4.7 Article

Robustly Coordinated Operation of an Emission-free Microgrid with Hybrid Hydrogen-battery Energy Storage

期刊

CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
卷 8, 期 2, 页码 369-379

出版社

CHINA ELECTRIC POWER RESEARCH INST
DOI: 10.17775/CSEEJPES.2021.04200

关键词

Degradation; Costs; Hydrogen; Batteries; Microgrids; Hydrogen storage; Load modeling; Column-and-constraint-generation algorithm; coordinated operation; emission-free microgrid; hybrid hydrogen-battery storage; robust optimization

资金

  1. T-RECs Energy Pte. Ltd. [04IDS000719N014]
  2. National Natural Science Foundation of China [71801054]

向作者/读者索取更多资源

This paper investigates an emission-free hybrid hydrogen-battery energy storage microgrid and proposes a coordinated operational strategy to minimize daily operation costs. Numerical simulations using Australian energy market data validate the effectiveness of the proposed strategy.
High intermittence of renewable energy resources (RESs) and restriction for greenhouse gas (GHG) emissions have significantly challenged the operations of traditional diesel generator (DG) based microgirds. This paper considers an emission-free microgid with hybrid hydrogen-battery energy storage (HHBES) and proposes a coordinated operational strategy to minimize its daily operation costs. In addition to the electricity purchase costs in the day-ahead market and the operational costs of RESs, the total degradation cost of HHBES is also included in the cost calculation. The proposed operational strategy consists of two coordinated stages. At the day-ahead stage, the schedule for the tie-line power is exchanged with the main grid, the output power of the fuel cell (FC) and the input power of the electrolysis device (ED) are optimized under the worst case of uncertain power output from RESs and power demand from electricity loads (ELs). At the intra-day stage, the battery power is determined according to the short-term prediction for the power of RESs and ELs. The problem is formulated as a robust optimization model and solved by a two-level column-and-constraint-generation (C&CG) algorithm. Numerical simulations using Australian energy market operator (AEMO) data are carried out to validate the effectiveness of the proposed strategy.

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