Creation of a distributed energy system for the production of thermal and electric energy DOI Open Access
Nassim Rustamov, Kamalbek Berkimbayev,

Zagipa Abdikulova

et al.

Eastern-European Journal of Enterprise Technologies, Journal Year: 2024, Volume and Issue: 6(8 (132)), P. 6 - 15

Published: Dec. 30, 2024

The object of the study is distributed generation (DG) system for remote areas where extending power lines challenging or impossible. demonstrates how integrating electrical and thermal energy modules based on renewable sources (RES) into a common DG bus can ensure continuous supply. This approach provides both heat electricity to consumers, independent weather conditions an advantage over traditional systems reliant variable like wind solar energy. Numerical assessments suggest that proposed improve local resource utilization by approximately 20–30 % compared single-source setups. enhanced efficiency results in more stable output, with fewer interruptions caused low speeds reduced irradiance. Economically, reducing dependence diesel generators about 15–25 translate substantial fuel cost savings. In addition, shifting production away from non-renewable may cut greenhouse gas emissions estimated 10–20 %, contributing environmental protection targets. this research received lies its solution off-grid delivery rural areas, which generally rely expensive frequently unreliable centralized infrastructure. By leveraging implementing cogenerative system, significantly reduces reliance grids enhances independence facilities. highlights practical value solution, particularly far limited access systems. suggested not only energy, but it also coincides worldwide trends toward sustainable decentralized solutions

Language: Английский

Robust economic scheduling model for virtual power plant considering electrolysis of molten carbonate and dynamic compensation mechanism DOI
Jiaxing Wen, Rong Jia,

Ge Cao

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134696 - 134696

Published: Jan. 1, 2025

Language: Английский

Citations

1
A day-ahead self-dispatch optimization framework for load-side virtual control units participating in active power regulation of power grids DOI
Mingze Zhang, Weidong Li,

Samson S. Yu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134791 - 134791

Published: Jan. 1, 2025

Language: Английский

Citations

1
Integrating solar PV systems for energy efficiency in portable cabins: A case study in Kuwait DOI
Ahmad Sedaghat, Rasool Kalbasi, Ramadas Narayanan

et al.

Solar Energy, Journal Year: 2024, Volume and Issue: 277, P. 112715 - 112715

Published: June 27, 2024

Language: Английский

Citations

6
A systematic review of nanotechnology for electric vehicles battery DOI Creative Commons
Pulkit Kumar, Harpreet Kaur Channi, Atul Babbar

et al.

International Journal of Low-Carbon Technologies, Journal Year: 2024, Volume and Issue: 19, P. 747 - 765

Published: Jan. 1, 2024

Abstract Nanotechnology has increased electric vehicle (EV) battery production, efficiency and use. is explored in this car illustration. Nanoscale materials topologies research energy density, charge time cycle life. Nanotubes, graphene metal oxides improve storage, flow charging/discharge. Solid-state lithium-air high-energy batteries are safer, more dense stable using nanoscale catalysts. improves parts. Nanostructured fluids reduce lithium dendrite, improving batteries. Nanocoating electrodes may damage extend benefits the planet. Nanomaterials allow parts to employ ordinary, safe instead of rare, harmful ones. promotes recycling, reducing waste. Change does not influence stable, cost-effective or scalable items. Business opportunities for nanotechnology-based EV need research. High-performance, robust environmentally friendly might make cars popular transportation sustainable with development. An outline nanotechnology researchexamines publication patterns, notable articles, collaborators contributions. This issue was researched extensively, indicating interest. Research focuses on anode materials, storage performance. A landscape assessment demonstrates nanotechnology’s growth future. comprehensive literature review examined nanosensors EVs. Our study provides a solid foundation understanding current state research, identifying major trends discovering breakthroughs sensors by carefully reviewing, characterizing rating important papers.

Language: Английский

Citations

4
Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets DOI Creative Commons
Zahid Ullah, Arshad Ali, Azam Nekahi

et al.

Electricity, Journal Year: 2024, Volume and Issue: 5(2), P. 370 - 384

Published: June 12, 2024

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to increasing need for flexibility, resilience, and improved systems’ integration. VPPs harness several characteristics bring together distributed resources (DERs), resulting economic gains grid reliability. Nevertheless, encounter major challenges when it comes engaging markets, mainly because there is no all-encompassing policy regulatory framework specifically designed accommodate their unique characteristics. This underscores necessity research endeavours develop more advanced methods structures long-term viability VPPs. To address this concern, study advocates implementation a multi-aspect (MAF) as systematic approach thoroughly examine each aspect (VPPs). A STEEP (social, technological, environmental, economic, political) analytical tool utilized evaluate challenges, opportunities, benefits VPP existing markets. proposed highlights factors actions that be taken tackle related VPP’ entry into suggests further support required promote fast widespread adoption implementations. For reason, favourable based on social, considerations necessary realize genuine contributions

Language: Английский

Citations

4
Two-stage distributionally robust optimal operation of rural virtual power plants considering multi correlated uncertainties DOI Creative Commons

Shenglei Wu,

Yong Wang,

Lurao Liu

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2024, Volume and Issue: 161, P. 110173 - 110173

Published: Aug. 8, 2024

Language: Английский

Citations

4
Deep learning and smart energy-based lightweight urban power load forecasting model for sustainable urban growth DOI Creative Commons
Haewon Byeon, Azzah AlGhamdi, Ismail Keshta

et al.

Frontiers in Sustainable Cities, Journal Year: 2025, Volume and Issue: 6

Published: Jan. 15, 2025

Introduction Urban power load forecasting is essential for smart grid planning but hindered by data imbalance issues. Traditional single-model approaches fail to address this effectively, while multi-model methods mitigate splitting datasets incur high costs and risk losing shared distribution characteristics. Methods A lightweight urban model (DLUPLF) proposed, enhancing LSTM networks with contrastive loss in short-term sampling, a difference compensation mechanism, feature extraction layer reduce costs. The adjusts predictions learning differences employs dynamic class-center regularization. Its performance was evaluated through parameter tuning comparative analysis. Results DLUPLF demonstrated improved accuracy imbalanced reducing computational It preserved characteristics outperformed traditional efficiency prediction accuracy. Discussion effectively addresses complexity challenges, making it promising solution forecasting. Future work will focus on real-time applications broader systems.

Language: Английский

Citations

0
Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach DOI Creative Commons
Tirunagaru V. Sarathkumar, Arup Kumar Goswami, Baseem Khan

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

Over time, the importance of virtual power plants (VPP) has markedly risen to seamlessly incorporate sporadic nature renewable energy sources into existing smart grid framework. Simultaneously, there is a growing need for advanced forecasting methods bolster grid's stability, flexibility, and dispatchability. This paper presents dual-pronged, innovative approach maximize income in day-ahead market through VPP. On one front, VPP generation units, including solar photovoltaic, wind power, combined heat employs novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating revenue's superior frontier accomplished by integrating storage Monte-Carlo optimization. The proposed method effectively synergizes concepts VPP, storage, AOLSTM yield more substantial electricity market. Notably, introduced demonstrates minimal error metrics compared conventional such as persistence, Gradient Boost, Random Forest.

Language: Английский

Citations

0
Storage economy and markets DOI
Edisson Villa‐Ávila, Paúl Arévalo, Danny Ochoa-Correa

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 187 - 209

Published: Jan. 1, 2025

Language: Английский

Citations

0
ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact DOI Creative Commons

Xi Liu,

Hui Hwang Goh, Haonan Xie

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1035 - 1035

Published: Feb. 9, 2025

With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification module defects are crucial for extending service life preserving efficiency. PV arrays, subjected to severe outside circumstances, prone exacerbated by dust accumulation, potentially leading complex compound faults. The resemblance between individual faults sometimes leads misclassification. To address this challenge, paper presents a novel hybrid deep learning model, ResGRU, which integrates residual network (ResNet) with bidirectional gated recurrent units (BiGRU) improve fault diagnostic accuracy. Additionally, Squeeze-and-Excitation (SE) is incorporated enhance relevant features while suppressing irrelevant ones, hence improving performance. further optimize inter-class separability intra-class compactness, center loss function employed as an auxiliary model’s discriminative capacity. This proposed method facilitates automated extraction from I-V curves accurate diagnosis faults, partial shading scenarios, under varying levels aiding in formulation efficient cleaning schedules. Experimental findings indicate that suggested model achieves 99.94% accuracy on pristine data 98.21% noisy data, markedly surpassing established techniques such artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, other ResNet-based approaches. Thus, offers reliable solution array diagnosis.

Language: Английский

Citations

0