Optimized energy management in Grid-Connected microgrids leveraging K-means clustering algorithm and Artificial Neural network models DOI Creative Commons
Peter Anuoluwapo Gbadega, Yanxia Sun,

Olufunke Abolaji Balogun

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 336, P. 119868 - 119868

Published: May 5, 2025

Language: Английский

Return on Investment and Sustainability of HVDC Links: Role of Diagnostics, Condition Monitoring, and Material Innovations DOI Open Access
Gian Carlo Montanari, Myneni Sukesh Babu

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 3079 - 3079

Published: March 31, 2025

HVDC cable systems are becoming an upscaled technical option, compared to AC, because of various factors, including easier interconnections, lower losses, and longer transmission distances. In addition, renewables providing direct DC energy, electrified transportation, aerospace where can be favored higher carried specific power all point in the direction broad future usage HV MV links. However, contrary there is little return from on-field installation as regards long-term reliability aging processes. This gap must covered by intensive research, contributing this research purpose paper. The focus on key points for (and MVDC) sustainability, design modeling able account voltage transients extrinsic (such that caused partial discharges) impact insulation conductivity (which rules electric field distribution, thus rate). Also, recyclable nanostructured materials, well health conditions, considered. It shown how accelerated due transients, aging-time dependence conductivity, free PDs. Algorithms condition evaluations, which have additional value a relatively new technology such polymeric cables, applied system under discharges, showing they provide indication degradation globally or locally (weak spots) possible maintenance times. All effectively contribute reducing risk major breakdown damage operation, would significantly affect investment (ROI).

Language: Английский

Citations

0

Forward-thinking frequency management in islanded marine microgrid utilizing heterogeneous source of generation and nonlinear control assisted by energy storage integration DOI Creative Commons

P Odelu,

Chandan Kumar Shiva, Sachidananda Sen

et al.

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

Published: April 21, 2025

The increasing environmental challenges and global warming concerns have driven a shift towards renewable energy-based power generation, particularly in microgrids. However, marine microgrids face load-frequency regulation due to energy intermittency, unpredictable load variations, nonlinear system dynamics. Conventional control strategies often struggle with poor convergence, limited adaptability, suboptimal frequency stabilization. Addressing these requires an advanced optimization technique for robust stability dynamic environments. This study proposes Chaotic Chimp-Mountain Gazelle Optimizer (CCMGO) optimizing fractional-order proportional-integral-derivative (FOPID) controllers, enhancing multi-source microgrid. integrates wave energy, wind turbines, solar towers, photovoltaic along controlled biogas micro hydro bio-diesel engine generation. To improve grid flexibility, battery storage systems, ultra-capacitors, electric vehicles are incorporated compensation. CCMGO algorithm combines the exploration strength of mountain gazelle optimizer solution diversity enhancements from chaotic mapping chimp algorithm, preventing premature convergence improving efficiency. performance CCMGO-optimized controllers (PID, PD-PID, FOPI-FOPID, FOPID) is evaluated under various conditions, including impulse, ramp, stochastic disturbances, test robustness adaptability. Simulation results demonstrate that CCMGO-based FOPID outperform conventional strategies, achieving lower deviations, faster settling times, enhanced transient response. These findings establish CCMGO-FOPID as powerful tool microgrids, ensuring greater resilience, stability,

Language: Английский

Citations

0

Optimized energy management in Grid-Connected microgrids leveraging K-means clustering algorithm and Artificial Neural network models DOI Creative Commons
Peter Anuoluwapo Gbadega, Yanxia Sun,

Olufunke Abolaji Balogun

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 336, P. 119868 - 119868

Published: May 5, 2025

Language: Английский

Citations

0