Search Results (6,294)

The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for dispatch and reserve decisions because they do not quantify risk. This study investigates probabilistic forecasting of short-horizon solar generation using quantile regression on a public dataset of solar output and meteorological variables. This study proposes a hybrid attention–convolution model that combines an attention-based encoder to capture long-range temporal dependencies with a causal temporal convolution module that extracts fast local fluctuations using only past information, preventing information leakage. The two representations are fused and decoded jointly across multiple future horizons to produce consistent quantile trajectories. Experiments against representative machine-learning and deep-learning baselines show improved probabilistic accuracy and competitive central forecasts, while illustrating an important sharpness–calibration trade-off relevant to risk-aware grid operation. Key novelties include a multi-horizon quantile formulation at 15 min resolution for one-hour-ahead PV increments, a HybridCNN–STTransformer that fuses causal temporal convolutions with Transformer attention, and a horizon-token decoder that models inter-horizon dependencies to produce consistent multi-step quantile trajectories; reliability/sharpness diagnostics and post hoc calibration are discussed for operational risk-aware use. Full article

Accurate photovoltaic (PV) power forecasting under extreme weather conditions remains challenging due to the non-stationary and multi-modal nature of meteorological influences. This study proposes a novel four-stage learning framework integrating signal decomposition, hyperparameter optimization, temporal dependency learning, and residual compensation to enhance forecasting resilience during El Niño–Southern Oscillation (ENSO) climate transitions. The framework employs CEEMDAN for fluctuation mode decoupling, TOC for global hyperparameter optimization, Transformer model for spatiotemporal dependency learning, and EEMD-GRU for error correction. Experimental validation utilized a comprehensive dataset from Australia’s Yulara power station comprising 104,269 samples at 5 min resolution throughout 2024, covering a complete ENSO transition period. Compared against baseline Transformer model and CNN-BiLSTM models, the proposed framework achieved nRMSE of 1.08%, 7.04%, and 2.81% under sunny, rainy, and sandstorm conditions, respectively, with corresponding R² values of 0.99981, 0.99782, and 0.99947. Cross-year validation (2023 to 2025) demonstrated maintained performance with nRMSE ranging from 4.68% to 15.88% across different temporal splits. The framework’s modular architecture enables targeted handling of distinct physical processes governing different weather regimes, providing a structured approach for climate-resilient PV forecasting that maintains 2.56% energy consistency error while adapting to rapid meteorological shifts. Full article

Studying bird, insect, and other wildlife interactions with photovoltaic (PV) solar energy facilities is difficult due to limited multi-season, multi-site data. Researchers can address such data gaps by combining passive monitoring and artificial intelligence (AI). As a part of the development of AI-enabled avian–solar monitoring software, we collected over 19,000 h of daytime videos at five PV sites across three U.S. regions between 2019 and 2024. We applied a moving object detection and tracking (MODT Version 1) AI model we developed earlier to 4373 h of the footage to extract moving objects in video frames, and human reviewers interpreted the model output and identified 68,646 bird, 25,968 insect, and 169 other wildlife instances to generate the training/validation dataset. We analyzed the data by site, region, and season, considering ground cover and landscapes. Songbirds were most common, with raptors as the next most frequent group. Most notably, no bird collisions were confirmed in our observations collected from the videos. Birds most often flew over or near panels, with the highest observations in the Midwest and Northeast (approximately 30 observations per hour on average) and fewer in the desert Southwest. Other behaviors included perching, foraging, and nesting. Bird abundance peaked during breeding and migration seasons. AI-assisted video monitoring proved effective for non-invasively studying flying wildlife at solar facilities to inform ecologically mindful energy development. Full article

Smart microgrids are localized energy systems that integrate distributed energy resources, such as photovoltaics (PVs) and battery storage, to optimize energy use, enhance reliability, and minimize environmental impacts. This paper investigates the operation of a smart microgrid installed at the Hellenic Mediterranean University (HMU) campus in Heraklion, Crete, Greece. The system, consisting of PVs and battery storage, operates under a zero feed-in scheme, which maximizes on-site self-consumption while preventing electricity exports to the main grid. With increasing PV penetration and growing grid congestion, this scheme is an increasingly relevant strategy for microgrid operations, including university campuses. A properly sized PV–battery microgrid operating under zero feed-in operation can remain financially viable over its lifetime, while additionally it can achieve significant environmental benefits. The study performed at the HMU Campus utilizes measured hourly data of load demand, solar irradiance, and ambient temperature, while PV and battery components were modeled based on real technical specifications. The study evaluates the system using financial and environmental performance metrics, specifically net present value (NPV) and annual greenhouse gas (GHG) emission reductions, complemented by sensitivity analyses for battery technology (lead–carbon and lithium-ion), load demand levels, varying electricity prices, and projected reductions in lithium-ion battery costs over the coming years. The findings indicate that the microgrid can substantially reduce grid electricity consumption, achieving annual GHG emission reductions exceeding 600 tons of CO₂. From a financial perspective, the optimal configuration consisting of a 760 kWp PV array paired with a 1250 kWh lead–carbon battery system provides a system autonomy of 46% and achieves an NPV of EUR 1.41 million over a 25-year horizon. Higher load demands and electricity prices increase the NPV of the optimal system, whereas lower load demands enhance the system’s autonomy. The anticipated reduction in lithium-ion battery costs over the next 5–10 years is expected to provide improved financial results compared to the base-case scenario. These results highlight the techno-economic viability of zero feed-in microgrids and provide valuable insights for the planning and deployment of similar systems in regions with increasing renewable penetration and grid constraints. Full article

Uncovering the interdependencies among barrier factors and pinpointing the most critical obstacles are essential to overcoming the resistance encountered by photovoltaic (PV) integration into electricity markets. This study first employs grounded theory to identify and categorize the key barriers impeding PV participation, thereby constructing a comprehensive barrier factor model. Subsequently, Interpretive Structural Modeling (ISM) is applied to systematically analyze the interrelations and hierarchical structure among these barriers. The results reveal that: (1) The complex system of PV participation comprises 15 distinct barriers, which can be grouped into 4 overarching categories: economic and cost-related challenges, policy and regulatory uncertainties, technological and infrastructure constraints, and environmental and resource limitations. (2) These barriers form a six-tier hierarchical structure, reflecting their layered influence. (3) Root-level barriers—such as inadequate government fiscal support and the absence of a comprehensive coordination mechanism—play a foundational role in hindering progress. In response, this study proposes policy recommendations, including establishing a unified and effective coordination framework to align renewable energy policies and formulating standardized guidelines for PV panel recycling. Full article

Photovoltaic (PV) systems play a significant role in renewable energy production. Due to the nonlinear and multi-modal nature of PV models, using accurate model parameters is crucial. In recent years, metaheuristic algorithms have been utilized to estimate these parameter values. While established metaheuristics like Genetic Algorithms (GAs) incorporate mechanisms such as mutation and selection to maintain diversity, they may still encounter challenges related to premature convergence when navigating the complex, multi-modal landscapes of PV parameter estimation. In this study, the performance of the previously proposed Bipolar Improved Roosters Algorithm (BIRA), which enhances search efficiency through a bipolar movement strategy to balance exploration and exploitation phases, is evaluated. BIRA is compared with the Simple GA (SGA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) in estimating the electrical parameters of a single-diode PV model using experimental current-voltage data. The experimental results demonstrate that BIRA outperforms its competitors, achieving the lowest Root Mean Squared Error (RMSE) of 1.0504 $\times {10}^{-3}$ for the Siemens SM55 and 4.8698 $\times {10}^{-4}$ for the Kyocera KC200GT modules. Furthermore, statistical analysis using the Friedman test confirms BIRA’s superiority, ranking it first among all tested algorithms across both datasets. These findings indicate that BIRA is a effective and reliable tool for accurate PV parameter estimation. Full article

Hot-spots represent a significant failure mechanism in photovoltaic (PV) modules, typically attributable to electrical mismatching. However, thermo-optical degradation of the encapsulant, including discoloration and delamination, can both trigger and amplify mismatch by inducing localized optical losses and temperature rise. The present paper proposes a compact circuit-level electro-thermal–optical model that explicitly captures the short-term closed-loop interaction between mismatching, cell temperature, and temperature-dependent optical properties. The photogenerated current is formulated as a function of irradiance, cell temperature, and encapsulant degradation, enabling dynamic feedback between heating and optical losses. Numerical simulations are carried out on a commercial 40-cell PV module under four representative operating static scenarios. The results demonstrate that, even in the absence of shading, optical degradation can generate multimodal P–V characteristics, drive cells into reverse bias, and produce hot-spots. When optical degradation coexists with irradiance mismatch, the feedback loop significantly amplifies mismatching and shifts the maximum power point toward thermally unsafe operating conditions. These findings demonstrate that maximizing instantaneous power does not necessarily maximize lifetime energy yield, underscoring the need for thermal-aware MPPT strategies and providing a practical framework for early detection of thermo-optical faults in PV modules. Full article

The transition toward sustainable energy in Arctic and subarctic regions requires innovative approaches that account for both the unique geographical conditions and the economic and policy challenges associated with isolated power systems. This study examines how net energy metering (NEM) and net billing schemes influence distributed solar photovoltaic (PV) adoption and financial performance among utilities in Alaska’s Railbelt. The Railbelt, which supplies power to three-quarters of the state’s population, remains heavily reliant on natural gas and exhibits limited renewable penetration compared to other arctic regions. Using a stochastic risk-based modeling framework with Monte Carlo simulations and the Bass diffusion model, the analysis estimates the 15-year financial impacts of different NEM adoption scenarios on utilities. Results show that while NEM drives PV adoption through higher compensation for exported generation, it also increases potential revenue losses for utilities compared to net billing. Policy innovations like those introduced in Alaska’s House Bill 164 (HB 164), which establishes a reimbursement fund to mitigate utility revenue losses, indicate that regulatory work is being designed to balance distributed generation incentives with economic sustainability. This work provides a baseline for understanding how a policy framework influences both utility and consumer economics in terms of NEM and solar PV adoption in Arctic and subarctic systems. Full article

This study presents an AI-driven computational framework for optimizing the orientation and spatial deployment of photovoltaic (PV) systems installed on uneven terrain, with the objective of enhancing energy efficiency and supporting sustainable energy development. The proposed methodology integrates PVsyst-based numerical simulations with statistical modeling and bio-inspired heuristic optimization algorithms, forming a hybrid machine learning–assisted decision-making approach. A heuristic–parametric optimization strategy was employed to evaluate multiple tilt and azimuth configurations, aiming to maximize specific energy yield and overall system performance, expressed through the performance ratio (PR). The model was validated using site-specific climatic data from Veracruz, Mexico, and identified an optimal azimuth orientation of approximately 267.3°, corresponding to an estimated PR of 0.8318. The results highlight the critical influence of azimuth orientation on photovoltaic efficiency and demonstrate strong consistency between simulation outputs, statistical analysis, and intelligent optimization results. From an industrial perspective, the proposed framework reduces planning uncertainty and energy losses associated with suboptimal configurations, enabling more reliable and cost-effective photovoltaic system design, particularly for installations on uneven terrain. Moreover, the methodology significantly reduces planning time and potential installation costs by eliminating the need for preliminary physical testing, offering a scalable and reproducible AI-assisted tool that can contribute to lower levelized energy costs, enhanced system reliability, and more efficient deployment of photovoltaic technologies in the renewable energy industry. Future work will extend the model toward a multivariable machine learning framework incorporating tilt angle, climatic variability, and photovoltaic technology type, further strengthening its applicability in real-world environments and its contribution to Sustainable Development Goal 7: affordable and clean energy. Full article

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs). Full article

The transition to renewable energy, mainly via the use of PV (photovoltaic) systems, is essential for addressing global concerns related to climate change, energy security, and sustainability. Conventional Maximum Power Point Tracking (MPPT) techniques, particularly Perturb and Observe (P&O) and Incremental Conductance methods, rely on fixed-step gradient-based control, which leads to steady-state oscillations around the maximum power point, slow convergence during rapid irradiance and temperature variations, and inaccurate tracking under partial shading conditions. These technical limitations often cause the controller to deviate from the global maximum power point, resulting in reduced dynamic efficiency, increased power losses, and degraded power quality in practical PV applications. To overcome these limitations, this research proposes a hybrid optimization model that incorporates ACO (Ant Colony Optimization) and an ANN (Artificial Neural Network) to enrich the effectiveness of MPPT in PV systems. The proposed model is designed to dynamically adapt to variations in solar irradiance and temperature, effectively addressing the inadequacies present in the conventional techniques and also improving the MPPT efficiency in PV systems. By leveraging the unique strengths of both ACO and ANN, the model significantly improves energy extraction and also ensures robustness against environmental fluctuations. Simulation results demonstrate that the proposed ACO–ANN MPPT framework achieves a total harmonic distortion (THD) of 1.39%, representing a reduction of approximately 34–70% compared to conventional and recent AI-based MPPT techniques, while simultaneously delivering higher voltage stability, faster convergence, and increased maximum power extraction. This contribution is vital in paving the way for future advancements in renewable energy systems and provides a more reliable approach to solar power optimization, which can greatly aid in achieving sustainable energy goals. Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

This paper presents two enhanced variants of the Artificial Protozoa Optimizer (APO), namely the Adaptive Balanced Artificial Protozoa Optimizer (AB-APO) and the Fractional Calculus-Enhanced Artificial Protozoa Optimizer (FC-APO), for optimal multi-Distributed Energy Resources (DERs) planning in smart radial distribution networks. The proposed framework addresses the coordinated allocation of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) units, and Battery Energy Storage Systems (BESS). The AB-APO introduces an adaptive balancing mechanism that dynamically regulates exploration and exploitation to improve convergence stability and robustness, while the FC-APO incorporates fractional-order dynamics to embed long-memory effects, enhancing numerical stability and search smoothness. The proposed optimizers are evaluated on the IEEE-33 and IEEE-69 bus systems under eight DERs penetration scenarios. Simulation results demonstrate significant reductions in real and reactive power losses, improved voltage profiles, and effective mitigation of EV-induced network stress. Real power loss reductions exceeding 54%, 38.53%, 53.78%, 38.20%, 61.68%, and 60.72% are achieved for the IEEE-33 system, while reductions of 64.32%, 63.51%, 64.33%, 63.51%, 67.31%, and 67.04% are obtained for the IEEE-69 system across Scenarios 3–8. Overall, the results highlight the effectiveness of adaptive balancing and fractional-order modeling in strengthening APO-based optimization and confirm the suitability of the AB-APO and FC-APO as efficient planning tools for future smart distribution networks. Full article

This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. We benchmark three univariate forecasting approaches for load demand, photovoltaic (PV) generation, and wind generation under a consistent 24-to-1 input setup, including GRU, LSTM, and a persistence baseline. We report point-forecast metrics (RMSE, MAE, and $R^2$) and also provide 90% prediction intervals (PI90) using conformal calibration to quantify uncertainty. To assess downstream impact, forecasts are coupled with a dual-branch MATLAB/Simulink microgrid model. One branch uses True profiles and the other uses forecast-driven Pred inputs, while both branches share the same rule-based EMS and BESS constraints. System performance is evaluated using time-series comparisons and monthly key performance indicators (KPIs) covering grid import and export, grid peak power, battery throughput, and state-of-charge (SoC) statistics. We further report an illustrative cost sensitivity under a flat tariff and a throughput-based degradation proxy. Results show that forecasting performance is target dependent. GRU achieves the best overall point accuracy for load and PV, whereas wind is strongly driven by short term persistence at the one hour horizon, and in this measurement only setup without meteorological covariates the persistence baseline can match or outperform the deep learning models. In the microgrid simulations, Pred and True trajectories remain qualitatively consistent, and SoC-related indicators and peak power remain comparatively consistent across months. In contrast, energy-flow indicators, especially grid export and battery throughput, show larger deviations and dominate the observed cost sensitivity. Overall, the findings suggest that compact hour-ahead forecasts can be adequate to preserve operational reliability under a constraint-driven EMS, while forecast improvements mainly translate into economic efficiency gains rather than reliability-critical benefits. Full article

Photovoltaic (PV) power forecasting has become a key tool for the intelligent management of electrical grids. Since the largest source of error in PV power forecasting originates from uncertainties in solar irradiance prediction, improving the accuracy of solar irradiance forecasts has emerged as an active research topic. This study evaluates multiple random tree-based model versions using a challenging dataset collected at globally distributed stations, spanning elevations from sea level to nearly 4000 m and covering a wide range of climate classes. The originality of the study lies in the synergistic contribution of two elements: the innovative inclusion of diffuse irradiance among the predictors and a comparative analysis of forecast quality across lowland and mountainous locations. In such environments, accurate solar resource forecasting is particularly important for the intelligent management of stand-alone PV systems deployed at high altitudes and in remote, off-grid areas. Overall, the results identify Extremely Randomized Trees (XTRc) as the best-performing model. XTRc achieves Skill Scores ranging from 0.087 to 0.298 across individual stations. The model accuracy remains high even at mountain stations, provided that sky-condition variability is low. Full article