
Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 55, P. 101517 - 101517
Published: Aug. 30, 2024
Language: Английский
Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 55, P. 101517 - 101517
Published: Aug. 30, 2024
Language: Английский
Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 104, P. 114441 - 114441
Published: Nov. 15, 2024
Language: Английский
Citations
11The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(18), P. 26002 - 26035
Published: Aug. 18, 2024
This paper introduces a cutting-edge deep learning-based model aimed at enhancing the short-term performance of microgrids by simultaneously minimizing operational costs and emissions in presence distributed energy resources. The primary focus this research is to harness potential demand response programs (DRPs), which actively engage diverse range consumers mitigate uncertainties associated with renewable sources (RES). To facilitate an effective response, study presents novel incentive-based payment strategy packaged as pricing offer. approach incentivizes participate DRPs, thereby contributing overall microgrid optimization. conducts comprehensive comparative analysis evaluating under scenarios without integration DRPs. problem formulated challenging mixed-integer nonlinear programming problem, demanding robust optimization technique for resolution. In regard, multi-objective particle swarm algorithm employed efficiently address intricate problem. showcase efficacy proficiency proposed methodology, real-world smart case chosen representative example. obtained results demonstrate that offer leads significant improvements performance, emphasizing its revolutionize sustainable cost-effective management modern power systems. Key numerical our approach. study, implementation cost reduction 12.5% decrease carbon 14.3% compared baseline DR integration. Furthermore, shows notable increase RES utilization 22.7%, significantly reduces reliance on fossil fuel-based generation.
Language: Английский
Citations
9Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: 24, P. 100766 - 100766
Published: Oct. 1, 2024
Language: Английский
Citations
9Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102993 - 102993
Published: Sept. 1, 2024
Language: Английский
Citations
7Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 238, P. 111116 - 111116
Published: Oct. 5, 2024
Language: Английский
Citations
4Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 5083 - 5095
Published: Nov. 9, 2024
Language: Английский
Citations
4Applied Energy, Journal Year: 2024, Volume and Issue: 379, P. 124902 - 124902
Published: Nov. 22, 2024
Language: Английский
Citations
4International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 165, P. 110461 - 110461
Published: Jan. 21, 2025
Language: Английский
Citations
0Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 58, P. 101642 - 101642
Published: Feb. 14, 2025
Language: Английский
Citations
0Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 1051 - 1051
Published: March 25, 2025
With the increasing demand for electricity, it is causing a growing burden on power grid. In order to alleviate pressure system, series of response (DR) strategies have emerged. This paper studied DR potential and energy flexibility city-scale building clusters under pre-cooling combined with temperature reset. study firstly selected 18 types buildings, each containing three construction years as prototype represent 228,539 buildings in Shenzhen. Then several were developed, after comparative analysis, optimal strategy was obtained applied entire Shenzhen cluster, simulation analysis conducted nine administrative districts. Among them, this used AutoBPS-DR added code based Ruby language automatically generate models finally simulated consumption results by EnergyPlus. The showed that duration 0.5 h change 2 °C both reset strategy. Under strategy, small medium can achieve better results, maximum load reduction 23.89 W/m2 rate 56.82%. Guangming District best results. Finally, peak electricity amount cluster calculated be 0.007 kWh/m2 21.87%, respectively, cost saving percentage 0.081 CNY/m2 15.05%, respectively. From this, seen had shown considerable
Language: Английский
Citations
0