Hasil untuk "Renewable energy sources"

Ghada E. Hegazy, Nadia A. Soliman, Yasser R. Abdel-Fattah et al.

Abstract Background This study investigates the potential of microbial fuel cells (MFCs) for bioelectricity generation from seawater and wastewater sources. It focuses on the isolation, identification, and statistical optimization of electrogenic bacteria from diverse environmental samples, aiming to enhance sustainable production of bioenergy. Results Four bacterial isolates were obtained from Max surface water, oil factory wastewater, Abu-Qir bottom sediment, and fish factory wastewater. 16 S rRNA gene sequencing identified these isolates as Stenotrophomonas sp. strain S2 (El-Max), Bacillus paralicheniformis strain O3 (Oil factory), Bacillus safensis strain QB (Abu-Qir), and Serratia sp.strain GH3 (Fish factory). Initial screening of microbial consortia showed promising bioelectricity generation, with voltage outputs ranging from 0.175 V to 0.542 V crosswise isolates. Statistical method using Plackett–Burman Design (PBD) screened the key factors influencing voltage production, including pH, time, oxygen, inoculum size, mediator, and resistance. Each isolate exhibited a distinct pattern of factor significance, yet the models for the four strains demonstrated excellent predictive power with R² values near 0.98 or higher. Conclusion These results underscore the strong potential of specific electrogenic bacterial strains, isolated from diverse wastewater sources, to enhance bioelectricity production in Microbial Fuel Cells (MFCs). The identification of critical operational parameters provides valuable insights for optimizing MFC performance. Together, these findings demonstrate the viability of MFCs as an effective dual-purpose technology for wastewater treatment and renewable energy production.

A. Kirby, T. Nishino, L. Lanzilao et al.

<p>Turbine–wake and farm–atmosphere interactions can reduce wind farm power production. To model farm performance, it is important to understand the impact of different flow effects on the farm efficiency (i.e. farm power normalised by the power of the same number of isolated turbines). In this study we analyse the results of 43 large-eddy simulations (LESs) of wind farms in a range of conventionally neutral boundary layers (CNBLs). First, we show that the farm efficiency <span class="inline-formula"><i>η</i>_f</span> is not well correlated with the wake efficiency <span class="inline-formula"><i>η</i>_w</span> (i.e. farm power normalised by the power of front-row turbines). This suggests that existing metrics, classifying the loss of farm power into wake loss and farm blockage loss, are not best suited for understanding large wind farm performance. We then evaluate the assumption of scale separation in the two-scale momentum theory <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx26">Nishino and Dunstan</a>, <a href="#bib1.bibx26">2020</a>)</span> using the LES results. Building upon this theory, we propose two new metrics for wind farm performance: turbine-scale efficiency <span class="inline-formula"><i>η</i>_TS</span>, reflecting the losses due to turbine–wake interactions, and farm-scale efficiency <span class="inline-formula"><i>η</i>_FS</span>, indicating the losses due to farm–atmosphere interactions. The LES results show that <span class="inline-formula"><i>η</i>_TS</span> is insensitive to the atmospheric condition, whereas <span class="inline-formula"><i>η</i>_FS</span> is insensitive to the turbine layout. Finally, we show that a recently developed analytical wind farm model predicts <span class="inline-formula"><i>η</i>_FS</span> with an average error of 5.7 % from the LES results.</p>

Marwan A. Ali, Omer K. Ahmed

The overuse of fossil resources, including oil and coal, has accelerated the energy crisis. The use of renewable energy, especially solar energy, is seen as a viable solution to this problem because fossil fuels pollute the environment and make it difficult for plants and animals to survive. The atmospheric vortex engine (AVE) and the solar vortex engine (SVE) are innovative ways to harness solar energy by mimicking the natural vortex dynamics to generate power. Important developments include the integration of solar collectors with vortex engines and the enhancement of design parameters, such as air inlets and turbine locations, as well as reducing the height of the chimney and reducing its construction area. These changes enhance its cost-effectiveness and do not require large building areas. This article provides a comprehensive analysis of current developments in vortex wind turbine systems for power generation. The articles studied consist of theoretical and experimental analyses. Furthermore, we conducted field research using short-term experimental designs. The research suggests that significant improvements in energy efficiency are possible. However, it emphasizes the need for further research and development to transform these technologies from small-scale prototypes to large-scale commercial applications. This would facilitate renewable energy generation and reduce our dependence on dirty energy sources.

Mohamed Hariri Muhammad Hafeez, Joohari Muhammad Imran, Abd Halim Amir Rabani et al.

The increasing imperative for sustainable energy solutions has significantly amplified the demand for commercial grid-connected photovoltaic (PV) systems, particularly those integrated into rooftop installations within urbanized environments. Malaysia's Net Energy Metering (NEM) 3.0 policy, a cornerstone of the nation's renewable energy strategy, permits commercial establishments to connect up to 75% of their peak electrical demand capacity to the national grid. This strategic allowance empowers property owners to substantially offset their energy expenditures and realize considerable savings on electricity bills over extended periods. The widespread deployment of PV systems leads to complexities notably concerning the grid's power factor which may lead to thermal inefficiencies and potential failures of switching apparatus within the electrical infrastructure. This research presents a detailed design analysis and economic evaluation of a substantial 4324.75 kWp rooftop PV system by utilizing the GCPV system. The study leverages specialized PV system software (PVsyst) to conduct environmental, financial, and technical assessments specifically at the USM Engineering Campus, Penang, Malaysia. Empirical data from 2023 reveal that the USM Engineering Campus achieved an approximate saving of RM 2.2 million during the initial year following the installation of its grid-connected PV system. It is observed that the degradation in the system's power factor from an initial 0.96 to 0.83 was primarily attributed to the suboptimal operational state of the pre-existing capacitor banks. The financial analysis specifically tailored for the commercial buildings operating under the NEM 3.0 framework projects a favorable five-year return on investment (ROI). This research serves as a valuable case study for commercial building owners contemplating the adoption of green energy production and exploring significant avenues for cost reduction.

Lutfu Sua, Haibo Wang, Jun Huang

Unpredictability of renewable energy sources coupled with the complexity of those methods used for various purposes in this area calls for the development of robust methods such as DL models within the renewable energy domain. Given the nonlinear relationships among variables in renewable energy datasets, DL models are preferred over traditional machine learning (ML) models because they can effectively capture and model complex interactions between variables. This research aims to identify the factors responsible for the accuracy of DL techniques, such as sampling, stationarity, linearity, and hyperparameter optimization for different algorithms. The proposed DL framework compares various methods and alternative training/test ratios. Seven ML methods, such as Long-Short Term Memory (LSTM), Stacked LSTM, Convolutional Neural Network (CNN), CNN-LSTM, Deep Neural Network (DNN), Multilayer Perceptron (MLP), and Encoder-Decoder (ED), were evaluated on two different datasets. The first dataset contains the weather and power generation data. It encompasses two distinct datasets, hourly energy demand data and hourly weather data in Spain, while the second dataset includes power output generated by the photovoltaic panels at 12 locations. This study deploys regularization approaches, including early stopping, neuron dropping, and L2 regularization, to reduce the overfitting problem associated with DL models. The LSTM and MLP models show superior performance. Their validation data exhibit exceptionally low root mean square error values.

Can Wan, Biao Li, Xuejun Hu et al.

The rapid growth of renewable energy generation challenges the secure operation of power systems. It becomes crucial to quantify the critical security boundaries and hosting capability of renewable generation at the system operation level. This paper proposes a novel point-hyperplane geometry (PHG) method to accurately obtain the geometric expression of the operational security region of renewable energy generation for power systems. Firstly, the geometric expression of the operational security region is defined as a polytope of boundary hyperplanes in the form of inequalities satisfying the system operation constraints. Then, an orthogonal basis generation method is proposed to solve a single boundary hyperplane of the polytope based on intersecting and orthogonal geometric principles. Next, a point-hyperplane iteration algorithm is developed to progressively obtain the overall geometric polytope of the operational security region of renewable energy generation in power systems. Besides, the flexible performance trade-off can be achieved by modifying the proposed maximum tolerated angle between adjacent hyperplanes. Finally, comprehensive case studies verify the effectiveness and superiority of the PHG method.

M. S. Pshirkov, A. S. Kovankin

We present a search for spatio-temporal clusters in 16 years of Fermi-LAT very-high-energy (VHE; $E>100$~GeV) data using the DBSCAN algorithm, focusing on high Galactic latitude ($|b|>10^{\circ}$) clusters with $\geq5$ events and transient doublets (two events within $\leq 3$ days). Of 107 detected clusters, two correspond to previously unidentified VHE sources: weak BL Lacertae objects 4FGL J0039.1-2219 and 4FGL J0212.2-0219, promising targets for next-generation VHE observatories. Due to the low VHE photon background, even doublets with a duration of several days exhibited high statistical significance. While most of the 114 detected doublets originated from bright TeV emitters (e.g., Mrk 421, Mrk 501), we identified six VHE flares lacking TeVCat associations. Five of these flares correlate with sources from the Third Catalog of Fermi-LAT High-Energy Sources (3FHL), while one 'orphan' flare lacks a high-energy (HE; $E > 10$~GeV) source counterpart. Some of these flares reached extreme luminosities of $\mathcal{O}(10^{47}~\mathrm{erg~s^{-1}})$. No consistent temporal correlation emerged between HE and VHE activity: HE flares preceded, coincided with, or followed VHE emission across sources, with some showing no HE counterpart. Remarkably, 3FHL J0308.4+0408 (NGC 1218) is a Seyfert Type I galaxy, while no object of this class was known as a VHE emitter before. The 'orphan' flare without any known HE source in the vicinity may originate from NGC 5549, a low-luminosity LINER galaxy. Both sources expand the limited sample of non-blazar AGN detected at VHE energies. The fact that some weak sources with non-aligned jets and, sometimes, even without any traces of HE activity, could demonstrate very short and powerful VHE flares cannot be easily accounted in many current AGN models and calls for their further development.

Redeil N. Arreza, Alec Nowell A. Ranara, Trisha Kerstin C. Tan et al.

Significant volumes of wastewater, particularly from the textile industry, pose environmental concerns due to the presence of hazardous substances such as ortho-toluidine (OT). The photo-Fenton process can be used to break down and remove this hazardous organic compound. Previous studies on the photo-Fenton process have focused on local optimization of operating variables without considering cost factors. The photo-Fenton process is studied in this paper with UVA irradiation, Fe2+ dosage, and H2O2 concentration considered as variables. The study uses fuzzy optimization in a multi-objective framework for making decisions to determine the optimal values of OT degradation with its corresponding cumulative uncertainty error (YA), and the total operating cost (CT), both of which are essential for assessing the techno-economic feasibility of the process. The Pareto front was generated from the objective functions to establish the boundary limits for YA and CT. The results show an overall satisfaction level of 71.81% for the objective functions, indicating a partially satisficing solution for maximizing OT degradation while minimizing operating cost. The optimum conditions of the variables require 85.70 W m−3 UVA irradiation, 0.5177 mM for Fe2+ dosage, and 7.85 mM for the H2O2 concentration. These conditions yielded an OT degradation value of 83.22% and a total operating cost of 768.61 USD·m−3. Comparison with previous literature showed an OT degradation efficiency that was 16.78% lower. However, this tradeoff in the process efficiency is offset by a total operating cost that is 2.28 times cheaper, emphasizing the cost-effectiveness of the fuzzy optimized solution.

Lucia Mazzapioda, Francesco Piccolo, Alessandra Del Giudice et al.

Abstract Single lithium-ion conducting polymer electrolytes are promising candidates for next generation safer lithium batteries. In this work, Li+-conducting Nafion membranes have been synthesized by using a novel single-step procedure. The Li-Nafion membranes were characterized by means of small-wide angle X-ray scattering, infrared spectroscopy and thermal analysis, for validating the proposed lithiation method. The obtained membranes were swollen in different organic aprotic solvent mixtures and characterized in terms of ionic conductivity, electrochemical stability window, lithium stripping-deposition ability and their interface properties versus lithium metal. The membrane swollen in ethylene carbonate:propylene carbonate (EC:PC, 1:1 w/w) displays good temperature-activated ionic conductivities (σ ≈ 5.5 × 10–4 S cm−1 at 60 °C) and a more stable Li-electrolyte interface with respect to the other samples. This Li-Nafion membrane was tested in a lithium-metal cell adopting LiFePO4 as cathode material. A specific capacity of 140 mAhg−1, after 50 cycles, was achieved at 30 °C, demonstrating the feasibility of the proposed Li-Nafion membrane.

Xiaoqing Wang, Xin Du, Haiyun Wang et al.

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

Jia‐Wei Xia, Dandan Hu, Chu‐Peng Xiao et al.

Abstract Under the influence of environmental pollution and energy scarcity, integrated energy systems (IES) have received extensive attention in the field of energy supply due to their ability to consume renewable energy and enhance energy utilization. In the context of low‐carbon scheduling for IES, numerous studies calculate the system's carbon emissions based on the carbon emission coefficients of energy devices. However, IES is a multi‐energy coupling system in which a device's energy input can originate from multiple sources with varying degrees of carbon emissions, making it difficult to accurately calculate the resulting carbon emissions using fixed coefficients. Consequently, a carbon emission flow (CEF) model is constructed for the system to calculate carbon emissions. In addition to the basic input–output CEF model, the CEF model for energy storage devices is considered, and carbon emission constraints during system operation are formulated based on the CEF model. Furthermore, many studies on low‐carbon scheduling of IES overlook the uncertainties associated with load and renewable energy. Therefore, a two‐stage scheduling model consisting of day‐ahead stage and intra‐day stage is developed to achieve reliable energy supply. Finally, through experiments, the low‐carbon performance and reliability of the model are validated.

Hamid Zakernezhad, Ali Soleimani, Milad Mohabbati

This article evaluates the types of typical renewable energy technologies separately. Switching from fossil-fuel-based technology to green energies is time-consuming and requires a high investment cost. In addition, the production of energy by new technology may cause the deactivation of some power plants, which will result in unemployment and negative economic growth impacts. In this research, the policies of different countries have been investigated, and strategies for developing these resources have been proposed.

Steven P. Reinhardt

As humanity accelerates its shift to renewable energy generation, people who are not experts in renewable energy are learning about energy technologies and the energy market, which are complex. The answers to some questions will be obvious to expert practitioners but not to non-experts. One such question is Why solar and wind generation are expected to supply the bulk of future energy when they are intermittent. We learn here that once the baseline hurdles of scalability to utility scale and the underlying resources being widely available globally are satisfied, the forecasted cost of solar and wind is 2-4X lower than competing technologies, even those that are not as scalable and available. The market views intermittency as surmountable.

Marco Calautti, Damiano Duranti, Paolo Giorgini

Managing the growing data from renewable energy production plants for effective decision-making often involves leveraging Ontology-based Data Access (OBDA), a well-established approach that facilitates querying diverse data through a shared vocabulary, presented in the form of an ontology. Our work addresses one of the common problems in this context, deriving from feeding complex class hierarchies defined by such ontologies from fragmented and imbalanced (w.r.t. class labels) data sources. We introduce an innovative framework that enhances existing OBDA systems. This framework incorporates a dynamic class management approach to address hierarchical classification, leveraging machine learning. The primary objectives are to enhance system performance, extract richer insights from underrepresented data, and automate data classification beyond the typical capabilities of basic deductive reasoning at the ontological level. We experimentally validate our methodology via real-world, industrial case studies from the renewable energy sector, demonstrating the practical applicability and effectiveness of the proposed solution.

Giovanni Gino Zanvettor, Marco Casini, Antonio Vicino

Renewable energy communities are legal entities involving the association of citizens, organizations and local businesses aimed at contributing to the green energy transition and providing social, environmental and economic benefits to their members. This goal is pursued through the cooperative efforts of the community actors and by increasing the local energy self-consumption. In this paper, the optimal energy community operation in the presence of energy storage units is addressed. By exploiting the flexibility provided by the storage facilities, the main task is to minimize the community energy bill by taking advantage of incentives related to local self-consumption. Optimality conditions are derived, and an explicit optimal solution is devised. Numerical simulations are provided to assess the performance of the proposed solution.

S. Goel, Renu Sharma

Hailiang Xu, Mingkun Gao, Pingjuan Ge et al.

The modular multilevel converters (MMCs) are popularly used in high-voltage direct current (HVDC) transmission systems. However, for the direct modulation based MMC, its complex internal dynamics and the interaction with the grid impedance would induce the frequency coupling effect, which may lead to instability issues, especially in the case of weak grid. To effectively suppress the sub- and super-synchronous oscillations, this paper proposes a linear active disturbance rejection control (LADRC) based MMC control strategy. The LADRC mainly consists of the linear extended state observer (LESO) and the linear state error feedback (LSEF). And it is a potential method to enhance the system stability margin, attributing to its high anti-interference capability and good tracking performance. Thereupon, the system small-signal impedance model considering frequency coupling is established. And the effect of the introduction of the LADRC on the system stability is further investigated using the Nyquist criterion. Particularly, the influences of key control parameters on the stability are discussed in detail. Meanwhile, the impact of LADRC on the transient performance is explored through closed-loop zero poles. Finally, the correctness of the theoretical analysis and the effectiveness of the proposed control strategy are verified via electromagnetic simulations.

Lars Mohrmann, Vikas Joshi, Jim Hinton et al.

HESS J1809$-$193 is one of the unidentified very-high-energy gamma-ray sources in the H.E.S.S. Galactic Plane Survey (HGPS). It is located in a rich environment, with an energetic pulsar and associated X-ray pulsar wind nebula, several supernova remnants, and molecular clouds in the vicinity. Furthermore, HESS J1809$-$193 was recently detected at energies above 56 TeV with HAWC, which makes it a PeVatron candidate, that is, a source capable of accelerating cosmic rays up to PeV energies. We present a new analysis of the TeV gamma-ray emission of HESS J1809$-$193 with H.E.S.S., based on improved analysis techniques. We find that the emission is best described by two components with distinct morphologies and energy spectra. We complement this study with an analysis of Fermi-LAT data in the same region. Finally, taking into account further multi-wavelength data, we interpret our results both in a hadronic and leptonic framework.

Noemie Globus, Roger Blandford

Understanding the acceleration of Ultra High Energy Cosmic Rays is one of the great challenges of contemporary astrophysics. In this short review, we summarize the general observational constraints on their composition, spectrum and isotropy which indicate that nuclei heavier than single protons dominate their spectra above $\sim 5\,{\rm EeV}$, that they are strongly suppressed above energies $\sim50\,{\rm EeV}$, and that the only significant departure from isotropy is a dipole. Constraints based upon photopion and photodisintegration losses allow their ranges and luminosity density to be estimated. Three general classes of source model are discussed - magnetospheric models (including neutron stars and black holes), jet models (including Gamma Ray Bursts, Active Galactic Nuclei and Tidal Disruption Events) and Diffusive Shock Acceleration models (involving large accretion shocks around rich clusters of galaxies). The value of constructing larger and more capable arrays to measure individual masses at the highest energies and probably identifying their sources is emphasized.

Lucien Werner, Peeyush Kumar

This paper introduces a deep reinforcement learning (RL) framework for optimizing the operations of power plants pairing renewable energy with storage. The objective is to maximize revenue from energy markets while minimizing storage degradation costs and renewable curtailment. The framework handles complexities such as time coupling by storage devices, uncertainty in renewable generation and energy prices, and non-linear storage models. The study treats the problem as a hierarchical Markov Decision Process (MDP) and uses component-level simulators for storage. It utilizes RL to incorporate complex storage models, overcoming restrictions of optimization-based methods that require convex and differentiable component models. A significant aspect of this approach is ensuring policy actions respect system constraints, achieved via a novel method of projecting potentially infeasible actions onto a safe state-action set. The paper demonstrates the efficacy of this approach through extensive experiments using data from US and Indian electricity markets, comparing the learned RL policies with a baseline control policy and a retrospective optimal control policy. It validates the adaptability of the learning framework with various storage models and shows the effectiveness of RL in a complex energy optimization setting, in the context of multi-market bidding, probabilistic forecasts, and accurate storage component models.