Search Results (168)

This study develops a probabilistic carbon-aware optimization framework for residential 24/7 carbon-free electricity (CFE) under uncertain load, PV generation and dynamic grid carbon intensity. Historical half-hourly monitoring data are represented through KDE-Copula scenario generation, while grid carbon factors are described by time-series decomposition and ARMA-based residual scenarios. A two-stage mixed-integer linear program jointly sizes and dispatches battery energy storage (BESS) and thermal energy storage (TES) by minimizing annualized lifecycle CO₂ emissions. The results show that coordinated BESS–TES operation improves PV utilization and avoids carbon-intensive grid imports, especially in winter. In the examined capacity range, increasing BESS capacity from 0 to 100 kWh reduces annual lifecycle emissions from approximately 8600 to 6000 kg CO₂ yr⁻¹, corresponding to about a 30% reduction, whereas the marginal benefit of additional TES is constrained by DHW demand and its embodied emissions. Electrical storage is therefore the principal carbon-shifting resource, while a moderately sized TES complements it by moving heat-pump operation toward low-carbon and PV-rich periods. The framework provides a practical basis for carbon-aware design of residential electrification systems. Full article

Battery energy storage systems (BESSs) are increasingly coupled with photovoltaic (PV) generation, yet their deployment is governed by economics rather than by technical feasibility. This review synthesises 122 indexed Q1/Q2 studies (2016–2026) on the financial payback and return on investment of PV-coupled storage, extending the analysis to vehicle-to-grid (V2G) integration. Records retrieved from Scopus, Web of Science, IEEE Xplore, and the MDPI portal were screened to peer-reviewed journals, assigned to ten thematic clusters, appraised against a ten-criterion reporting-transparency rubric, and combined by narrative synthesis. Reported payback periods range from a few years to beyond the asset’s service life, and the levelized cost of storage spans roughly 170–350 USD/MWh. In the reviewed corpus, the retail-to-export price spread, the stacking of self-consumption, arbitrage, grid-service revenues, and degradation-aware operation each move returns in a consistent direction, whereas which of them binds hardest is a property of the case rather than a ranking the evidence supports. V2G is almost always analysed in isolation from stationary storage; an illustrative harmonised comparison indicates that it substitutes for stationary capacity rather than adding to it. The review maps the combined PV+BESS+V2G revenue stack and identifies an integrated, degradation-corrected, policy-sensitive economic model as the principal research gap. Full article

Poland’s rapid expansion of renewable energy sources (RES) and of distributed photovoltaics (PVs) in particular has increasingly collided with a finite and unevenly distributed resource: available capacity in the electricity distribution grid. This paper examines grid-connection capacity as a spatial constraint on RES development in Poland, combining a national overview of grid congestion (2023–2026) with a quantitative case study of the 52 coherent 110 kV node groups administered by ENERGA-OPERATOR S.A. under the statutory reporting regime of Article 7(8l) of the Polish Energy Law. The node-group figures are not an observed annual series: the operator’s disclosure gives the capacity available in the base year of 2018, together with the capacity it planned to make available in each year up to 2023, so the analysis characterises the spatial distribution implied by the operator’s own five-year development plan rather than realised outcomes. Using descriptive statistics, a Gini coefficient, Lorenz curve analysis, and an original growth index (the ENERGA Grid-Connection Growth Index, EGCI) developed for this study, we show that available connection capacity is markedly unequally distributed across node groups (Gini = 0.48 in 2018, rising to 0.51 by the 2023 planning horizon) and that this inequality has a distinct regional pattern: the Olsztyn branch, corresponding to the Warmia–Mazury region (Warmińsko-mazurskie voivodeship), more than doubles its share of the operator’s total available capacity under the plan (from 12.4% to 29.3%, or from 75 MW to 365 MW in absolute terms), moving from the third-lowest to the highest planned capacity among the operator’s six branches, while two of its nine node groups—including the regional capital’s own—receive no increase at all under the plan. A voivodeship-level spatial analysis, mapped using verified administrative boundary data, shows a pattern consistent with this at a national scale: Warmia–Mazury has the second-lowest installed generation capacity of Poland’s 16 voivodeships despite favourable land and irradiation conditions for photovoltaic development. Bootstrap analysis confirms this robust level of inequality while indicating that, with 52 units, the five-year increase is best read as a consistent tendency rather than as a statistically established widening; the operator-level values are specific to ENERGA-OPERATOR and are not numerically generalisable to Poland’s other four operators. We discuss the implications of these findings for grid-investment planning, RES-integration policy (cable pooling, storage co-location, curtailment reduction), and the energy-security dimension of an increasingly decentralised, weather-dependent generation system, and we identify concrete directions for future quantitative and spatial research. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems. Full article

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article

Red sour soup dumpling filling, a prepared meat product from Guizhou, China, requires validated shelf-life control strategies for industrial-scale commercialization. This study investigated a composite preservation system of sodium D-isoascorbate, phospholipids, and sodium lactate using a Box–Behnken design, and evaluated its effects on color, lipid oxidation (PV, TBARS), TVB-N, texture, sensory scores, APC, and coliforms during accelerated shelf-life testing. Key indicators were identified by correlation analysis, and shelf life was predicted using log-logistic AFT and temperature-state-dependent kinetic models. The optimized formulation (0.27% sodium D-isoascorbate, 0.11% phospholipids, and 0.05% sodium lactate) significantly inhibited lipid oxidation, retarded color deterioration, maintained texture, and delayed sensory decline. Sensory shelf life (SSL₅₀) at 5 °C, −5 °C, and −18 °C extended from 4.44 d to 6.49 d, 16.70 d to 22.90 d, and 108.99 d to 136.76 d, whereas PV-based shelf life extended from 11.0 d to 14.5 d, 15.8 d to 18.8 d, and 124.3 d to 224.3 d. The relative extension of sensory shelf life decreased as storage temperature decreased, whereas PV shelf-life extension surged at low temperatures, indicating a decoupling of lipid oxidation from textural deterioration under frozen conditions. Therefore, shelf-life prediction for dumpling fillings should move away from arbitrary relative thresholds and instead establish sensory-calibrated absolute chemical thresholds, thereby helping align chemical shelf-life indicators with consumer-relevant quality endpoints. Full article

Industrial energy transition has moved from pilot deployment to system integration, where renewable supply must be assessed together with process fit, organisational maturity, and uncertainty. This study proposes a risk-aware assessment framework integrating ISO 50001 energy-management maturity, ISO 31000 risk-management logic, a digital-twin-ready operational architecture, scenario simulation, Conditional Value-at-Risk (CVaR), and multi-criteria decision analysis. The study does not report a live plant-level digital twin or empirical survey validation. Instead, it specifies a five-layer implementation architecture, uses a synthetic survey-like dataset solely to demonstrate parameter recovery, and applies 350 Monte Carlo replications to an industrial energy hub comprising photovoltaic and wind generation, battery storage, and optional Power-to-H₂-to-Power storage. The quantitative workflow is reported with explicit equations, input assumptions, random seed, CVaR estimator, TOPSIS weights, and weight-sensitivity analysis. Under the adopted assumptions, the PV–wind–battery configuration achieved the lowest mean cost and CVaR, whereas hydrogen storage substantially reduced curtailment but increased mean cost and tail risk without materially reducing grid purchases. These results are conditional on the stated model assumptions and should not be generalised as empirical evidence. The framework supports structured investment and operational assessment by linking technical performance, organisational readiness, and cost–risk–decarbonisation trade-offs. Full article

To address the limitations of traditional day-ahead bidding strategies in handling the time-varying uncertainty of photovoltaic output, and considering that single-point forecasts are insufficient for reliable risk-based decision-making, this paper proposes a day-ahead bidding strategy for PV power plants based on dynamic error-band optimization. First, a dynamic uncertainty quantification method based on dual-model prediction discrepancy is proposed. It couples two complementary forecasting mechanisms—Long Short-Term Memory, and Seasonal Autoregressive Integrated Moving Average—and utilizes the Dynamic Time Warping algorithm to extract their discrepancy as a dynamic input for subsequent risk assessment and decision-making. Secondly, based on this uncertainty indicator, a probabilistic mapping model is constructed to link prediction uncertainty to the risk of power violation, translating the abstract prediction discrepancy into a concrete economic risk probability. Finally, considering the trade-off between economic benefits and security, a dynamic error-band optimization mechanism is introduced to adaptively determine the bidding margin at different time periods. Case results for a 20 MW PV plant show that the dynamic strategy reduces the number of violation events to zero in the tested daily bidding case, compared with four violations under a fixed 5% error band and one violation under a fixed 10% error band. The corresponding economic revenue increases by 5.3% and 11.2% relative to the fixed 5% and fixed 10% strategies, respectively. Full article

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Accurate photovoltaic (PV) power forecasting is essential for the efficient operation of residential energy systems and microgrids, as reliable short-term predictions enable improved energy scheduling, demand management, and operational planning in distributed energy environments. In this study, one-hour-ahead forecasting of residential PV power generation is investigated using real-world data collected from multiple households within an Irish energy community. Several deep-learning architectures, including long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), CNN–LSTM hybrid networks, and attention-based LSTM models, are evaluated and compared with a seasonal autoregressive integrated moving average (SARIMA) statistical model. A sliding-window approach is employed to transform the PV time series into a supervised learning problem. To ensure statistical robustness, deep-learning models are evaluated using a multi-run framework, and results are reported as mean ± standard deviation based on MAE, RMSE, MAPE, and R² metrics across multiple households. The results indicate that deep-learning models achieve consistently strong forecasting performance, with GRU frequently providing the most reliable predictions across several households. For instance, in House 5, GRU achieved an RMSE of 142.02 ± 1.87 W and an R² of 0.694 ± 0.008, while in Houses 11 and 13 it attained R² values of 0.837 ± 0.002 and 0.835 0.08, respectively. However, performance varied across households, reflecting the influence of data variability and generation patterns on model effectiveness. In comparison, the SARIMA model demonstrated competitive performance and, in certain cases, outperformed deep-learning models. For example, in House 4, it achieved the lowest RMSE of 90.68 W and the highest R² of 0.709. Overall, these findings highlight that while deep-learning models offer greater adaptability and stability, statistical models remain effective for more regular PV generation patterns. Consequently, the study emphasizes the importance of evaluating forecasting models under realistic household-level conditions and demonstrates that both deep-learning and statistical approaches can provide short-term PV forecasting. Full article

With the growing penetration of photovoltaic (PV) generation, robust uncertainty characterization is essential for secure operation, economic dispatch, and flexibility planning. This review surveys PV scenario generation from three perspectives: (i) explicit probabilistic approaches (distribution fitting, Copula-based dependence modeling, autoregressive moving average (ARMA)-type time-series methods, and clustering/dimensionality reduction), (ii) deep generative models (GANs, VAEs, and diffusion models), and (iii) hybrid Statistical Relational AI (SRAI) frameworks. We discuss the strengths of explicit models in interpretability and tractability, and their limitations in representing high-dimensional nonlinear, multimodal, and multiscale spatiotemporal dependencies. We also examine the ability of deep generative methods to synthesize diverse scenarios across meteorological regimes and multiple sites, while noting persistent challenges in interpretability, physical consistency, and deployment. To bridge these gaps, we outline an SRAI-oriented integration pathway that embeds statistical structure, meteorology–power relations, spatiotemporal coupling, and operational constraints into generative architectures. Finally, we highlight directions for future research, including unified evaluation protocols, cross-regional data collaboration, controllable extreme-scenario generation, and computationally efficient generative designs. Full article