4.8 Article

Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid

期刊

APPLIED ENERGY
卷 328, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.120195

关键词

Renewable energy; Fuel cell -based combined heat and power; Multiple time -scale energy management; Hydrogen -based multi -energy microgrid

资金

  1. Natural Science Foundation of China
  2. Technology Research and Development Program of Zhejiang Province
  3. [52177119]
  4. [2022C01239]

向作者/读者索取更多资源

This paper proposes a multiple time-scale energy management solution for a hydrogen-based multienergy microgrid (MEMG) to supply electricity, hydrogen, and heating loads, aiming to minimize operational costs. The solution involves day-ahead energy scheduling and real-time energy dispatch using model predictive control (MPC), considering renewable energy generation and demand uncertainties. The solution is assessed through simulation experiments and outperforms the benchmark solution, reducing the mean daily operational costs by 37.08%.
With the technological advances in energy conversion and utilization of multiple forms of energy sources, hydrogen energy has attracted increasing attention. The hydrogen energy can be used to directly supply the hydrogen demand and generate both electricity and heat through fuel cell-based combined heat and power (FCCHP) unit. This paper proposed a multiple time-scale energy management solution for a hydrogen-based multienergy microgrid (MEMG) to supply electricity, hydrogen and heating loads aiming to minimize the MEMG operational cost with consideration of renewable energy generation and demand uncertainties. The proposed solution consists of day-ahead energy scheduling and model predictive control (MPC) based real-time energy dispatch in the presence of the electricity market. In the hydrogen-based MEMG, the electricity and hydrogen can be dispatched and utilized across multiple interconnected subsystems to improve the overall system energy utilization efficiency. The proposed solution is extensively assessed through simulation experiments compared with a benchmark solution. The numerical results confirm that the proposed solution outperforms the benchmark solution with the mean daily actual operational costs reduced by 37.08%.

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