptability evaluation method particularly considering planned future wind farms and photovoltaic (PV) stations is proposed. Available historical meteorological data are converted into wind and PV power output sequences through renewable power conversion models. Then, a generative adversarial network (GAN)-based model is built to learn the temporal fluctuation characteristics and inter-station correlations of renewable generation, thereby generating multiple representative wind and PV output scenarios. Finally, these scenarios are embedded into a security-constrained unit commitment (SCUC)-based model to evaluate system adaptability in terms of load shedding, renewable energy curtailment, accommodation rate, and reserve margin. The proposed method provides a forward-looking framework for assessing the operational adaptability of power systems under high renewable energy penetration. Full article

This paper quantifies PV-based power dispatch availability, termed “dynamic firm capacity” (DFC), to provide operational certainty at the power plant level. Unlike conventional PV forecasts with unavoidable errors, DFC is a conservative dispatch target designed to be reliably achievable by the PV–BESS system, enabling firm generation. Curtailment is treated as a controllable plant-level resource to improve dispatch reliability. Three methods for determining DFC are compared: a baseline using PV forecasts directly, a fixed-ratio downscaling of the PV forecast, and a deep reinforcement learning (DRL) approach based on the Proximal Policy Optimisation (PPO) algorithm. Using data from Rugby Run Solar Farm (RUGBYR1) in Queensland, Australia, PPO achieves higher accuracy with smaller battery capacity and lower curtailment than the other two methods, with the advantage widening as battery capacity and power limit grow. Against two additional tuned benchmarks (an empirical probability-of-exceedance quantile forecast and a state-of-charge-aware rule-based policy), PPO reaches comparable dispatch compliance at a fraction of the curtailed energy. The best-performing PPO agents demonstrate 100% DFC realisation and near-zero dispatch error at near-zero curtailment, with a BESS capacity equivalent to one hour of peak PV output and a 0.67 E-rate power limit. Results are reported across 100 independently trained agents per scenario using the September 2023–August 2024 period, and the method’s advantage is preserved on a chronologically later, fully unseen evaluation window (September 2024–May 2025). An open-source DRL training environment is released to support reproducible PV–BESS research. Full article

Highland pastures in Montenegro, known as katuns, are seasonal settlements without grid power or network coverage and which are located where conventional monitoring is unfeasible. This study presents a solar-powered, off-grid system for livestock and environmental monitoring. It integrates, into a single portable unit, a solar power station, an edge-AI computer, a camera, environmental sensors, a LoRaWAN gateway, and a cellular router for backhaul. All parts are pre-wired in a modular enclosure, deployable by one operator in under 30 min. Data are fed to the agroNET farm-management platform and a purpose-built mobile web application; livestock detection runs on-device using a model from our earlier work. The system was evaluated at three sites, including a highland katun near Žabljak (~1450 m), under a two-phase energy-measurement protocol. During field logging it drew ~75 W on average against ~125 W solar input—a measured surplus that is used to recharge the battery—with a daily monitoring load of ~1560 Wh. The four-panel array’s nameplate potential in summer is an estimated ~3700 Wh/day, indicating substantial headroom relative to the measured load. At 80% depth of discharge the battery gives ~20 h autonomy, and the detection pipeline ran continuously, processing ~10,000 frames at under 3 s latency. The results demonstrate the feasibility of off-grid precision livestock farming, reaching TRL 6. Full article

Long-term energy system models often represent renewable energy technologies using aggregated potentials and average capacity factors, which may insufficiently reflect the spatial and technological heterogeneity of weather-dependent resources. This study develops and implements resource- and performance-based renewable energy supply curves for solar photovoltaics, onshore wind and offshore wind in the TIMES-PL energy system model for Poland. These supply curves are coupled with time-dependent techno-economic assumptions in TIMES-PL, allowing the modelled attractiveness of individual renewable resource classes to change across model years. The proposed approach combines spatial resource assessment, GIS-based data processing and differentiated hourly capacity factor profiles. The supply curves were constructed using data from the JRC ENSPRESO database, the PVGIS interface and the Copernicus Climate Data Store, with QGIS applied to classify renewable resource potential according to regional conditions, wind farm location and photovoltaic panel orientation. Two model scenarios were compared: a base scenario without supply curves and a scenario with implemented supply curves. The results show that incorporating spatial and technological constraints changes the modelled optimal capacity mix, although the overall system-level differences remain moderate. Accordingly, the results should be interpreted primarily in terms of installed capacity expansion rather than as a full comparison of system costs, electricity generation, unit dispatch or balancing effects. The total installed capacity in the supply-curve scenario is 1.91–3.44 GW higher than in the base scenario, corresponding to less than 3% of total system capacity. This increase results from the model being required to use renewable resource classes with lower capacity factors once the most favourable potentials are fully utilised. This study demonstrates that renewable energy supply curves can improve the representation of spatially differentiated renewable deployment options in long-term national energy system modelling. Full article

Large-scale breeder chicken farms generate high-moisture manure, and all-in/all-out management can constrain continuous manure handling, especially during cold northern winters. This study proposed and evaluated a staged resource-recovery pathway for breeder chicken manure under all-in/all-out farm management. The pathway consisted of an implemented on-farm primary aerobic fermentation stage for rapid reduction and sanitization, an implemented centralized secondary aerobic fermentation stage for standardized organic fertilizer production, and a proposed solar-greenhouse-assisted low-temperature module for seasonal continuity support. System performance was assessed through a practice-based case evaluation using enterprise operational records, field investigations, and routine monitoring data on manure generation, process parameters, product quality, and logistics/cost indicators. The primary stage showed a relatively stable operational window across case farms, with fresh manure moisture contents of 85–90%, compost temperatures increasing from approximately 30 °C to 60 °C before declining to about 40 °C, and pH values ranging from 7.0 to 9.5, while batch duration and moisture-control pathways varied among farms. The secondary stage demonstrated standardized downstream processing capacity; the tested organic fertilizer complied with NY/T 525-2021, while the bio-organic fertilizer specifications met the benchmark requirements of NY 884-2012. The conceptual winter continuity-support module was discussed as a conceptual engineering supplement requiring future operational validation. Overall, the evaluated pathway may provide a practice-based reference for sustainable manure management, standardized fertilizer production, and circular agricultural resource recovery under intensive breeder chicken production conditions. Full article