Hierarchical online energy management for residential microgrids with Hybrid hydrogen–electricity Storage System

Journal Article (2024)
Author(s)

J. Wu (Aalborg University, TU Delft - Intelligent Electrical Power Grids)

S. Li (Aalborg University, TU Delft - Intelligent Electrical Power Grids)

A. Fu (TU Delft - Intelligent Electrical Power Grids)

Miloš Cvetkovic (TU Delft - Intelligent Electrical Power Grids)

Peter Palensky (TU Delft - Electrical Sustainable Energy)

Juan C. Vasquez (Aalborg University)

Josep M. Guerrero (Aalborg University, Universitat Politecnica de Catalunya, Catalan Institution for Research and Advanced Studies (ICREA))

Research Group
Intelligent Electrical Power Grids
DOI related publication
https://doi.org/10.1016/j.apenergy.2024.123020
More Info
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Publication Year
2024
Language
English
Research Group
Intelligent Electrical Power Grids
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Volume number
363
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Abstract

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.

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