Hasil untuk "Renewable energy sources"

H. Ibrahim, A. Ilinca, J. Perron

J. Goldemberg

A. H. Fathima, K. Palanisamy

Farshid Kamrani, Kristen Schell

The increasing integration of renewable energy sources (RESs) into modern power systems presents significant opportunities but also notable challenges, primarily due to the inherent variability of RES generation. Accurate forecasting of RES generation is crucial for maintaining the reliability, stability, and economic efficiency of power system operations. Traditional approaches, such as deterministic methods and stochastic programming, frequently depend on representative scenarios generated through clustering techniques like K-means. However, these methods may fail to fully capture the complex temporal dependencies and non-linear patterns within RES data. This paper introduces a multivariate Long Short-Term Memory (LSTM)-based network designed to forecast RESs generation using their real-world historical data. The proposed model effectively captures long-term dependencies and interactions between different RESs, utilizing historical data from both local and neighboring areas to enhance predictive accuracy. In the case study, we showed that the proposed forecasting approach results in lower CO2 emissions, and a more reliable supply of electric loads.

T. H. Malik, C. Bak

<p>Establishing a clear correlation between blade leading-edge erosion (LEE) and the performance of operational wind turbines is challenging due to the complex interaction of various factors. This study aims to improve the understanding and analysis of real wind turbine measurements by employing aeroelastic simulations to investigate the combined effects of LEE, turbulence intensity (TI), and time averaging as a data processing technique and to show how they obscure the effects of erosion. The study does not aim to investigate each contributing factor in detail but seeks to provide insights through selected examples, thereby illustrating how these conditions hinder the detection of blade erosion’s effects on power loss. An aeroelastic model provided by an offshore original-equipment manufacturer (OEM) was used to simulate various scenarios. Turbulence intensity was varied for a range of wind speeds, and the aerofoil characteristics for the blade were modified to simulate different degrees of erosion, represented by varying levels of roughness. For a given site, findings reveal that even mild simulated erosion can reduce the annual energy production (AEP) by 0.82 % at 6 % TI, while more severe erosion leads to a 1.46 % decrease. Furthermore, increasing TI exacerbates these losses, with 15 % TI causing up to 2.14 % AEP reduction for eroded blades, making it increasingly difficult to distinguish between the effects of blade erosion and turbulence intensity on turbine performance. These effects are most pronounced at sites with lower average wind speeds. Moreover, the interaction between TI levels and longer time-averaging periods, which vary with wind speed, can obscure the true magnitude of LEE's impact on short-term power fluctuations. This study suggests that 10 min time-averaging periods can mask performance and that analysing unsteady-rotor data with shorter time periods, such as 1 s periods, is preferable. The work emphasises the importance of considering the blade condition's impact in the context of various influencing factors for accurate AEP assessments, performance monitoring, and improved wind turbine design for operational wind turbines.</p>

Abinaya K, Sowmmiya U

With ever fluctuating marine loads, the need for optimised, less complex Power Management Scheme (PMS) for ferry microgrid becomes crucial. In this work, a Ro-Ro ferry based microgrid is presumed encompassing Diesel Generators (DGs), Solar PV and battery as the power sources to cater the fluctuating propulsion and constant service loads. The system has a hybrid AC-DC bus, interlinked through a bidirectional, voltage sourced, Interlinking Converter (IC) exerting uninterrupted bidirectional power transfer, load compensation and unity power factor operation through Instantaneous Power Theory (IPT). The entire system is modelled and state based PMS is framed for all possible conditions with two states, five sub states and fourteen cases so as to reduce the carbon footprint through the maximum usage of renewables. The entire system performance for different sea states (calm, moderate and turbulent) along with the response of state based PMS for different scenarios claims to be the main merit of this work. The proposed power management, notably reduces the DG usage by 40 – 60 % and achieves upto 74 % of renewable energy penetration during calm sea states, resulting in an estimated 40 % reduction in carbon footprint. A Hardware-in-the-Loop (HiL) configuration employing OPAL-RT OP4512 real-time simulator encompassing the microgrid and dSPACE Scalexio for real-time controller implementation is used to validate the state based PMS. With different mode changes, the steady state is observed to reach within 500 ms exhibiting steady power flow and good control responsiveness under a range of load and sea condition. The HiL results indicate the effective achievement of decarbonization in maritime microgrids in a scalable and effective manner.

Gregor Papa, Rok Hribar, Gašper Petelin et al.

Abstract Background Achieving climate neutrality in cities is a major challenge, especially in light of rapid urbanization and the urgent need to combat climate change. This paper explores the role of advanced computational methods in the transition of cities to climate neutrality, with a focus on energy supply and transportation systems. Central to this are recent advances in artificial intelligence, particularly machine learning, which offer enhanced capabilities for analyzing and processing large, heterogeneous urban data. By integrating these computational tools, cities can develop and optimize complex models that enable real-time, data-driven decisions. Such strategies offer the potential to significantly reduce greenhouse gas emissions, improve energy efficiency in key infrastructures and strengthen the sustainability and resilience of cities. In addition, these approaches support predictive modeling and dynamic management of urban systems, enabling cities to address the multi-faceted challenges of climate change in a scalable and proactive way. Main text The methods, which go beyond traditional data processing, use state-of-the-art technologies such as deep learning and ensemble models to tackle the complexity of environmental parameters and resource management in urban systems. For example, recurrent neural networks have been trained to predict gas consumption in Ljubljana, enabling efficient allocation of energy resources up to 60 h in advance. Similarly, traffic flow predictions were made based on historical and weather-related data, providing insights for improved urban mobility. In the context of logistics and public transportation, computational optimization techniques have demonstrated their potential to reduce congestion, emissions and operating costs, underlining their central role in creating more sustainable and efficient urban environments. Conclusions The integration of cutting-edge technologies, advanced data analytics and real-time decision-making processes represents a transformative pathway to developing sustainable, climate-resilient urban environments. These advanced computational methods enable cities to optimize resource management, improve energy efficiency and significantly reduce greenhouse gas emissions, thus actively contributing to global climate and environmental protection.

Zeshan Abbas, Hafiza Yousra Bibi, Usman Khalid et al.

Microbial fuel cells (MFCs) have potential in wastewater treatment, biogas production and clean energy generation. MFCs provide an interdisciplinary research approach incorporating engineering and natural sciences. This study explores MFCs’ capabilities to produce electricity and biogas from wastewater and field soil substrates with different compositions. A two-chamber MFC system was operated anaerobically. Household sewage water used as the organic substrate with different soil amounts. Six different process feed compositions, labeled MFC-1–6, were investigated. MFC-1 exhibited the highest biogas generation volume of 245 cm³ and 42 mW/cm² power density. MFC-5 and −6 yielded 100 cm³ and 130 cm³, respectively. Wastewater treatment was effective on day 20, with pH, conductivity, turbidity, and total dissolved solids decreased to 7.3, 2.6 mS, 326 NTUs, and 1114 mg/L, respectively. Since, MFC-1 autonomously generated −800 mV, an external battery supplied an additional 600 mV to meet the methane generation voltage requirements. MFCs’ effectiveness in addressing wastewater treatment and renewable energy production was highlighted.

Zhaobin Li, Waifan Tang, Shulun Mak et al.

Background: Leachate-induced clogging in landfill drainage systems significantly impairs operational efficiency while posing substantial environmental risks. The complex interactions among leachate components (e.g., organic matter, heavy metals, and inorganic salts), microbial communities, and inorganic precipitates lead to clogging that reduces hydraulic conductivity. Traditional control methods often fail to address these underlying processes, necessitating a deeper understanding of clogging mechanisms and effective mitigation strategies. Significance: This study provides an in-depth analysis combining a review of existing literature and experimental insights into the role of microbial communities in clogging formation and the effectiveness of aged refuse layers as a mitigation measure.To provide a comprehensive assessment, a life cycle assessment (LCA) framework is employed to analyze the environmental impacts of various clogging control methods.This study contributes to theoretical advancements by integrating a comprehensive review of LCA frameworks in the context of landfill management, addressing a gap in current literature. The integration also provides a nuanced analysis of the environmental trade-offs and their implications for sustainable landfill practices.By integrating LCA, this research offers a dual perspective that addresses both technical challenges and environmental trade-offs, contributing to more sustainable landfill management practices. Results: Laboratory experiments demonstrated that microbial activity significantly promoted calcium carbonate precipitation, leading to reduced hydraulic conductivity in landfill drainage systems. Partially saturated aged refuse layers reduced clogging potential by up to 40% by stabilizing leachate chemistry and inhibiting biofilm formation. However, life cycle assessment (LCA) results indicate that while aged refuse layers mitigate clogging, they also increase the global warming potential (GWP) by 10% compared to conventional methods, highlighting the need to balance technical efficacy with environmental sustainability. Conclusion: This study provides critical insights into microbial contributions to landfill leachate-induced clogging and emphasizes the importance of incorporating environmental considerations into landfill management. Although aged refuse layers are effective in reducing clogging, their environmental trade-offs should be carefully evaluated. Future research should explore alternative materials and configurations to optimize both clogging control and environmental performance, promoting more sustainable landfill drainage management strategies.

Biantoro Agung W., Majid R.B. Abdul, Vidayanti D. et al.

This research presents the design and development of a Smart Rainwater Harvesting (RWH) system integrated into a green building using hybrid energy sources—solar and wind—supported by Internet of Things (IoT) technology. This study used the VDI 2221 engineering design methodology to systematically analyze, select, and develop a suitable RWH system design powered by solar cells and wind energy. The main objective of this research was to conduct a comparative analysis of various RWH tool designs to identify the most efficient and sustainable configuration to be applied in coastal and deltaic environments. This research method employed quantitative analysis and VDI 2221 analysis, a method for engineering product development developed by the Verein Deutscher Ingenieure (VDI), the German Association of Engineers. This method has become an international standard in engineering design, especially in the fields of industrial engineering and in civil systems engineering. The research results indicate that there are three RWH design options for coastal areas, namely Model 1, Model 2, and Model 3. The best choice is Model 1, which utilises sturdy, rust-resistant, and wind-resistant materials and incorporates renewable energy and IoT technology. The wind power plant is capable of producing 32.42 kWh/day of electricity, while the harvested rainwater ranges from 43 mm to 1043 mm per month. A comprehensive design concept that aligns with green building standards and is feasible to implement an innovative and sustainable rainwater harvesting system.

Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour et al.

Hydrogen produced through electrolysis with renewable power is considered key to decarbonize several hard-to-electrify sectors. This work proposes a novel approach to model the active electricity market participation of co-located renewable energy and electrolyzer plants, based on opportunity-cost bidding. While a renewable energy plant typically has zero marginal cost, selling power to the grid carries a potential opportunity-cost of not producing hydrogen when it is co-located with a hydrogen electrolyzer. We first consider only the electrolyzer, and derive its revenue of consuming electricity based on the non-convex hydrogen production curve. We then consider the available renewable energy production and form a piece-wise linear cost curve representing the opportunity cost of selling (or revenue from consuming) various levels of electricity. This cost curve can be used to model a stand-alone electrolyzer or a co-located hydrogen and renewable energy plant participating in an electricity market. Our case study analyzes the effects of market-bidding electrolyzers on electricity markets and grid operations. We compare two strategies for a co-located electrolyzer-wind plant; one based on the proposed bid curve and one with a more conventional fixed electrolyzer consumption. The results show that electrolyzers that actively participate in the electricity market lower the average cost of electricity and the amount of curtailed renewable energy in the system compared with a fixed consumption case. However, the difference in total system emissions between the two strategies is insignificant. The specific impacts vary based on electrolyzer capacity and hydrogen price, which determines the location of the co-located plant in the electricity market merit order.

M. Rafique, S. Rehman

Tamer Emre, Adnan Sözen

Abstract Energy poverty (EP), a pressing global concern, is uniquely manifested in regions like eastern Turkey due to intertwined socio-economic conditions and intricate energy consumption patterns. This study critically examines the electricity market dynamics, highlighting the direct impact on end-users, from households to entire communities facing challenges such as unauthorized consumption and waste. Our findings over 2 years period of 6 million customer invoices through 17 cities of 5 distribution companies underscore the limitations of traditional income-based measures in capturing the nuances of EP. In response, we introduce a novel metric—the power-cut index per consumer (PCPC)—spotlighting the prevalence of power interruptions due to non-payment as an actionable intervention metric. To address EP’s challenges, we present a mechanism encouraging consumers to reduce consumption, offering debt discounts as incentives. Our methodological approach, harnessing both the Monte Carlo simulation and optimization, promises flexible, actionable strategies tailored to diverse EP situations. Drawing parallels with the European Union’s energy transition efforts, this study proposes the adaptation of European frameworks to cater to Turkey’s unique landscape. By anchoring our insights in real stories of those affected by EP, we highlight the human dimension, emphasizing the urgency of stakeholder collaboration to ensure a future where energy facilitates prosperity rather than hindrance. The collective endeavors of infrastructure companies, governmental agencies, NGOs, and the public are pivotal in sculpting a brighter, equitable energy future.

Fatemeh Kalantari, Jian Shi, Harish S Krishnamoorthy

Solving a significant number of differential-algebraic equations is essential for conducting transient stability simulations on a complex and interconnected electrical power system. Because of the increasing size and intricacy of modern grids, traditional sequential simulation methods are becoming more computationally challenging and time-consuming to use for dynamic simulations. This research aims to examine parallelism techniques from an innovative algorithm viewpoint to the hardware level by utilizing multiple graphics processing units as the main computational tool designed to simulate the dynamic behaviour of power systems on a large scale. Our approach features a novel hybrid algorithm to improve the dynamic simulations of grids with significant renewable energy integration. The algorithm employs a Schwarz-based approach in its initial decomposition phase to isolate synchronous machines and renewable sources from the grid. This separation enhances the algorithm's ability to manage these components effectively. Subsequently, each subsystem undergoes further division into subdomains in the second stage, with separate computations carried out using the Schur-complement technique. The simulations were conducted on various test systems, with a maximum of 25,000 buses, 8,000 synchronous generators, and 256 PV farms all modelled in detail. We introduce a GPU-oriented preprocessing and vectorization parallelization method for the implementation of our two-stage algorithm. This approach provides the flexibility to tailor parallelization techniques, including nested for loops and kernel pragmas, to craft a hardware solution that enhances the performance of our dynamic domain decomposition algorithm. Simulation outcomes have verified that this approach can yield a notable 7.8x acceleration in speed when executed on an NVIDIA GeForce RTX 2070 SUPER GPU.

Giuseppe Di Sciascio

In high energy Gamma-Ray Astronomy with shower arrays the most discriminating signature of the photon-induced showers against the background of hadron-induced cosmic-ray is the content of muons in the observed events. In the electromagnetic $γ$-showers the muon production is mainly due to the photo-production of pions followed by the decay $π\toμν$. In high energy photo-production process the photon exhibits an internal structure which is very similar to that of hadrons. Indeed, photon-hadron interactions can be understood if the physical photon is viewed as a superposition of a bare photon and an accompanying small hadronic component which feels conventional hadronic interactions. Information on photo-production $γ$p and $γγ$ cross-sections are limited to $\sqrt{s}\leq$ 200 GeV from data collected at HERA. Starting from $E_{lab}\approx$100 TeV the difference between different extrapolations of the cross sections increases to more than 50\% at $E_{lab}\approx$10$^{19}$ eV, with important impact on a number of shower observables and on the selection of the photon-initiated air showers. Recently, the LHAASO experiment opened the PeV-sky to observations detecting 40 PeVatrons in a background-free regime starting from about $E_{lab}\approx$ 100 TeV. This result provides a beam of pure high energy primary photons allowing to measure for the first time the photo-production cross section even at energies not explored yet. The future air shower array SWGO in the Southern Hemisphere, where the existence of Super-Pevatrons emitting photons well above the PeV is expected, could extend the study of the hadron nature of the photons in the PeV region. In this contribution the opportunity for a measurement of the photo-production cross section with air shower arrays is presented and discussed.

Timo A. Lehtola, A. Zahedi

Abstract Solar energy and wind power supply are renewable, decentralised and intermittent electrical power supply methods that require energy storage. Integrating this renewable energy supply to the electrical power grid may reduce the demand for centralised production, making renewable energy systems more easily available to remote regions. Control systems optimise solar energy and wind power sources to supply renewable energy to the power grid. Vehicle to Grid (V2G) operations support intermittent production as battery storage. In V2G operations, electric power flows from the power grid to the battery storage and from the battery storage back to the power grid. The primary goal of this study is to improve the existing renewable energy supply to provide more reliable units in the power grid. We consider the V2G concept as an extension of the smart charging system allowing electric vehicles to be able to inject battery energy into the power grid, acting as distributed generators or energy storage systems. This review shows how parallel V2G storage and battery storage supports the power grid. Further, the review indicates that decentralised V2G battery storages will be included in future renewable energy systems.

Pan Li, Yang Hao, Yu Wu et al.

Abstract A CO2-based Enhanced Geothermal System (CO2-EGS) has dual benefits of heat extraction and CO2 storage. Mineralization storage of CO2 may reduce reservoir permeability, thereby affecting heat extraction. Solutions require further research to optimize and balance these two benefits. In this study, CO2 storage and heat extraction were simulated by alternating cyclic injection of water and supercritical CO2 into fractured granite. By analyzing the changes of ion composition in water samples and the minerals of fracture surface, the mechanisms controlling the fracture permeability with and without proppant were obtained. The results suggest that monticellite and vaterite were formed besides montmorillonite, calcite and illite after increasing the injection cycles. This promotes mineralization storage of CO2 but reduces reservoir permeability. Without proppant, the permeability decreased in three stages and the reduction rate exhibited a sharp-slow–fast–slow trend. While the use of proppant caused an increase of two orders of magnitude in permeability. Therefore, increasing the non-contact area of the main fracture and the CO2 flow velocity can avoid a large decrease in permeability, which will increase the heat extraction and mineralization storage of CO2. The findings provide solutions for the CO2 emission reduction and the efficient exploitation of hot dry rock.

Anurag Roy, Bin Ding, Maria Khalid et al.

Summary: The future of energy generation is well in tune with the critical needs of the global economy, leading to more green innovations and emissions-abatement technologies. Introducing concentrated photovoltaics (CPVs) is one of the most promising technologies owing to its high photo-conversion efficiency. Although most researchers use silicon and cadmium telluride for CPV, we investigate the potential in nascent technologies, such as perovskite solar cell (PSC). This work constitutes a preliminary investigation into a “large-area” PSC module under a Fresnel lens (FL) with a “refractive optical concentrator-silicon-on-glass” base to minimize the PV performance and scalability trade-off concerning the PSCs. The FL-PSC system measured the solar current-voltage characteristics in variable lens-to-cell distances and illuminations. The PSC module temperature was systematically studied using the COMSOL transient heat transfer mechanism. The FL-based technique for “large-area” PSC architectures is a promising technology that further facilitates the potential for commercialization.

Aleksander Lukashevich, Aleksander Bulkin, Yury Maximov

Renewable energy sources (RES) are increasingly integrated into power systems to support the United Nations' Sustainable Development Goals of decarbonization and energy security. However, their low inertia and high uncertainty pose challenges to grid stability and increase the risk of blackouts. Stochastic chance-constrained optimization, particularly data-driven methods, offers solutions but can be time-consuming, especially when handling multiple system snapshots. This paper addresses a dynamic joint chance-constrained Direct Current Optimal Power Flow (DC-OPF) problem with Automated Generation Control (AGC) to facilitate cost-effective power generation while ensuring that balance and security constraints are met. We propose an approach for a data-driven approximation that includes a priori sample reduction, maintaining solution reliability while reducing the size of the data-driven approximation. Both theoretical analysis and empirical results demonstrate the superiority of this approach in handling generation uncertainty, requiring up to twice less data while preserving solution reliability.

Balvinder Singh, Adam Slowik, Shree Krishna Bishnoi

In this article, a dual-stage proportional integral–proportional derivative with filter (PI–PDF) controller has been proposed for a hybrid two-area power system model having thermal-, hydro-, gas-, wind-, and solar-based power generating sources. Superconductor magnetic energy storage (SMES) units to cope with the transient power deviations have been incorporated in both areas. Governor dead-band (GDB) is considered in the governor model of thermal, and a generation rate constraint (GRC) is considered in the thermal and hydro turbine models to analyze the impact of system nonlinearity. The parameters of the proposed control strategy are optimally tuned by deploying a newly developed bull–lion optimization (BLO) to maintain optimal frequency and power response during system load deviations. Variations in wind speed and PV solar irradiance data have been included to examine the effectiveness of the BLO-based PI–PDF controller with system uncertainties and variability of renewable energy sources. The obtained results are validated by comparison with recently developed existing optimization techniques. The results revealed that the proposed control strategy is efficient for regulating the frequency and tie-line power of renewable integrated power systems. Further, the BLO-based PI–PDF control strategy improved the performance in terms of performance indices like settling time and peak overshoot/undershoot in wide scale.