Hasil untuk "Renewable energy sources"

B. Mathiesen, H. Lund, D. Connolly et al.

A. Hussain, S. M. Arif, Muhammad Aslam

Nadia Doytch, S. Narayan

Yonghong Kuang, Yongjun Zhang, Bin Zhou et al.

K.J. Rajimon, Rajiv Gandhi Gopalsamy

Perovskite, oxide, organic, and dye-sensitised solar cells are studied from 2015 to 2025, and their current standing and future Mott-Schottky (MS) analysis in photovoltaic (PV) research are highlighted in this review. The incorporation of MS characterisation methodology with solar cell capacitance simulator one dimension (SCAPS-1D) simulations, ab-initio calculations, impedance spectroscopy, and nascent data-driven models is addressed. The MS approach will always be at the forefront in the extraction of the flat band potential, doping concentration, depletion region width, and built-in potential. This is the link between the energetics of the semiconductors and the charge transport of the solar cells and other PV. With MS-validated doping profile optimisation, interface engineering achieves (37.66%) power conversion efficiencies, 1.52 V (open-circuit voltages) and fill factors above (87%). Unfortunately, there are limitations of the frequency-dependent capacitance, parasitic elements, trap states, and non-ideal depletion layer of some architectures, like organic and hybrid ones. The MS and simulations to be used together, and machine learning adoption and analytical models to improve the electronic characterisation, have the potential to resolve the problems. This study offers a critical evaluation of current methods and inherent constraints in MS analysis, offering a strategic framework for the systematic design of efficient, durable, and sustainable solar technologies.

Ion Santra, Kristian Stølevik Olsen

We investigate the impact of intermittent energy injections on a Brownian particle, modeled as stochastic renewals of its kinetic energy to a fixed value. Between renewals, the particle follows standard underdamped Langevin dynamics. For energy renewals occurring at a constant rate, we find non-Boltzmannian energy distributions that undergo a shape transition driven by the competition between the velocity relaxation timescale and the renewal timescale. In the limit of rapid renewals, the dynamics mimics one-dimensional run-and-tumble motion, while at finite renewal rates, the effective diffusion coefficient exhibits non-monotonic behavior. To quantify the system's departure from equilibrium, we derive a modified fluctuation-response relation and demonstrate the absence of a consistent effective temperature. The dissipation is characterized by deviations from equilibrium-like response, captured via the Harada-Sasa relation. Finally, we extend the analysis to non-Poissonian renewal processes and introduce a dimensionless conversion coefficient that quantifies the thermodynamic cost of diffusion.

Peter Skands

The theory talks at Moriond QCD and High-Energy Interactions 2025 covered the full range of scales from BSM, top, Higgs, EW, and hard QCD physics, through resummation, factorisation, and PDFs, to hadronic, heavy-ion, nonperturbative, and lattice QCD. A few talks also touched on methodologies. We here summarise main points of most of these contributions.

Anubhav Kumar Pandey, Chaima Mansour, Nandini K. K. et al.

Abstract This paper reports the optimum operation of a virtual power plant (VPP) to reduce the reliance on coal and gas-based energy generation. This practice not only promotes the incorporation of renewable-based energy sources but also reduces the reliance on fossil-based resources which are limited in the reserves. The selected VPP system comprises small hydropower, wind turbine, solar photovoltaics and fuel cells accompanied by a co-generation unit. The proposed VPP is also equipped with a storage facility applicable as a flexible storage option i.e., an electric vehicle followed by an energy storage system. In addition, the primary objective of this work is to lessen the overall cost associated with the system along with a reduction in the generated emissions. Moreover, a Golden Jackal-based Optimization inspired by nature is utilized in a single and multi-objective framework to attain the desired target objectives. Furthermore, the scheduling of the VPP system is carried out on a day-ahead basis and the outcome in terms of cost and emission is compared with the available work in the literature and the result reveals the effectiveness of the system in terms of quality solutions sets for the objectives, minimum computational time and better convergence behavior. In particular, there is a reduction in cost by 2.1% and 0.62% when compared with the salp swarm algorithm and beluga whale algorithm followed by 2.12% and 1.18% in emission. Furthermore, the cost/emission of the VPP system is also abridged by employing a Pareto-based approach which shows the suitability and effectiveness of the developed system.

F. (. Rouholahnejad, J. Gottschall

<p>Accurate wind speed determination at the height of the rotor swept area is critical for resource assessments. ERA5 data combined with short-term measurements through the “measure, correlate, predict” (MCP) method are commonly used for offshore applications in this context. However, ERA5 poses limitations in capturing site-specific wind speed variability due to its low resolution. To address this, we developed random forest models extending near-surface wind speed up to 200 m, focusing on the Dutch part of the North Sea. Based on public 2-year floating lidar data collected at four locations, the 15 % testing subset shows that the random forest model trained on the remaining 85 % of site-specific wind profiles outperforms the MCP-corrected ERA5 wind profiles in accuracy, bias, and correlation. In the absence of rotor height measurements, a model trained within a 200 km region handles vertical extension effectively, albeit with increased bias. Our regionally trained random forest model exhibits superior accuracy in capturing wind speed variations and local effects, with an average deviation below 5 % compared to corrected ERA5 with a 20 % deviation from measurements. The 10 min random-forest-predicted wind speeds capture the mesoscale section of the power spectrum where ERA5 shows degradation. For stable conditions the root mean squared error and bias are 12 % and 29 % larger, respectively, compared to unstable conditions, which can be attributed to the decoupling effect at higher heights from the surface during stable stratification. Our study highlights the potential enhancement in wind resource assessment by means of machine learning methods, specifically random forest. Future research may explore extending the random forest methodology for higher heights, benefiting a new generation of offshore wind turbines, and investigating cluster wakes in the North Sea through a multinational network of floating lidars, contingent on data availability.</p>

Balaji Panchal, Chia-Hung Su, Chun-Chong Fu et al.

Biodiesel has the potential to significantly contribute to the elimination of the current global energy and climate change challenges. However, its production and commercialization have been hindered by the diverse nature of feedstocks, and production techniques. This comparative review evaluates the production of biodiesel by electrolysis method with other methods such as (trans)esterification, supercritical transesterification, emulsion or micro-emulsion, and thermal cracking or pyrolysis, microwave-assited transesterification, and photocatalysis in terms of their environmental impact and commercial feasibility. Also, this study focuses on the availability of different biodiesel feedstocks and summarizes their characteristics affect biodiesel properties. It also outlines the criteria for selecting feedstocks for sustainable and low-cost biodiesel production. Waste cooking oil based third-generation feedstocks have been shown to be superior in comparison. Among all biodiesel production processes, electrolysis is the most suitable because it is an eco-friendly method with properties comparable to diesel. Recent research provides an update on the current challenges and opportunities for biodiesel commercialization, taking into account techno-economic and environmental considerations. The review concludes with future perspectives and suggestions regarding the selection criteria of feedstocks and production techniques to make biodiesel production cost-effective, efficient, and environmentally friendly.

Martin Kittel, Wolf-Peter Schill

Variable renewable energy droughts, so called Dunkelflaute events, emerge as a challenge for climate-neutral energy systems based on variable renewables. Here we characterize European drought events for on- and offshore wind power, solar photovoltaics, and renewable technology portfolios, using 38 historic weather years and an advanced identification method. Their characteristics heavily depend on the chosen drought threshold, questioning the usefulness of single-threshold analyses. Applying a multi-threshold framework, we quantify how the complementarity of wind and solar power temporally and spatially alleviates drought frequency, return periods, duration, and severity within (portfolio effect) and across countries (balancing effect). We identify the most extreme droughts, which drive major discharging periods of long-duration storage in a fully renewable European energy system, based on a policy-relevant decarbonization scenario. Such events comprise sequences of shorter droughts of varying severity. The most extreme event occurred in winter 1996/97 and lasted 55 days in an idealized, perfectly interconnected setting. The average renewable availability during this period was still 47% of its long-run mean. System planners must consider such events when planning for storage and other flexibility technologies. Methodologically, we conclude that using arbitrary single calendar years is not suitable for modeling weather-resilient energy scenarios.

Zigang Chen

This paper explores the integration of renewable energy sources into power systems, highlighting the resulting complexities such as variability and intermittency that challenge traditional power flow dynamics. We delve into innovative Optimal Power Flow (OPF) strategies designed to manage the unpredictability of renewable sources while ensuring economically viable and stable grid operations. A thorough review of state-of-the-art OPF algorithms, particularly those that enhance systems with substantial renewable integration, is presented. The discussion spans fundamental OPF principles, adaptations to renewable energies, and categorization of the latest advancements in areas such as energy uncertainty management, energy storage integration, linearization techniques application, and data-driven strategy utilization. Each sector's application benefits and limitations are critically analyzed. The paper concludes by pinpointing ongoing challenges and suggesting future research trajectories to foster adaptable and robust power system operations in the renewable-dominant energy era.

Peng Liu, Zhe Liu, Tingting Fu et al.

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

Saloydinov Sardorjon, Zakhidov Romen, Umarov Suhrob et al.

The development of technology to stabilize and increase the energy efficiency of hydroelectric power plants in water reservoirs using wind energy technologies represents a significant advancement in renewable energy integration. This study explores the synergistic use of wind turbines and hydroelectric systems to enhance energy production and operational stability. The methodology involved the integration of wind turbines with the existing hydroelectric power infrastructure in water reservoirs. Computational models were used to simulate the combined energy output and assess the performance under various environmental conditions. Additionally, field tests were conducted in a controlled environment to validate the computational models and determine the optimal configuration for maximum efficiency. The stabilization of energy production was also observed, reducing the variability caused by fluctuations in water flow. These results were consistent across different test sites, demonstrating the robustness of the integrated system. By leveraging wind energy to complement hydroelectric power, the proposed technology not only increases energy efficiency but also contributes to a more stable and resilient power grid. This advancement supports the global transition towards cleaner energy sources and offers a viable solution to the challenges faced by standalone renewable energy systems.

Jin Lu, Xingpeng Li

The increasing interest in hydrogen as a clean energy source has led to extensive research into its transmission, storage, and integration with bulk power systems. With the evolution of hydrogen technologies towards greater efficiency, and cost-effectiveness, it becomes essential to examine the operation and expansion of grids that include both electric power and hydrogen facilities. This paper introduces an expansion strategy for electric power and hydrogen transmission systems, tailored for future renewable energy-enriched grids. Our proposed transmission expansion planning with hydrogen facilities (TEP-H) model integrates daily operations of both electric power and hydrogen transmissions. The fuel cells and electrolyzers are used for electrical-hydrogen energy conversion, and related constraints are considered in TEP-H. We applied TEP-H to the Texas 123-bus backbone transmission grid (TX-123BT), for various renewable penetration levels and hydrogen technology development assumptions. It gave us insights on the scenarios that hydrogen transmission become feasible and economically beneficial. We also compared the performance of TX-123BT system with the hybrid transmission investment and the pure electrical transmission investment obtained by a traditional transmission expansion planning (TEP-T) model. The numerical results indicate that future renewable grids can have lower total cost with TEP-H if future electrical-hydrogen energy conversion efficiency is high.

Mahak Bhatia, Aled Williams

With increases in population, there is a noticeable change across the world in pollution levels. Recently there has been growing demand for renewable energy operated devices boomed. Numerous reasons have led to such growth including lower operating costs and reduced greenhouse gas emissions. In order to obtain the optimised output, it is required to consider all the major parameters constraining the decision-making. Multi-Criteria Decision-Making (MCDM) is one of the most reliable and effective tools for decision making with many objectives. The technique focuses to prioritise the available alternatives in the decision space by considering the influential factors (or parameters) and their relative importance in the overall decision making. Because analysis conducted using MCDM approaches utilises an algorithm to obtain the desired output, this paper focuses on the application of an MCDM approach to identify those criteria that are essential in renewable energy technology systems.

Mohammad Mohammadi, Jesse Thornburg

Exploring the convergence of electric vehicles (EVs), renewable energy, and smart grid technologies in the context of Texas, this study addresses challenges hindering the widespread adoption of EVs. Acknowledging their environmental benefits, the research focuses on grid stability concerns, uncoordinated charging patterns, and the complicated relationship between EVs and renewable energy sources. Dynamic time warping (DTW) clustering and k-means clustering methodologies categorize days based on total load and net load, offering nuanced insights into daily electricity consumption and renewable energy generation patterns. By establishing optimal charging and vehicle-to-grid (V2G) windows tailored to specific load characteristics, the study provides a sophisticated methodology for strategic decision-making in energy consumption and renewable integration. The findings contribute to the ongoing discourse on achieving a sustainable and resilient energy future through the seamless integration of EVs into smart grids.

M. M. Balashov

Over the past decade, the global energy sector has undergone major fundamental and structural changes as part of the global energy transition. The energy industry of the Russian Federation, as a key player in the global energy market and the world economy as a whole, is undergoing similar changes. In this case, in terms of ensuring high competitiveness and long-term energy security of the state, it is crucial to set priorities and build models of sustainable development for each of the industries related to the energy sector. Indeed, the process of replacing carbon-intensive energy sources with a systematic increase in the share of new, renewable energy sources (RES) should be gradual and consistent to avoid imbalances in energy systems and maintain equity for all stakeholders. In this context, the search for advanced, low-carbon energy sources is a priority for the vast majority of countries around the world. In addition, the development of renewable energy is one of the goals of Russia՚s energy strategy until 2035. At the same time, despite the obvious advantages of the Russian power industry such as the absence of dependence on budget funds, the overwhelming majority of private investment in the industry, the availability of effective mechanisms for attracting investment and the basic principle of balancing the interests of all market participants, there are also negative consequences of this approach. The nationwide task of developing the energy system and increasing the availability of electricity on the territory of the Russian Federation in terms of financing is becoming an exclusive burden on electricity consumers themselves; even insignificant risks in their operation can turn into a threat not only to sustainable development, but also to their very existence. In this context, the analysis of key directions and proposals to minimise the economic impact of the global energy transition on large energy-intensive industrial consumers of electricity and capacity is of particular relevance.

Bernardo Patella, Sonia Carbone, Luigi Roberto Oliveri et al.

Green hydrogen is a real alternative to change the current energy system. Electrochemical water splitting is considered an attractive solution to convert and store the surplus of renewable energy sources. However, hydrogen production by water electrolysis is not economically sustainable due to the use of high noble metals as catalysts (generally platinum or palladium). In order to reduce costs, in this work we have synthesized a ternary alloy of nickel, iron and sulfur and used it as the cathode in an alkaline electrolyzer to produce hydrogen from water. Furthermore, to increase the features of the proposed alloy, this material was synthesized into the pore of a polycarbonate membrane to obtain a nanostructured electrode that exposes a very high surface area to the solution and consequently a large number of electrocatalytic active sites. The electrode fabrication was carried out by potential-controlled pulsed electrochemical deposition where the potential switch from -0.45 V to -1.3 V vs. SCE for 60 cycles. The electrode was characterized by SEM and EDS showing the nanostructured nature and the composition of the electrode. Then it was tested as the cathode in an alkaline electrolyzer (30% KOH) at room temperature. Preliminary results show that the addition of sulfur to the alloy permits to increase in the electrode features compared to the binary alloy of nickel and iron.

Marwan Mahmoud, Sami Ben Slama

The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.