Journal
APPLIED ENERGY
Volume 349, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2023.121628
Keywords
Data-driven method; Hybrid seasonal-battery energy storage; Long-term operation; Microgrid
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This paper investigates the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage and proposes a data-driven scheduling-correction framework. The method eliminates the need for long-term renewable power forecasts and has been shown to outperform existing approaches in terms of economy, power supply reliability, and renewable energy utilization.
With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization.
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