Search Results (2,146)

This study identifies the optimal location for an offshore energy island to supply sustainable power to desalination plants along the Red Sea coast. As demand for clean energy in water production grows, integrating renewables into desalination systems becomes increasingly essential. A decision-making framework was developed to assess site feasibility based on renewable energy potential (solar, wind, and wave), marine traffic, site suitability, planned developments, and proximity to desalination facilities. Data was sourced from platforms such as Windguru and RETScreen, and spatial analysis was conducted using Inverse Distance Weighting (IDW) and Multi-Criteria Decision Analysis (MCDA). Results indicate that the central Red Sea region offers the most favorable conditions, combining high renewable resource availability with existing infrastructure. The estimated regional desalination energy demand of 2.1 million kW can be met using available renewable sources. Integrating these sources is expected to reduce local CO₂ emissions by up to 43.17% and global desalination-related emissions by 9.5%. Spatial constraints for offshore installations were also identified, with land-based solar energy proposed as a complementary solution. The study underscores the need for further research into wave energy potential in the Red Sea, due to limited real-time data and the absence of a dedicated wave energy atlas. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article

Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R² and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions. Full article

Microbial fuel cells (MFCs) are bio-electrochemical systems that harness microorganisms to convert organic pollutants in wastewater directly into electricity, offering a dual solution for sustainable wastewater treatment and renewable energy generation. This paper presents a holistic techno-economic and environmental feasibility assessment of large-scale MFC deployment in Dhaka’s industrial zone, Bangladesh, as a relevant case study. Here, treating 100,000 cubic meters of wastewater daily would require a capital investment of approximately USD 500 million, with a total project cost ranging between USD 307.38 million and 1.711 billion, depending on system configurations. This setup has an estimated theoretical energy recovery of 478.4 MWh/day and a realistic output of 382 MWh/day, translating to a per-unit energy cost of USD 0.2–1/kWh. MFCs show great potential for treating wastewater and addressing energy challenges. However, this paper explores remaining challenges, including high capital costs, electrode and membrane inefficiencies, and scalability issues. Full article

As renewable energy technologies advance, identifying efficient photocatalytic materials for water splitting to produce hydrogen has become an important research focus in materials science. This study presents a multi-task regression model (MTRM) designed to predict the conduction band minimum (CBM), valence band maximum (VBM), and solar-to-hydrogen efficiency (STH) of inorganic materials. Utilizing crystallographic and band gap data from over 15,000 materials in the SNUMAT database, machine-learning methods are applied to predict CBM and VBM, which are subsequently used as additional features to estimate STH. A deep neural network framework with a multi-branch, multi-task regression structure is employed to address the issue of error propagation in traditional cascading models by enabling feature sharing and joint optimization of the tasks. The calculated results show that, while traditional tree-based models perform well in single-task predictions, MTRM achieves superior performance in the multi-task setting, particularly for STH prediction, with an MSE of 0.0001 and an R² of 0.8265, significantly outperforming cascading approaches. This research provides a new approach to predicting photocatalytic material performance and demonstrates the potential of multi-task learning in materials science. Full article

Road transportation is a major contributor to greenhouse gas (GHG) emissions in Thailand. This study assesses the potential for GHG mitigation in the road transport sector from 2018 to 2030. Emission factors for various vehicle types and technologies were derived using the International Vehicle Emissions (IVE) model. Emissions were then estimated based on country-specific vehicle data. In the baseline year 2018, total emissions were estimated at 23,914.02 GgCO₂eq, primarily from pickups (24.38%), trucks (20.96%), passenger cars (19.48%), and buses (16.95%). Multiple mitigation scenarios were evaluated, including the adoption of electric vehicles (EVs), improvements in fuel efficiency, and a shift to renewable energy. Results indicate that transitioning all newly registered passenger cars (PCs) to EVs while phasing out older models could lead to a 16.42% reduction in total GHG emissions by 2030. The most effective integrated scenario, combining the expansion of electric vehicles with improvements in internal combustion engine efficiency, could achieve a 41.96% reduction, equivalent to 18,378.04 GgCO₂eq. These findings highlight the importance of clean technology deployment and fuel transition policies in meeting Thailand’s climate goals, while providing a valuable database to support strategic planning and implementation. Full article

Many environmental risks, such as global warming and depletion of natural resources, force governments to achieve economic growth and financial development without causing environmental degradation. The dependency of countries’ dependence on fossil fuels also causes energy supply security problems due to the associated risks at regional and global levels. These reasons lead countries to diversify and increase their renewable energy investments. In this context, this study focuses on the most attractive countries in terms of renewable energy investments and analyzes the relationships between renewable energy consumption (REC), carbon dioxide emissions (CO₂), economic growth (EGRO), financial development (FD), and energy dependence (EDP) using the panel regression method. This research uses data from 38 countries between 1991 and 2021 within the scope of the “Renewable Energy Attractiveness Index” (RECAI) created by Ernst & Young. As a result of the heterogeneity and cross-sectional dependency tests, the data were analyzed using the Westerlund cointegration test, the Augmented Mean Group (AMG) estimator, and the Emirmahmutoglu and Kose causality test. The findings from this study show that FD and EGRO have a positive and significant effect on REC, whereas they have a negative and significant relationship with CO₂ emissions. Findings from the causality test show that FD has an impact on both CO₂ and EGRO. In addition, within the scope of this study, a causality was determined between EDP and REC, and a mutual relationship between energy demand and CO₂ was revealed. In light of these findings, governments should increase their investments in renewable energy to ensure sustainable economic growth and energy supply security while minimizing environmental degradation. Full article

The accurate estimation of the higher heating value (HHV) of wood biomass is essential to evaluating the latter’s energy potential as a renewable energy material. This study proposes an Artificial Neural Network (ANN) model to predict the HHV by using proximate analysis parameters—moisture, volatile matter, ash, and fixed carbon. A dataset of 252 samples (177 for training and 75 for testing), sourced from the Phyllis database, which compiles the physicochemical properties of lignocellulosic biomass and related feedstocks, was used for model development. Various ANN architectures were explored, including one to three hidden layers with 1 to 20 neurons per layer. The best performance was achieved with the 4–11–11–11–1 architecture trained using the backpropagation algorithm, yielding an adjusted R² of 0.967 with low mean absolute error (MAE) and root mean squared error (RMSE) values. A graphical user interface (GUI) was developed for real-time HHV prediction across diverse wood types. Furthermore, the model’s performance was benchmarked against 26 existing empirical and statistical models, and it outperformed them in terms of accuracy and generalization. This ANN-based tool offers a robust and accessible solution for carbon utilization strategies and the development of new energy storage material. Full article

This study anticipates cooperative manure management as a process for generating bioenergy from livestock manure, thereby reducing greenhouse gas (GHG) emissions in Bangladesh. Therefore, this study’s main objective was to identify clusters for cooperative society development and optimize suitable locations for biogas plant establishment within a cooperative system. Scenarios were explored based on manure types using cluster and network analyses of geographic information systems (GIS). The study observed 13 clusters, which have the potential to produce 6045 million m³ of biogas that can be converted to 9068.64 GWh of electricity yearly. Biogas plants additionally produced 5491.04 kilotons of biofertilizer by reducing GHG emissions estimated to be 10.16 million tons of CO_2eq in 2024. This study also optimized 10, 6, and 8 optimum locations for biogas plants according to the scenarios. To implement the findings, this study recommended a coordinated action plan based on the circular economy, which helps to obtain both environmental and economic benefits for a cooperative society. These cooperatives can be implemented for renewable energy production from livestock manure at the community level for sustainable energy generation in Bangladesh. Full article

With the increasing load and renewable energy capacity in interconnected power grids, the system voltage stability faces significant challenges. Tie-line transmission power is a critical factor influencing the voltage stability margin. To address this, this paper proposes a fully data-driven global sensitivity calculation method for the tie-line power-voltage stability margin, aiming to quantify the impact of tie-line power on the voltage stability margin. The method first constructs an online estimation model of the voltage stability margin based on system measurement data under ambient excitation. To adapt to changes in system operating conditions, an online updating strategy for the parameters of the margin estimation model is further proposed, drawing on incremental learning principles. Subsequently, considering the source–load uncertainty of the system, a global sensitivity calculation method based on analysis of variance (ANOVA) is proposed, utilizing online acquired voltage stability margin and tie-line power data, to accurately quantify the impact of tie-lines on the voltage stability margin. The accuracy of the proposed method is verified through the Nordic test system and the China Electric Power Research Institute (CEPRI) standard test case; the results show that the error of the proposed method is less than 0.3%, and the computation time is within 1 s. Full article

A scheme of load frequency control (LFC) is proposed based on the deep deterministic policy gradient (DDPG) and active disturbance rejection control (ADRC) for multi-region interconnected power systems considering the renewable energy sources (RESs) and energy storage (ES). The dynamic models of multi-region interconnected power systems are analyzed, which provides a basis for the subsequent RES access. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are adopted due to their rapid response capabilities and fast charge–discharge characteristics. To stabilize the frequency fluctuation, a first-order ADRC is designed, utilizing the anti-perturbation estimation capability of the first-order ADRC to achieve effective control. In addition, the system states are estimated using a linear expansion state observer. Based on the output of the observer, the appropriate feedback control law is selected. The DDPG-ADRC parameter optimization model is constructed to adaptively adjust the control parameters of ADRC based on the target frequency deviation and power deviation. The actor and critic networks are continuously updated according to the actual system response to ensure stable system operation. Finally, the experiment demonstrated that the proposed method outperforms traditional methods across all performance indicators, particularly excelling in reducing adjustment time (45.8% decrease) and overshoot (60% reduction). Full article

The challenges of globalization and urbanization increasingly impact the Historic Urban Landscape (HUL), yet fine-grained and quantitative methods for evaluating HUL remain limited. Adopting a human-centered perspective, this study introduces a novel framework to quantitatively evaluate HUL through the lens of Historical Appearance Integrity (HAI). An evaluation system comprising four key dimensions (building materials, building colors, decorative details, and streetscape morphology) was constructed using the Analytic Hierarchy Process (AHP). An Elo rating system was subsequently applied to quantify the scores of the indicators. A prediction model was developed based on transfer learning and feature fusion to estimate the scores of the indicators. The model achieved accuracies above 93% and loss values below 0.2 for all four indicators. The framework was applied to the Inner Qinhuai Historical Character Area in Nanjing for validation. Results show that the spatial distribution of HAI in the area exhibits significant spatial heterogeneity. On a 0–100 scale, the average HAI scores were 23.17 for primary roads, 27.73 for secondary roads, and 46.93 for branch roads. This study offers a fine-grained, automated approach to evaluate HAI along urban streets and provides a quantitative reference for heritage conservation and urban renewal strategies. Full article

The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such as (1) the spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) the cost dependencies of the different components and system characteristics. When conducting such analyses at the regional or national scale, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, identifying the lack of national-scale product cost models for HPPs as the main gap and establishing a replicable methodology for hybrid plant mapping. A simplified example is shown using this methodology for a country-level analysis. Full article