A significant amount of livestock manure is generated in Bangladesh, creating challenges for sustainable manure management. Bioenergy and organic fertilizer production from manure are expected to provide opportunities for renewable resources, including environmental benefits. Therefore, this research aimed to spatially assess the potential of manure for biogas and compost using GIS (geographic information system) symbology and hot spot analyses, based on theoretical estimations. This study identified hot spots for biogas and compost production from various types of livestock manure at the district and sub-district levels, whereas previous studies have only explored these at a national level. The estimated total biogas and compost potential was approximately 15,035.50 million m<sup>3</sup> and 67.36 million tons, respectively, from livestock manure in 2024, distinguishing it as a feasible alternative to fossil fuels for electricity generation and synthetic fertilizers for crop production. Overall, the regional pattern maps of the socio-economic potential, hot spot identification, and environmental benefits assessments of manure will provide a more localized approach to planning sustainable manure management strategies for biogas and compost production in Bangladesh.
Fuel subsidies have been a central topic of discussion for decades in the Global South, including Nigeria, often implemented to enhance energy affordability for the population. However, on 29 May 2023, the President of Nigeria announced the elimination of the fuel subsidy, resulting in an increase in energy and electricity costs exceeding 300%. This resulted in widespread protests nationwide, significantly affecting all sectors, particularly enterprises. Thus, this study examines the impact of fuel subsidy removal on micro, small, and medium enterprises (MSMEs), as well as their level of awareness and correlation with willingness to transition to renewable energy technologies (RETs), utilizing original survey data from 1461 MSMEs across Nigeria. Results indicate that the removal of fuel subsidies impacted 90% of MSMEs surveyed. Regarding the willingness to transition to RETs, 77.2% of MSMEs expressed a positive inclination, whereas 11.7% were unwilling to undertake this transition. The willingness of MSMEs to transition is influenced by several factors, including state of residence, geographical area (settlement), level of education, enterprise category, the role of the respondents, energy type utilized by the enterprise, and the level of awareness of various RETs. The study’s findings enhance understanding of the factors influencing the adoption of RETs among MSMEs in Nigeria and the potential to inform strategies for sustainable energy development. Furthermore, the identification of specific factors influencing the transition decision provides valuable insights for targeted interventions and policymaking.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Knowing the Levelized Cost of Energy (LCOE) allows for evaluating the profitability of different energy generation technologies, identifying the options with the lowest costs, and, in turn, promoting the transition to more sustainable energy sources for governments and private companies. Therefore, it is essential to analyze the competitiveness of a concentrated solar power (CSP) plant in La Joya, Arequipa, Peru, in comparison with the local electricity provider (SEAL) tariff and the LCOE target set for 2030 by the U.S. Department of Energy's Solar Energy Technologies Office (SETO). This study focuses on assessing the feasibility of five CSP plant configurations with different capacities (19.9 MWe,50 MWe, 100 MWe, 150 MWe, and 200 MWe) in Arequipa by calculating the LCOE with varying durations of thermal energy storage (TES) from 0 to 18 hours. Additionally, the LCOE of the Gemasolar plant (19.9 MWe) in Seville, Spain, is analyzed and projected in Arequipa using economies of scale. The projected LCOEs of the CSP plants are compared with SETO’s target (5 ¢/kWh) and SEAL’s tariff (20 ¢/kWh). Finally, the LCOE is broken down into its main components to identify the most significant costs. The methodology was developed in three stages: (1) collection of technical, economic, and geographical parameters of Gemasolar along with climate and radiation data from Arequipa; (2) simulations in the System Advisor Model (SAM) software to optimize CSP plant design, considering the number and arrangement of heliostats, as well as the dimensions of the tower and receiver; and (3) processing of results in Excel to calculate the LCOE for each CSP configuration and the generation of contour maps in MATLAB to compare LCOE, TES, design power, and relative percentages against SETO targets, SEAL tariffs, and the Gemasolar plant. A total of 152 simulations were conducted in SAM to optimize the design. The results show that the LCOE of the analyzed CSP plants is between 120% and 260% above the SETO target, with values ranging from 11 to 18 ¢/kWh. However, the projected CSP LCOE is between 10% and 61% lower than SEAL’s rate, with values between 12.2 and 18 ¢/kWh. The four main components account for 78.6% of the total LCOE, with thermal storage being the most significant (37.5%), followed by heliostats (21.89%), the receiver (11.54%), and the power block (8.23%). The average annual LCOE reduction for CSP technology is approximately 1.69%. In conclusion, none of the projected CSP configurations achieve the SETO target, and even with a reduction in the main components, the LCOE would remain between 86.28% and 226.28% above this target. Thermal storage is the component with the greatest cost reduction potential, potentially lowering the LCOE by 20%. Nevertheless, all the projected CSP configurations are attractive for public or private investment, as they offer electricity at a lower cost than the local SEAL provider. Although Peru has photovoltaic plants that harness solar radiation, the LCOE of the analyzed CSPs is 219.2% higher. However, CSPs offer a significant advantage in terms of capacity factors, reaching up to 65% compared to 33% for photovoltaic plants.
Details in building design and construction. Including walls, roofs
Talal H. Alsabhan, Shaima Alghannam, Hamed M. Alhoshan
et al.
Investigating the key factors that contribute to the development of a green economy is essential for governments and policymakers as they decide where to allocate their investments. However, the determinants of a green economy, particularly regarding different energy sources, remain an under-researched area, especially in the context of GCC (Gulf Cooperation Council) economies. This study aims to explore the roles of natural gas production, crude oil production, and electricity production from renewable energy sources in the transition towards green economic transformation. For our estimation, we employed panel data techniques, utilizing data from all six GCC economies covering the period from 2010 to 2023. Our results indicate that both the use of renewable energy sources and natural gas production have significantly contributed to advancing green economic transformation in these economies. In contrast, crude oil production has been found to be an irrelevant factor in explaining the transition to green growth in the GCC. The causality analysis revealed that there is a one-way causal relationship between natural gas production and green economic transformation and a two-way causal relationship between electricity generation from renewable sources and green economic transformation in GCC economies. Based on the study’s findings, we recommend that policymakers in GCC economies embrace green economic transformation by increasing the use of renewable sources and natural gas in production. Green economic transformation would help GCC economies pursue advanced, sustainable economic performance.
Abstract Energy management has enhanced sustainability, dependability, and efficiency in smart grids. Urbanisation, technology, and consumer behaviour have boosted need for innovative power use and price control systems. The paper intends to construct ML for smart grid power use and price prediction. This work used an advanced shark smell-tuned flexible support vector machine (ASS-FSVM) to forecast smart grid price and power use. Weather stations, smart meters, and market price databases document power use and pricing. The quality and consistency of data are enhanced via the processes of cleaning and normalizing inputs. PCA reduces dimensionality by extracting pre-processed data characteristics. Optimized and tested FSVM models can anticipate smart grid power use and pricing. ASS may identify the most important dataset properties. The research evaluates electricity consumption forecasting using accuracy (98.05%), recall (98.93%), precision (97.10%), and F1-score (98.04%), and electricity price predicting using MAPE (4.32%), RMSE (5.80%), MSE (8.50%), and MAE (2.95%). The recommended strategy greatly increases forecast accuracy, helping utilities improve grid stability, demand responsiveness, and customer pricing.
Compared to traditional resources, user-side resources are of various types and have more significant uncertainty about their regulatory capacity, leading to difficulties in coordinating decisions about their simultaneous participation in the electric energy and peaking ancillary services markets. This paper proposes a joint bidding decision-making method for the day-ahead electricity energy and peak shaving auxiliary service market based on distributed robust opportunity constraints, which addresses the problem of difficulty in using an accurate probability density distribution to represent the uncertainty process of user-side resources. Firstly, a data-driven method for characterizing the uncertainty of load regulation capacity is investigated, and fuzzy sets are constructed without assuming specific probability distributions of random variables. Then, to minimize the risk expectation of the joint bidding cost on the customer side, a bidding strategy that considers the uncertainty is proposed. Finally, an example simulation verifies the reasonableness and effectiveness of the proposed joint bidding method, and the results show that the constructed model overcomes the problem of over-conservatism of the robust model, and the computational adaptability is better than that of the stochastic model, which achieves a better balance between robustness and economy.
César Aristóteles Yajure, Valesca M. Fuenzalida Sánchez
Studies on electricity demand forecasting usually focus on the magnitude of the variable, however, the methodology used in this study also addresses the time at which the peak demand occurs, crucial for planning energy generation, smoothing the demand peaks and establishing differentiated rates. To predict the time of maximum demand, supervised machine learning algorithms were used: random forests, K nearest neighbors, support vector machine, and logistic regression. The dataset consists of hourly maximum and minimum demand data from 2021 to 2024 for a country in South America, including environmental factors such as temperature and seasonality. Since the data in the peak demand prediction variable is unbalanced, the study used oversampling techniques such as SMOTE-NC (synthetic instances of the minority classes to balance the data set). A multi-criteria decision-making approach is used to select the best classification model, considering model evaluation metrics as decision criteria. The most important conclusion drawn by the study is that the model obtained with the support vector machine algorithm turned out to be optimal, and successfully predicted the time of maximum demand on 15 of the 17 test days. The findings highlight the unbalanced nature of peak demand hours, which predominantly occur around 8 pm.
Ground Penetrating Radar (GPR) image detection currently faces challenges such as low accuracy, false detections, and missed detections. To overcome these challenges, we propose a novel model referred to as GDS-YOLOv8n for detecting common underground targets in GPR images. The model incorporates the DRRB (Dilated Residual Reparam Block) feature extraction module to achieve enhanced multiscale feature extraction, with certain C2f modules in the YOLOv8n architecture being effectively replaced. In addition, the space-to-depth Conv downsampling module is used to replace the Conv modules corresponding to feature maps with a resolution of 320×320 pixels and less. This replacement assists in mitigating information loss during the downsampling of GPR images, particularly for images with limited resolution and small targets. Furthermore, the detection performance is enhanced using an auxiliary training module, ensuring performance improvement without increasing inference complexity. The introduction of the Inner-SIoU loss function refines bounding box predictions by imposing new constraints tailored to GPR image characteristics. Experimental results on real-world GPR datasets demonstrate the effectiveness of the GDS-YOLOv8n model. For six classes of common underground targets, including metal pipes, PVC pipes, and cables, the model achieves a precision of 97.1%, recall of 96.2%, and mean average precision at 50% IoU (mAP50) of 96.9%. These results indicate improvements of 4.0%, 6.1%, and 4.1%, respectively, compared to corresponding values of the YOLOv8n model, with notable improvements observed when detecting PVC pipes and cables. Compared with those of models such as YOLOv5n, YOLOv7-tiny, and SSD (Single Shot multibox Detector), our model’s mAP50 is improved by 7.20%, 5.70%, and 14.48%, respectively. Finally, the application of our model on a NVIDIA Jetson Orin NX embedded system results in an increase in the detection speed from 22 to 40.6 FPS after optimization via TensorRT and FP16 quantization, meeting the demands for the real-time detection of underground targets in mobile scenarios.
Viktor Lochot, Kaveh Khalilpour, Andrew F.A. Hoadley
et al.
The complexity of a sustainable economy is rooted in its socio-economic and environmental intricacies, particularly in formulating pathways for the harmonious integration of these parameters. This study introduces an extended input-output analysis and a multi-objective optimisation framework designed to discern trajectories for reducing CO2 emissions while simultaneously maximising GDP and employment. The economic alterations are evaluated through metrics facilitating the examination of both direct and indirect consequences stemming from perturbations within the economy. The focus of this research centres on the French economy, concentrating on pivotal sectors where reducing demand could yield the greatest reduction in CO2 emissions with minimal socio-economic ramifications. Additionally, a model is outlined for energy substitution, wherein fossil fuels in the French electricity mix are supplanted with clean energies. The ensuing effects of such a model on emission reduction pathways are scrutinised, followed by a comparison with the baseline case study.
Mahmoud M. Badr, Mohamed I. Ibrahem, Hisham A. Kholidy
et al.
In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid’s stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers’ privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers’ activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection.
Mustapha Mukhtar, Humphrey Adun, Dongsheng Cai
et al.
Abstract Recently, the International Energy Agency (IEA) released a comprehensive roadmap for the global energy sector to achieve net-zero emission by 2050. Considering the sizeable share of (Sub-Sahara) Africa in the global population, the attainment of global energy sector net-zero emission is practically impossible without a commitment from African countries. Therefore, it is important to study and analyze feasible/sustainable ways to solve the energy/electricity poverty in Africa. In this paper, the energy poverty in Africa and the high renewable energy (RE) potential are reviewed. Beyond this, the generation of electricity from the abundant RE potential in this region is analyzed in hourly timestep. This study is novel as it proposes a Sub-Sahara Africa (SSA) central grid as one of the fastest/feasible solutions to the energy poverty problem in this region. The integration of a sizeable share of electric vehicles with the proposed central grid is also analyzed. This study aims to determine the RE electricity generation capacities, economic costs, and supply strategies required to balance the projected future electricity demand in SSA. The analysis presented in this study is done considering 2030 and 2040 as the targeted years of implementation. EnergyPLAN simulation program is used to simulate/analyze the generation of electricity for the central grid. The review of the energy poverty in SSA showed that the electricity access of all the countries in this region is less than 100%. The analysis of the proposed central RE grid system is a viable and sustainable option, however, it requires strategic financial planning for its implementation. The cheapest investment cost from all the case scenarios in this study is $298 billion. Considering the use of a single RE technology, wind power systems implementation by 2030 and 2040 are the most feasible options as they have the least economic costs. Overall, the integration of the existing/fossil-fueled power systems with RE technologies for the proposed central grid will be the cheapest/easiest pathway as it requires the least economic costs. While this does not require the integration of storage systems, it will help the SSA countries reduce their electricity sector carbon emission by 56.6% and 61.8% by 2030 and 2040 respectively.
Effective short-term wind power forecast is essential for adequate power system stability, dispatching and cost control. There are various significant renewable energy sources available, including wind power, which is one of the most favorable power source. As a result, wind power is an important green form of electricity generation. Wind power prediction is a crucial topic in reducing the energy system's unpredictability. The act of balancing energy supply and demand is critical. The main aim of this research is to offer a time-series dataset-based prediction tool for estimating wind power. In addition, for visualizing the dataset obtained from the SCADA (Supervisory Control and Data Acquisition) system, dataset exploration approach is also presented. The dataset analysis approach are shown in polar coordinate systems and pair plot diagram for better understanding of the relation between wind and power production relationship. With the help of given input features i.e. theoretical power, produced active power, wind direction, wind speed, month and hour, generated turbine power is forecasted using various machine learning algorithms. The performance of the developed model has been assessed using several statistical criteria. The experimental findings reveal that the XGBoost regression approach has greater prediction accuracy than the other methods with R2 of around 0.969, MSE of 0.003, RMSE of 0.064, MAPE of 0.282, and MAE of 0.026.
Photovoltaics (PV) and Solar Thermal (ST) collectors are sometimes competitors, as investment capacity, energy demand, and roof space are limited. Therefore, a ratio that quantifies the difference in annual energy output between ST and PV for different locations is useful. A market survey assessing the average price and performance both in 2013 and 2021 was conducted, showing a factor of 3 cell price decrease combined with a 20% efficiency increase, while ST showed negligible variation. Winsun simulations were conducted, and the results were plotted on the world map. Despite variations due to local climate, the ratio of energy production (ST/PV) increases at lower latitudes mainly due to (a) higher air temperature increasing ST output but decreasing the PV output; (b) solar radiation reducing ST efficiency to zero while having a minor impact on PV efficiency. The ratio was calculated for several ST operating temperatures. For latitudes lower than 66°, the ratio of a flat plate at 50 °C to a PV module ranges from 1.85 to 4.46, while the ratio between a vacuum tube at 50 °C and a PV module ranges from 3.05 to 4.76. This ratio can support the decision between installing ST or PV while combining different factors such as energy value, system complexity, and installation cost.
Loau Al-Bahrani, Mehdi Seyedmahmoudian, Ben Horan
et al.
In Baghdad City's distribution power grid, a massive number of 630 kV distribution transformers (DTs) are used in residential neighborhoods. Each DT is joined to nine low-voltage 0.415 kV distribution feeders. Each feeder has a designated size of 1 × 240 mm<sup>2</sup> and is joined to a specified number of residential dwellings (N = 30) fixed in the initial design stage. The size and number of low-voltage 0.415 kV distribution feeders are set with no change. In this investigation, we use a new approach for modeling electricity demand in residential neighborhoods in Baghdad City and overcome this constraint by finding the optimum number of residential dwellings joined to the same low-voltage 0.415 kV distribution feeder. Two sets of the experimental equations are created to compute the number of residential dwellings that are required to be joined to the low-voltage 0.415 kV distribution feeder. The multi-gradient particle swarm optimization algorithm is used as an optimization tool to handle these experimental equations. Results show that each low-voltage 0.415 kV distribution feeder can be loaded with 50 dwellings instead of 30 due to the diversity among residential dwellings. Several facts about the load profile characteristics of residential dwellings in Iraq are established. This study's outcomes provide useful technical references for Iraq electrical design engineers to update the connection grids of low-voltage 0.415 kV distribution feeders in Baghdad City to achieve economic benefits..