Ruud Weijermars, Clement Afagwu, Yakai Tian
et al.
Concurrent approaches for estimating storage coefficients (E) of Geological Carbon Sequestration (GCS) target reservoirs are critically reviewed, and a robust procedure for estimation of such coefficients, which are time-dependent, is proposed. Our method is based on close analogy of what historically is done in hydrocarbon production and reserves estimations using recovery factors (F). Typically, F is computed by first estimating the original hydrocarbons in place (OHIP), then the cumulative production to a certain date (of the economic limit) is computed using production forecasting methods. The production forecast provides an estimated ultimate resource (EUR), and then F follows from the ratio EUR/OHIP. We propose to similarly compute the estimated ultimate storage (EUS) or cumulative injection by forward modeling, using Gaussian-based solutions of the pressure diffusivity equation, and after estimating the total storage resource (TSR), the coefficient E follows from the ratio EUS/TSR. The new method is demonstrated in a case study using representative data from the Porthos GCS Project, which repurposes the depleted P18 gas field (offshore, Dutch shelf area) for geological CO2 sequestration (GCS). The storage coefficient for the P18-6 segment of the Porthos GCS field after 20 years of injection reaches 18 %. In addition to the deterministic storage coefficient estimation, probabilistic values after 20 years of injection for E were estimated: P90-16 %, P50-36 % and P10-59 %. Separately, it is shown how a GCS project in a depleted gas field offers significant operational advantages over storage in saline aquifers. The competitive edge of depleted gas fields over saline aquifers has not been articulated before. The new methods for computing TSR, EUS and E, can handle probabilistic storage resource classification in compliance with the SPE SRMS classification framework for storage resource estimation.
Yuxi Zhao, Vicente Casares-Giner, Vicent Pla
et al.
The increasing global push for carbon reduction highlights the importance of integrating renewable energy into the supply chain of cellular networks. However, due to the stochastic nature of renewable energy generation and the uneven load distribution across base stations, the utilization rate of renewable energy remains low. To address these challenges, this paper investigates the trade-off between carbon emissions and downlink throughput in cellular networks, offering insights into optimizing both network performance and sustainability. The renewable energy state of base station batteries and the number of occupied channels are modeled as a quasi-birth-death process. We construct models for the probability of channel blocking, average successful transmission probability for users, downlink throughput, carbon emissions, and carbon efficiency based on stochastic geometry. Based on these analyses, an energy-based cell association scheme is proposed to optimize the carbon efficiency of cellular networks. The results show that, compared to the closest cell association scheme, the energy-based cell association scheme is capable of reducing the carbon emissions of the network by 13.0% and improving the carbon efficiency by 11.3%.
Bashir Mikail Usman, Satirenjit Kaur Johl, Parvez Alam Khan
Abstract This study examines the adoption of green governance systems in Nigeria's energy sector to attain carbon neutrality, following Sustainable Development Goals (SDGs) 7 and 13. The study conducted conducts a comprehensive narrative review of the literature to examine the current status of Nigeria's energy transition, emphasizing renewable sources such as solar, wind, hydro, and biofuel. It underscores stakeholder engagement for efficient collaboration and underlines the implementation of Green Governance (GG) strategies to enhance the performance of energy companies to achieve carbon neutrality. The proposed Climate Governance Framework (CG-GD) seeks to direct the industry towards a worldwide green deal. The findings highlight the essential function of green governance in transforming economic growth, innovating sustainable technologies, and fostering enduring socio-economic and environmental advantages in Nigeria. This paper examines the knowledge gap regarding Nigeria's sustainability practices, providing theoretical and practical insights for managers, regulators, policymakers, and other stakeholders.
Accurate renewable energy forecasting is essential to reduce dependence on fossil fuels and enabling grid decarbonization. However, current approaches fail to effectively integrate the rich spatial context of weather patterns with their temporal evolution. This work introduces a novel approach that treats weather maps as tokens in transformer sequences to predict renewable energy. Hourly weather maps are encoded as spatial tokens using a lightweight convolutional neural network, and then processed by a transformer to capture temporal dynamics across a 45-hour forecast horizon. Despite disadvantages in input initialization, evaluation against ENTSO-E operational forecasts shows a reduction in RMSE of about 60% and 20% for wind and solar respectively. A live dashboard showing daily forecasts is available at: https://www.sardiniaforecast.ifabfoundation.it.
The integration of renewable energy into the power grid is often hindered by its fragmented infrastructure, leading to inefficient utilization due to the variability of energy production and its reliance on weather conditions. Battery storage systems, while essential for stabilizing energy supply, face challenges like sub-optimal energy distribution, accelerating battery degradation, and reducing operational efficiency. This paper presents a novel solution to these challenges by developing a large-scale, interconnected renewable energy network that optimizes energy storage and distribution. The proposed system includes strategically placed battery storage facilities that stabilize energy production by compensating for fluctuations in renewable output. A priority charging algorithm, informed by real-time weather forecasting and load monitoring, ensures that the most suitable battery systems are charged under varying conditions. Within each storage facility, a secondary priority charging algorithm minimizes battery degradation by ranking batteries based on critical parameters such as state of health (SoH) and state of charge (SoC) and deciding which to charge. This comprehensive approach enhances the efficiency and longevity of battery storage systems, offering a more reliable and resilient renewable energy infrastructure.
Traditional power grid infrastructure presents significant barriers to renewable energy integration and perpetuates energy access inequities, with low-income communities experiencing disproportionately longer power outages. This study develops a Markov Decision Process (MDP) framework to optimize renewable energy allocation while explicitly addressing social equity concerns in electricity distribution. The model incorporates budget constraints, energy demand variability, and social vulnerability indicators across eight major U.S. cities to evaluate policy alternatives for equitable clean energy transitions. Numerical experiments compare the MDP-based approach against baseline policies including random allocation, greedy renewable expansion, and expert heuristics. Results demonstrate that equity-focused optimization can achieve 32.9% renewable energy penetration while reducing underserved low-income populations by 55% compared to conventional approaches. The expert policy achieved the highest reward, while the Monte Carlo Tree Search baseline provided competitive performance with significantly lower budget utilization, demonstrating that fair distribution of clean energy resources is achievable without sacrificing overall system performance and providing ways for integrating social equity considerations with climate goals and inclusive access to clean power infrastructure.
Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A novel decoupled mapping path is employed for joint probability distribution modeling, formulating regression tasks for both marginal distributions and the correlation structure using proper scoring rules to ensure the rationality of the modeling process. The scenario generation process is divided into two stages. Firstly, the dynamic correlation network models temporal correlations based on a dynamic covariance matrix, capturing the time-varying features of renewable energy while enhancing the interpretability of the black-box model. Secondly, the implicit quantile network models the marginal quantile function in a nonparametric, continuous manner, enabling scenario generation through marginal inverse sampling. Experimental results demonstrate that the proposed dynamic correlation quantile network outperforms state-of-the-art methods in quantifying uncertainty and capturing dynamic correlation for short-term renewable energy scenario generation.
Swaraj Pratim Sarmah. Pranjal Sarmah, Umananda Dev Goswami
We explore the effects of Bumblebee gravity on the propagation of ultra-high energy cosmic rays (UHECRs) using astrophysical sources modelled in the Unger-Farrar-Anchordoqui (UFA) framework (2015), which includes star formation rate (SFR), gamma-ray bursts (GRBs), and active galactic nuclei (AGN). We compute the density enhancement factor for various source separations distances ($d_\text{s}$s) up to 100 Mpc within the Bumblebee gravity scenario. Additionally, we calculate the CRs flux and their suppression, goodness-of-fit values obtained from comparisons with observational data from the Pierre Auger Observatory (PAO) and the Telescope Array experiment data for the flux and the Levenberg-Marquardt algorithm for suppression. The anisotropy in the CRs arrival directions is examined, with corresponding goodness-of-fit values obtained from the PAO surface detector data (SD 750 and SD 1500). Finally, we present skymaps of flux and anisotropy under different model assumptions, providing insights into the observational signatures of UHECRs in Bumblebee gravity. We show that Bumblebee gravity stands as a viable cosmological model for explaining key observational features of UHECRs, including spectrum, composition and anisotropy. Our results show that increasing the Bumblebee gravity parameter $l$ enhances the density factor $ξ$, particularly at low energies, highlighting Lorentz violation's impact on CRs' propagation. Larger $d_\text{s}$ values amplify deviations from the $Λ$CDM model, with AGN sources dominating at high energies and GRB/SFR sources at lower energies. The skymaps indicate the structured flux patterns at large $d_\text{s}$ and structured anisotropies at higher energies.
Wouter J. A. van Weerelt, Angela Fontan, Nicola Bastianello
In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid.
Muhammad Umair Danish, Kashif Ali, Kamran Siddiqui
et al.
Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector. Deep learning models are highly successful but struggle with limited historical data and become unusable when historical data are unavailable, such as in newly constructed buildings. On the other hand, physics-based models, such as EnergyPlus, simulate energy consumption without relying on historical data but require extensive building parameter specifications and considerable time to model a building. This paper introduces a Physics-Guided Memory Network (PgMN), a neural network that integrates predictions from deep learning and physics-based models to address their limitations. PgMN comprises a Parallel Projection Layers to process incomplete inputs, a Memory Unit to account for persistent biases, and a Memory Experience Module to optimally extend forecasts beyond their input range and produce output. Theoretical evaluation shows that components of PgMN are mathematically valid for performing their respective tasks. The PgMN was evaluated on short-term energy forecasting at an hourly resolution, critical for operational decision-making in smart grid and smart building systems. Experimental validation shows accuracy and applicability of PgMN in diverse scenarios such as newly constructed buildings, missing data, sparse historical data, and dynamic infrastructure changes. This paper provides a promising solution for energy consumption forecasting in dynamic building environments, enhancing model applicability in scenarios where historical data are limited or unavailable or when physics-based models are inadequate.
Renewable energy systems have emerged as a viable option to mitigate the environmental impacts of traditional fossil fuels. However, the intermittent nature of these renewables, such as solar and wind, makes it challenging to ensure a stable energy supply using only one type. Therefore, combining more than a single technology offers significant advantages in addressing the limitations associated with each individual system. Nevertheless, developing these systems requires substantial financial investments, making it crucial to identify the most suitable locations prior to installing them. In this article, the prime objective was to propose a preliminary evaluation of land suitability for constructing solar and wind hybrid facilities (PV–wind, PV–CSP, and CS–wind) in Tataouine, southern Tunisia. To this end, a GIS-based MCDA methodology was developed based on an extensive literature review and experts’ feedback while considering climate, topography, accessibility, and environmental factors. The results obtained revealed that the optimal area for a CSP–PV hybrid system is about 793 km 2 , indicating that this combination has the highest potential in terms of available resources and compatibility. On the other hand, well-suited locations for hosting CSP–wind and PV–wind systems covered areas of 412 and 333 km 2 , respectively. Such specific locations are capable of generating an annual technical potential of 316.169, 91.252, and 62.970 TWh for CSP–PV, CSP–wind, and PV–wind, respectively. Interestingly, comprising almost all of the most appropriate sites, Remada and Dhiba stand as the ideal locations for accommodating such hybrid systems. Considering this outcome, Tataouine can position itself as a model for renewable energy adoption in Tunisia. Therefore, it is imperative for policymakers, investors, and local communities to collaborate and embrace these hybrid systems to capitalize on this immense potential and pave the way for a greener and more prosperous future.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
Hayat Abdulla Yusuf, Abeer Faisal Abdulla, Fatema Aqeel Radhi
et al.
Biodiesel as a renewable and environmentally friendly fuel can be considered an alternative to fossil fuel in industries, and one of the promising approaches to developing biodiesel yield is its production in microreactors. However, the produced quantity from microreactors is limited which necessitates higher throughput microreactors to be produced, maintaining the high yield of biodiesel. Therefore, this study investigated the transesterification of waste cooking oil (WCO) with methanol in the presence of sodium hydroxide as the catalyst using a novel branched microreactor, used for higher throughput applications. Thus, a novel four-micro serpentine-based microreactor was designed and fabricated with no external tubing. Biodiesel is produced in the fabricated microreactor and the Box-Behnken Design method (BBD) in Minitab software was used to design the experiments with different operating conditions: methanol to oil molar ratio (6:1–12:1), catalyst concentration (0.5–1.5 wt%), and reaction temperature (55–65 °C) to optimize the biodiesel volume yield in the designed microreactor. The optimum biodiesel yield using GC–MS analysis was found to be 82.8 % at a methanol to oil molar ratio of 12:1, 1.5 wt% catalyst concentration, and reaction temperature of 59.4 °C while maintaining the reactants’ inlet flow rate of 20 µL/s. Production of up to 35 mL biodiesel was collected in 30 min only. In addition, the microreactor achieved up to 97 % conversion at inlet flow rates of 8.5 µL/s.
Due to their variable and intermittent nature, the integration of renewable energy sources poses control challenges related to voltage and frequency stability in isolated microgrids. This paper proposes an enhanced dynamic droop control strategy optimized in active time along with a Hybrid Energy Storage System (HESS) comprising Battery Energy Storage System (BESS), supercapacitors (SUPCA), and Superconducting Magnetic Energy Storage (SMES) to improve microgrid stability. The Dynamic Droop Gains (DDG) are continuously tuned using the rapid-converging SECANT numerical method to enhance transient response and steady-state performance, this was achieved using MATLAB/Simulink. The HESS combines the complementary characteristics of BESS, SUPCA and SMES to balance steady power supply and temporary overload capacity. Detailed simulation studies on a microgrid test system verify that the proposed control strategy significantly enhances voltage/frequency regulation, power sharing accuracy, BESS lifespan and overall stability compared to conventional droop techniques. The SUPCA further improves the transient performance and power quality by mitigating fluctuations. The research demonstrates an innovative way to harness advanced control algorithms and emerging storage technologies for next-generation resilient and sustainable microgrids.
Danyer Perez Adan, Luis Ignacio Estevez Banos, Tony Cass
et al.
In August 2023, IT experts and scientists came together for a workshop to discuss the possibilities of building a computer cluster fully on renewable energies, as a test-case at Havana University in Cuba. The discussion covered the scientific needs for a computer cluster for particle physics at the InSTEC institute at Havana University, the possibilities to use solar energy, new developments in computing technologies, and computer cluster operation as well as operational needs for computing in particle physics. This computer cluster on renewable energies at the InSTEC institute is seen as a prototype for a large-scale computer cluster on renewable energies for scientific computing in the Caribbean, hosted in Cuba. The project is called "Humboldt Highway", to remember Alexander von Humboldt's achievements in bringing cultures of the American and European continents closer together by exchange and travel. In this spirit, we propose a project that enables and intensifies the scientific exchange between research laboratories and universities in Europe and the Caribbean, in particular Cuba.
Renewable Energy Sources (RES) have been gaining popularity on a continuous basis and the current global political situation is only accelerating energy transformation in many countries. Objectives related to environmental protection and use of RES set by different countries all over the world as well as the European Union (EU) are becoming priorities. In Poland, after years of a boom in photovoltaic (PV) installations, the Renewable Energy Sources Act has been amended, resulting in a change to the billing system for electricity produced by individual prosumers. The change in the billing method, also in pursuance to the provisions of EU laws, has contributed to the inhibition of the PV installation market for fear of energy prices and investment payback time. In this paper, by using the Net Present Value (NPV) method, three mechanisms of billing of electricity from prosumer micro-installations—based on the net-metering principle and net-billing principle (using monthly and hourly prices)—have been analysed. Particular attention has also been paid to the aspects of electricity self-consumption and energy storages, which play a significant role in the economy of PV installations in the net-billing system.
Carlos Alberto Saenz Cortez, Johanna Mariel Vilela Saldarriaga
Mediante monitoreos ornitológicos realizados en las ex relaveras de Azalia y Chonta del 9 al 12 de marzo de 2021, ubicadas en el Distrito de Goyllarisquizga (Pasco), se ha determinado que la calidad ambiental es de ponderación Media. Señalándose, además, que es importante la restauración y reforestación de los componentes ecológicos de ellas ya que, según los resultados obtenidos, el 89.06% de las especies encontradas se encuentran registradas en la Lista roja de especies amenazadas de la UICN, catalogadas como Leves según la versión 3.1 de la segunda edición de las Categorías y criterios de la Lista roja de la UICN. Asimismo, el 10.92% de aves son endémicas de la zona, que significaría que solo pueden habitar ese tipo de ecosistemas, y el 3.36% de aves son catalogadas como CITES y se ubican en el Apéndice II de CITES, por lo que se puede concluir que el 89.06% de las especies son sensibles.
Almuqrin Aljawhara H., ALasali Heba Jamal, Sayyed M. I.
et al.
The present work aims to fabricate new inexpensive epoxy-based composites with a concentration described by the formula (90 − x)epoxy + 10Sb2O3 + xPbO, where x = 5, 10, 15, and 20 wt%. The impacts of the substitution of epoxy by PbO on the composite density and radiation shielding properties of the fabricated composites were studied. The density of the fabricated composites varied between 1.30 and 1.49 g·cm−3, enriching the PbO concentration. Utilizing the narrow beam transmission method, the linear attenuation coefficient (LAC) of the fabricated composites was measured using the NaI (Tl) detector as well as radioactive sources Am-241 and Cs-137. The LAC increased by 84% and 18% at gamma-ray energy of 0.059 and 0.662 MeV, when the PbO concentration raised between 5 and 20 wt%, respectively. Then the transmission rate and half-value layer of the fabricated composites were reduced by raising the PbO concentration. Therefore, the fabricated composite has good shielding properties in the low gamma-ray energy interval to be suitable for medical applications and low radioactive waste container constructions.
This study analyzes the influence of adequate electricity supply on the industrial sector in developing nations, utilizing panel data from 2000 to 2022. Contrary to original beliefs, the study examines industry output as the dependent variable, with renewable energy as the main explanatory factor. The study incorporated control variables such as CO2 emissions, government expenditure, GDP per capita, labor force participation, and gross capital formation. The investigation included panel Autoregressive Distributed Lag (ARDL) models, unit root tests, and causality tests. In emerging countries, industrial growth is positively impacted by government spending, labor force involvement, CO2 emissions, and GDP per capita. Developed countries demonstrate favorable impacts on industrial growth through gross fixed capital formation, renewable energy, and other factors, as indicated by the long-term outcomes of the ARDL method. Policymakers in developing nations may contemplate raising government spending in pertinent sectors, encouraging worker engagement, and enacting laws to decrease CO2 emissions based on these findings. Developed countries' authorities should prioritize improving gross fixed capital creation, integrating more renewable energy sources, and sustaining factors boosting industry growth.
Energy industries. Energy policy. Fuel trade, Energy conservation