Journal of Modern Power Systems and Clean Energy

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Home > Archive>Volume 9, Issue 5, 2021 >1149-1160. DOI:10.35833/MPCE.2020.000108
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Optimal Day-ahead Scheduling of Islanded Microgrid Considering Risk-based Reserve Decision
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Affiliation:

1.School of Electric Power, South China University of Technology, Guangdong Key Laboratory of Clean Energy Technology, Guangzhou, China;2.Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Dongguan, China;3.Guangzhou Power Electrical Technology Co., Ltd., Guangzhou, China

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 51777077) and the Natural Science Foundation of Guangdong Province (No. 2017A030313304).

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    Abstract:

    Due to the lack of support from the main grid, the intermittency of renewable energy sources (RESs) and the fluctuation of load will derive uncertainties to the operation of islanded microgrids (IMGs). It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs. With the high penetration of RESs, it faces both economic and environmental challenges if we only use spinning reserve for reserve support. To solve these problems, a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation, load, and storage. The operation risk due to reserve shortage is modeled by the conditional value-at-risk (CVaR) method. The correlation of input variables is considered for the forecasting error modeling of RES and load, and Latin hypercube sampling (LHS) is adopted to generate the random scenarios of the forecasting error, so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches. Furthermore, an optimal day-ahead scheduling model of joint energy and reserve considering risk-based reserve decision is established to coordinate the security and economy of the operation of IMGs. Finally, the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.

    表 5 Table 5
    表 1 Table 1
    表 2 Table 2
    表 3 Table 3
    表 4 Table 4
    图1 Typical architecture of IMGs.Fig.1
    图2 Generation-load-storage multi-type reserve scheme for IMGs.Fig.2
    图3 Mechanism of lost load and RES power curtailment.Fig.3
    图4 Correlation coefficients of power samples in each time period.Fig.4
    图5 Power of each schedulable unit in expected scenario.Fig.5
    图6 Relationship between CVaR and confidence level.Fig.6
    图7 Pareto front of different risk-aversion coefficients.Fig.7
    图 Day-ahead forecasting power of WT, PV, and load.Fig.
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History
  • Received:February 26,2020
  • Online: September 28,2021

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