Search Results (4,656)

This review examines the vast opportunities and key challenges in renewable waste management across the Gulf region, with a particular emphasis on Saudi Arabia. As global demand for sustainable energy intensifies, driven by technological advancements and environmental concerns, the Gulf Cooperation Council nations, notably Saudi Arabia, are beginning to acknowledge the urgency of transitioning from fossil fuel reliance to renewable waste management. This review identifies the abundant renewable resources in the region and highlights progress in policy development while emphasizing the need for comprehensive frameworks and financial incentives to drive further investment and innovation. Waste-to-energy (WTE) technologies offer a promising avenue for reducing environmental degradation and bolstering energy security. With Saudi Arabia targeting the development of 3 Gigawatts of WTE capacity by 2030 as part of national sustainability initiatives, barriers such as regulatory complexities, financial constraints, and public misconceptions persist. Ultimately, this review concludes that advancing renewable waste management in the Gulf, particularly through stronger policies, stakeholders’ collaboration, investment in WTE and an enhancement in public awareness and education, is critical for achieving sustainability goals. By harnessing these opportunities, the region can take decisive steps toward achieving sustainability, positioning Saudi Arabia as a leader in the global fight against climate change and resource depletion. Full article

Pipelines and power cables are critical infrastructures in coastal areas for transporting energy resources from offshore renewable installations to onshore grids. It is important to investigate the hydrodynamic forces on pipelines and cables and their surrounding flow fields, which are highly related to their on-bottom stability. The time-varying hydrodynamic forces coefficients and unsteady surrounding flows of a near-seabed pipeline subjected to a wave-induced oscillatory boundary layer flow are studied through numerical simulations. The Keulegan–Carpenter numbers of the oscillatory flow are up to 400, which are defined based on the maximum undisturbed near-bed orbital velocity, the pipeline diameter and the period of the oscillatory flow. The investigated Reynolds number is set to 1 × 10⁴, defined based on U_w and D. The influences of different seabed roughness ratios k_s/D (where k_s is the Nikuradse equivalent sand roughness) up to 0.1 on the hydrodynamic forces and the flow fields are considered. Both a wall-mounted pipeline with no gap ratio to the bottom wall and a pipeline with different gap ratios to the wall are investigated. The correlations between the hydrodynamic forces and the surrounding flow patterns at different time steps during one wave cylinder are analyzed by using the force partitioning method and are discussed in detail. It is found that there are influences of the increasing k_s/D on the force coefficients at large KC, while for the small KC, the inertial effect from the oscillatory flow dominates the force coefficients with small influences from different k_s/D. The FPM analysis shows that the elongated shear layers from the top of the cylinder contribute to the peak values of the drag force coefficients. Full article

The increasing integration of high-frequency power electronic converters in renewable energy-grid systems has escalated reliability concerns, necessitating FPGA-accelerated large-scale real-time electromagnetic transient (EMT) computation to prevent failures. However, most existing studies prioritize computational performance and struggle to achieve large-scale EMT computation. To enhance the computational scale, we propose a scalable hardware architecture comprising domain-specific components and data-centric processing element (PE) arrays. This architecture is further enhanced by a graph-based matrix mapping methodology and matrix-aware fixed-point quantization for hardware-efficient computation. We demonstrate our principles with FPGA implementations of large-scale multi-converter systems. The experimental results show that we set a new record of supporting 1200 switches with a computation latency of 373 ns and an accuracy of 99.83% on FPGA implementations. Compared to the state-of-the-art large-scale EMT computation on FPGAs, our design on U55C FPGA achieves an up-to 200.00× increase in the switch scale, without I/O resource limitations, and demonstrates up-to 71.70% reduction in computation error and 51.43% reduction in DSP consumption, respectively. Full article

Hydrogen is increasingly recognized as a clean energy vector and storage medium, yet its viability and strategic role in the Western Balkans remain underexplored. This study provides the first comprehensive techno-economic, environmental, and strategic evaluation of hydrogen production pathways in Albania. Results show clear trade-offs across options. The levelized cost of hydrogen (LCOH) is estimated at 8.76 €/kg H₂ for grid-connected, 7.75 €/kg H₂ for solar, and 7.66 €/kg H₂ for wind electrolysis—values above EU averages and reliant on lower electricity costs and efficiency gains. In contrast, fossil-based hydrogen via steam methane reforming (SMR) is cheaper at 3.45 €/kg H₂, rising to 4.74 €/kg H₂ with carbon capture and storage (CCS). Environmentally, Life Cycle Assessment (LCA) results show much lower Global Warming Potential (<1 kg CO₂-eq/kg H₂) for renewables compared with ~10.39 kg CO₂-eq/kg H₂ for SMR, reduced to 3.19 kg CO₂-eq/kg H₂ with CCS. However, grid electrolysis dominated by hydropower entails high water-scarcity impacts, highlighting resource trade-offs. Strategically, Albania’s growing solar and wind projects (electricity prices of 24.89–44.88 €/MWh), coupled with existing gas infrastructure and EU integration, provide strong potential. While regulatory gaps and limited expertise remain challenges, competition from solar-plus-storage, regional rivals, and dependence on external financing pose additional risks. In the near term, a transitional phase using SMR + CCS could leverage Albania’s gas assets to scale hydrogen production while renewables mature. Overall, Albania’s hydrogen future hinges on targeted investments, supportive policies, and capacity building aligned with EU Green Deal objectives, with solar-powered electrolysis offering the potential to deliver environmentally sustainable green hydrogen at costs below 5.7 €/kg H₂. Full article

In the contemporary energy landscape, the increasing demand for electricity and the inherent uncertainties associated with the integration of renewable resources have rendered the accurate and reliable forecasting of short- and long-term demand imperative. Energy demand forecasting, fundamentally a time series problem, can be inherently complex, nonlinear, and multi-scale. Therefore, interest in artificial intelligence–based methods that provide high performance for short- and long-term forecasting, rather than traditional methods, has increased in order to solve these problems. In this study, a hybrid artificial intelligence model based on LSTM, GRU, and Random Forest, utilizing a distinct mechanism to address these types of problems, is proposed. The Multi-Scale Sliding Window (MSSW) approach was utilized for the model’s input data to capture the dynamics of the time series at different scales. The optimization of windows was conducted using the Continuous Wavelet Transform (CWT) method to determine the optimal window sizes within the MSSW structure in a data-driven manner. Experimental studies on Panama’s real energy demand data from 2015 to 2020 show that the CWT-aided MSSW-hybrid model forecasts better with lower error rates (0.007 MAE, 0.009 RMSE, 1.051% MAPE) than single models and manually determined window sizes. The results of the study demonstrate the importance of hybrid structures and window optimization in energy demand forecasting. Full article

The multi-timescale optimization dispatch method for integrated energy systems proposed in this paper balances sustainability and low-carbon characteristics. It first incorporates shared energy storage resources such as electric vehicles into system dispatch, fully leveraging their spatiotemporal properties to enhance dispatch flexibility and rapid response capabilities for integrating renewable energy and enabling clean power generation. Second, an incentive-penalty mechanism enables effective interaction between the system and the green certificate–carbon joint trading market. Penalties are imposed for failing to meet renewable energy consumption targets or exceeding carbon quotas, while rewards are granted for meeting or exceeding targets. This regulates the system’s renewable energy consumption level and carbon emissions, ensuring robust low-carbon performance. Third, this strategy considers the close coordination between heating, cooling, and electricity demand response measures with the integrated energy system, smoothing load fluctuations to achieve peak shaving and valley filling. Finally, through case study simulations and analysis, the advantages of the multi-timescale dispatch strategy proposed in this paper, in terms of economic feasibility, low-carbon characteristics, and sustainability, are verified. Full article

The integration of renewable energy sources (RES) into energy systems is becoming increasingly widespread around the world, driven by various factors, the most relevant of which is the high environmental friendliness of these types of energy resources and the possibility of creating stable generation systems that are independent of the economic and geopolitical situation. The large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different methods of generation, each with its own characteristics. As a result, the issues of data collection and processing necessary for optimizing the operation of such energy systems are becoming increasingly relevant. The first stage of renewable energy integration involves building models to assess theoretical potential, allowing the feasibility of using a particular type of resource in specific geographical conditions to be determined. The second stage of assessment involves determining the technical potential, which allows the actual energy values that can be obtained by the consumer to be determined. The paper discusses a method for assessing the technical potential of solar energy using the example of a private consumer’s energy system. For this purpose, a generator circuit with load models was implemented in the SimInTech dynamic simulation environment, accepting various sets of parameters as input, which were obtained using an intelligent information search procedure and intelligent forecasting methods. This approach makes it possible to forecast the amount of incoming solar insolation in the short term, whose values are then fed into the simulation model, allowing the forecast values of the technical potential of solar energy for the energy system configuration under consideration to be determined. The implementation of such a hybrid assessment system allows not only the technical potential of RES to be determined based on historical datasets but also provides the opportunity to obtain forecast values for energy production volumes. This allows for flexible configuration of the parameters of the elements used, which makes it possible to scale the solution to the specific configuration of the energy system in use. The proposed solution can be used as one of the elements of distributed energy systems with RES, where the concept of demand distribution and management plays an important role. Its implementation is impossible without predictive models. Full article

This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article

This paper introduces a novel scheduling model that integrates weather-based productivity coefficients into multi-unit construction projects, aiming to enhance profit and reduce delays. The method is suitable especially for renewable energy, open-area projects. The authors propose a flow-shop optimization framework that considers key aspects of construction contracts, e.g., contractual penalties, downtime losses, and cash flow constraints. A proprietary Tabu Search (TS) metaheuristic algorithm variant is used to solve the resulting NP-hard problem. Numerical experiments on multiple test sets indicate that the TS algorithm consistently outperforms other methods in finding higher-profit schedules. A real-world wind farm case study further demonstrates substantial improvements, transforming an initially loss-making operation into a profitable venture. By explicitly accounting for weather disruptions within a formalized scheduling model, this work advances the understanding of reliable project planning under uncertain environmental conditions. The solution framework offers contractors an effective tool for mitigating scheduling risks and optimizing resource usage. The integration of weather data and cash flow management increases the likelihood of on-time and on-budget project delivery. Full article

The availability of properly analyzed energy resource potential data is a prerequisite in energy planning and development. However, this was sparsely applied in Ethiopia’s renewable energy turnkey project development strategies. This study focuses on developing a solar energy resource map of Tigray to accelerate the expansion of solar energy to improve electricity access through on-grid and off-grid development schemes. This study uses monthly sunshine hour data from sixteen meteorological stations, measured at a 2 m height, and average yearly solar radiation data from twenty-two satellite stations, validated by solar radiation data and measured at three sites at 10 and 30 m heights. The solar energy potential was analyzed by taking relevant atmospheric and meteorological factors to produce solar radiation components. Accordingly, the average annual solar radiation of Tigray was found to be 6.1 kWh/m²/day and 5.3 kWh/m²/day based on meteorological and satellite data, respectively. The meteorological result gave a closer estimate to Ethiopia’s ESMAP Global Solar result of 5.83 kWh/m²/day. Finally, monthly and annual average solar radiation maps of the region were developed using ArcGIS10.5. The study’s results could contribute to assisting various solar energy developers in preparing better solar energy development plans to alleviate the chronic energy poverty of the region. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

Global modern renewable energy based on geothermal, wind, solar, and marine resources has grown rapidly over the last decades despite low energy density, intermittent supply, and other qualities inferior to those of fossil fuels. What is the explanation for this growth? The main drivers of growth are assessed using economic theories and verified with statistical data. From the neo-classic viewpoint that focuses on price substitutions, the growth can be explained by the shift from energy-intensive agriculture and industry to labour-intensive services. However, the energy resources complemented rather than substituted for each other. In the evolutionary idea, investments supported by policies enabled cost-reducing technological change. Still, policies alone are insufficient to generate the growth of modern renewable energy as they are inconsistent across countries and in time. From the behavioural perspective that is preoccupied with innovative entrepreneurs, the value addition of electrification can explain the introduction of modern renewable energy in market niches, but not its fast growth. Instead of these mono-causalities, the growth of modern renewable energy is explained by technology diffusion during the pioneering, growth, and maturation phases. Possibilities that postpone the maturation are pinpointed. Full article

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system. Full article

The equilibrium between electricity demand and consumption is vital to ensure the stability of the transmission and distribution systems grid (TS & DS) and to ensure the stable operation of the electrical system. The aim of this review study is to highlight the current legislative and technical situation and the possibilities for managing peak loads, decentralization, sharing, storage, and sale of electricity generated from renewable sources in Slovakia. The European Union′s (EU) goal of achieving carbon neutrality by 2050 and a minimum of 42.5% renewable energy consumption by 2030 brings with it obligations for individual member states. These are transposed into national strategies. The current share of renewable sources in Slovakia is approximately 24% and the EU target by 2030 is probably unrealistic. Water resources are practically exhausted; other possibilities for increasing the share of renewable energy sources (RES) are in photovoltaics, wind, and thermal sources. Due to long-term geographical and historical development, electricity production in Slovakia is based on large-scale solutions. The move towards decentralization requires legislative and technical support. The review article examines the possibilities of increasing the share of RES and energy sharing in Slovakia, and examines the legislative, economic, and social barriers to their wider application. At the same time as the share of renewable sources in electricity generation increases, the article examines and presents solutions capable of ensuring the stability of electricity networks across Europe. The study formulates diversified strategies at the distribution network level and the consumer and building levels, and identifies physical (various types of electricity storage, electromobility, electricity liquidators) and virtual (electricity sharing, energy communities, virtual batteries) solutions. In conclusion, it defines the necessary changes in the legislative, technical, social, and economic areas for the most optimal improvement of the situation in the area of increasing the share of RES, supporting the decentralization of the electric power industry, and sharing electricity in Slovakia, also based on experience and good examples from abroad. Full article

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article