Impacts of partial shading and non-uniform irradiance in photovoltaic (PV) strings, and thus, arrays, can be mitigated by Array reconfiguration (ARC). On the other hand, the power clipping and the saturation of a PV inverter can lead to energy losses in grid-connected systems with limited maximum power point trackers (MPPTs) in their inverters. However, most ARC researchers focus on mismatched-oriented reconfiguration approaches that minimize the mismatch in each PV string to make them almost uniform. These conventional approaches aim to optimize the proxies, not the final alternating-current (AC) yield energy. Therefore, we proposed a string-level energy-first ARC method based on an Acceleration-Enhanced River Formation Dynamics (AE-RFD) algorithm. The proposed method directly maximized the delivered energy while maintaining current constraints and MPPT power limits. The proposed energy-first framework optimized the array configuration at the grid-tied inverter interface, maximizing delivered energy of multi-inputs and AC-side clipping constraints. A real grid-tied PV system of 15 kVA, 6 strings, and 3 MPPTs was utilized first to evaluate the proposed method with disturbed irradiance conditions for different MPPT architectures and a second benchmark PV system of 24 strings and 8 MPPTs. Our simulation results confirmed the effectiveness of the proposed method by demonstrating energy gains exceeding 5.5% with significant clipping reduction and enhancing the mismatch index. Comparisons with the ARC that is based on particle swarm optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) indicated that the proposed AE-RFD-based method exhibits improved robustness and stable convergence behavior.

This study assesses the techno-economic viability of grid-connected variable renewable energy (VRE) systems for small-sized hotels in low-wind areas of developing tropical countries where the sustainable tourism infrastructure is subjected to unique resource constraints. This paper compares three optimization methods-linear programming (LP), simulated annealing (SA), and the genetic algorithm (GA)-to determine the optimal levelized cost of energy (LCOE), component sizing, and generation mixes across four regulatory scenarios, using the hourly load and resource profiles of a planned hotel on Nusa Penida Island, Bali, Indonesia. Methodological comparison of these techniques unveils distinct computational trade-offs, with LP providing the fastest optimization (54.35 to 162.16 s), drastically outperforming iterative metaheuristics such as GA, which required up to 772.18 s. Regarding energy performance, all photovoltaic (PV)-integrated configurations outperform the grid-only benchmark of $0.0824/kWh. The PV–grid system yields the lowest LCOE ($0.0637 to $0.0714/kWh), reducing costs by 3.2% to 15% compared with the grid. Notably, hybrid wind–PV–grid configurations are economically feasible ($0.0662 to $0.0798/kWh), successfully meeting demands even under a stringent 25% obligatory renewable fraction. Without sellback incentives, the VRE mix ranges from 30.1% to 38.2% for PV–grid systems and reaches up to 49% for wind–solar–grid setups, whereas elevated sellback rates strongly boost VRE adoption. This study has practical implications for policymakers and hospitality investors by showing that while solar PV remains the most cost-effective asset, small wind turbines can synergistically complement grid–VRE systems in low-wind tropical environments when supported by strategic market mechanisms.

To meet the enhanced demand for renewable energy sources, high-performance and sustainable energy storage devices are needed. In this study, KOH activation and the controlled redox deposition of MnO₂ at different mass loadings (10, 25, and 40 wt.%) were used to synthesize biomass-based activated carbon (AC) and hybrid AC/MnO₂ electrodes from pomegranate peel biomass. The porous structure of the uniformly distributed MnO₂ nanoparticles was confirmed by detailed structural analysis using field emission scanning electron microscopy (FESEM), Brunauer-Emmett-Teller (BET) analysis, and X-ray diffraction (XRD) techniques. Electrochemical analysis was performed using a three-electrode system with 1M Na₂SO₄ as the electrolyte. The results showed that the AC/MnO₂-25 electrode possessed the highest electrochemical response with a specific capacitance of 394 Fg⁻¹ at 1 Ag⁻¹, along with good rate capability and low internal resistance. Even after 5000 cycles, the capacitance retention was 94.5%. The results confirmed a good electrochemical response with a Coulombic efficiency of approximately 98%. The enhanced electrochemical response was due to the combination of high-surface-area activated carbon with MnO₂ nanoparticles, which contributes to electric double-layer capacitance and pseudocapacitance. The results confirmed the suitability of pomegranate peel biomass as a sustainable source of biomass to produce high-performance electrode materials for the fabrication of high-performance supercapacitor devices.

Spark-ignition engines remain widely used in passenger cars, motorcycles, hybrid vehicles, and light-duty transport, making fuel flexibility and emission reduction important during the transition to low-carbon mobility. Methanol-gasoline blends have attracted attention as alternative fuels because methanol has high oxygen content, high octane quality, strong charge-cooling capacity, and flexible production routes. This review summarizes recent progress in methanol-gasoline blends for spark-ignition engines, covering fuel properties, blending ratios, combustion behavior, engine performance, regulated and unregulated emissions, life-cycle impacts, and application prospects. The literature shows that methanol addition can enhance knock resistance, promote oxygenated combustion, reduce soot-forming tendency, and support higher compression ratios, optimized ignition timing, and lean-burn operation. However, its lower energy density increases fuel consumption, while water affinity, phase separation, cold-start difficulty, corrosion, and material compatibility remain key barriers. Emission responses are strongly condition-dependent. CO and particulate emissions are often reduced, whereas HC and NO_x vary with operating condition, mixture preparation, injection strategy, and after-treatment performance. Formaldehyde, unburned methanol, and cold-start oxygenated emissions require further attention. Life-cycle benefits depend mainly on methanol production pathways. Low-level blends are more suitable for near-term use in existing engines, while higher blends require optimized engines, compatible fuel systems, green methanol supply, infrastructure readiness, and cost reduction.

Accurate forecasting of electrical, heating, and cooling demands is crucial for the optimal design, operation, and coordinated planning of multi-energy coupling systems. In this study, we present a short-term multi-energy load forecasting method for integrated energy systems based on a temporal convolutional network and multi-task learning. First, the Pearson correlation coefficient was used to analyze the correlations among meteorological variables. Then, the maximum overlap discrete wavelet transform (MODWT) module decomposed the input time-series data into components at different frequency levels. These decomposed time series datasets were input into the multi-task learning model for sparse sharing. Subsequently, the learned time series datasets of different frequencies were input into the temporal convolutional network for temporal scale feature extraction. To improve prediction accuracy, the improved energy valley optimizer (IEVO) was introduced to optimize the model parameters. The feature vectors output by the optimized temporal convolutional network were then fed into the corresponding Transformer models for adaptive weight adjustment. Finally, the MODWT module reconstructed the predictions at different frequency levels to obtain the final multi-energy load forecasting results.

Monolithic lithium-ion (Li-ion) battery designs continue to pose challenges in meeting diverse application requirements. This paper proposes a solution by introducing a novel hybrid battery architecture that considers combining two different Li-ion chemistries to mitigate each other’s limitations. The potential of this concept is demonstrated by considering the application of unmanned aerial vehicles (UAVs) used for emergency deployment, which require high energy density during deployment and good longevity during their long off-duty periods. For the given application, the study identifies the optimal capacity proportions for the hybrid architecture using nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) chemistries to balance energy availability and degradation, while keeping the change in weight, volume, and cost within acceptable limits compared to a typical single-chemistry battery pack. The optimal configuration showed an improvement of up to 35% in energy availability with a mere increase of less than 1% in calendric degradation compared to existing single-chemistry battery packs used for the same application. However, the weight and volume were penalised by 8%, while the cost dropped by around 4%. This result shows that no single hybrid configuration is universally optimal. The paper thereby establishes the broad potential that the architecture carries in a wide context.

Accurate state-of-health (SOH) prediction of lithium batteries is vital for guaranteeing the safe operation of battery systems and extending their service life. However, previous data-driven models were usually established based on ideal laboratory environments, which would severely restrict their generalization ability in real-world applications. This paper develops an SOH prediction method based on feature decoupling and a heterogeneous stacking ensemble learning framework (HSELF). First, the validation dataset was constructed by using accelerated cycle aging data with three levels of gradient noise. Second, features based on incremental capacity analysis (ICA) were extracted from a selected charging voltage interval to capture underlying battery degradation characteristics. Then, a feature decoupling strategy based on noise robustness decay rate (NRDR) and the corresponding differentiated feature input spaces were constructed for the heterogeneous stacking model. Additionally, a nested inner leave-one-battery-out cross-validation (LOBOCV) strategy was developed to achieve an unbiased evaluation of generalization under small-sample conditions. Finally, the ridge regression method was adopted as the meta-learner to integrate the outputs of the base models in the HSELF, leveraging their complementary strengths for mitigating prediction errors. Experimental results based on the LISHEN dataset successfully demonstrated the effectiveness of the developed SOH prediction method based on feature decoupling and HSELF. Under noise-free and typical industrial noise (𝜎≤10 mV ) conditions, the accuracy of the developed SOH prediction method would be significantly superior to that of individual base models. Furthermore, although the extreme noise ( 𝜎= 20 mV ) causes severe performance degradation with individual base models, the developed SOH prediction method can consistently maintain high prediction stability (𝑅² > 0.93). In addition, experimental results based on an additional HNEI dataset also demonstrate that the developed SOH prediction method can achieve an effective balance among prediction accuracy, noise robustness, and cross-dataset robustness.

A correction on A hybrid NMC–LFP lithium-ion battery architecture for UAV applications: Maximising energy readiness and minimising calendar aging. By Vishal Ram, Vishal Arvind and Surender Reddy Salkuti. AIMS Energy, 2026, Volume 14, Issue 4: 905–922. Doi: 10.3934/energy.2026036 The authors would like to make the following corrections to the published paper [1]. On page 905, the affiliations of the first author (Vishal Ram) and the second author (Vishal Arvind) were updated. The previous affiliation “Lehrstuhl für Elektrische Energiesysteme, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany” was replaced by: “Independent Researcher, Erlangen, Germany”. The updated author and affiliation information is as follows: Vishal Ram¹, Vishal Arvind¹ and Surender Reddy Salkuti^2,* 1. Independent Researcher, Erlangen, Germany 2. Department of Global Railroad, Woosong University, Jayang Dong, Dong-Gu, Daejeon 34606, Republic of Korea