A tri-objectives scheduling model for renewable-hydrogen-based microgrid system considering hydrogen storage system and demand-side management

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 68, P. 1412 - 1422

Published: May 1, 2024

Language: Английский

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Asymmetric Nash bargaining model for operation optimization of multi-integrated energy systems considering peer-to-peer energy trading

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105791 - 105791

Published: Aug. 31, 2024

Language: Английский

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Water-conscious green hydrogen production and conversion for harnessing long-term storage in remote renewable microgrids

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 318, P. 118870 - 118870

Published: Aug. 9, 2024

Language: Английский

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Enhancing cyber security in energy hubs with electrical and thermal storage: Leveraging machine learning for demand-supply structure perspective in threat detection

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115342 - 115342

Published: Jan. 15, 2025

Language: Английский

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Multi-objective operation of renewable multi-microgrids integrated with energy storage system considering power losses and harmonics

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 117, P. 116128 - 116128

Published: March 10, 2025

Language: Английский

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Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads

Mathematics, Journal Year: 2025, Volume and Issue: 13(9), P. 1439 - 1439

Published: April 28, 2025

To effectively account for the impact of fluctuations in power generation efficiency renewable energy sources such as photovoltaics (PVs) and wind turbines (WTs), well uncertainties load demand within an integrated system (IES), this article develops IES model incorporating units PV, WT, microturbines (MTs), Electrolyzer (EL), Hydrogen Fuel Cell (HFC), along with storage components including batteries heating systems. Furthermore, a response (DR) mechanism is introduced to dynamically regulate supply–demand balance. In modeling uncertainties, utilizes historical data on loads, combined adjustability decision variables, generate large set initial scenarios through Monte Carlo (MC) sampling algorithm. These are subsequently reduced using combination K-means clustering algorithm Simultaneous Backward Reduction (SBR) technique obtain representative scenarios. further manage distributionally robust optimization (DRO) approach introduced. This method uses 1-norm ∞-norm constraints define ambiguity probability distributions, thereby restricting fluctuation range mitigating deviations results, achieving balance between robustness economic process. Finally, solved column constraint algorithm, its effectiveness validated case studies. The MC adopted article, compared Latin hypercube followed by clustering-based scenario reduction, achieves maximum reduction approximately 17.81% total cost. Additionally, results confirm that number generated increases, optimized cost decreases, 1.14%. comprehensive analysis different approaches conducted, demonstrating lie those obtained from stochastic (SO) (RO), balancing conservatism efficiency.

Language: Английский

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Sustainable and reliability based coalition forming model for smart multi-microgrid systems considering battery and water storage systems

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 102, P. 114050 - 114050

Published: Oct. 12, 2024

Language: Английский

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A Multi-Objective Bi-Level Framework to Model Distribution System Operator’s Behavior in the Wholesale and Local Transactive Markets

Energy, Journal Year: 2024, Volume and Issue: 312, P. 133695 - 133695

Published: Nov. 2, 2024

Language: Английский

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