Optimal Day-ahead Scheduling of Islanded Microgrid Considering Risk-based Reserve Decision
Optimal Day-ahead Scheduling of Islanded Microgrid Considering Risk-based Reserve Decision
复制标题
考虑基于风险的储备决策的孤岛微电网日前最优调度
DOI：
10.35833/mpce.2020.000108
复制
发表时间：
2021
影响因子：
6.3
通讯作者：
Zhou Lai
中科院分区：
文献类型：
--
作者：
Liu Zehuai;Liu Siliang;Li Qinhao;Zhang Yongjun;Deng Wenyang;Zhou Lai
由于缺乏主电网的支持，可再生能源(RESS)的间歇性和负荷的波动将给孤岛微电网(IMG)的运行带来不确定性。为IMG的经济和可靠运行分配适当的后备能力至关重要。随着RESS的高渗透率，如果只使用旋转储备作为储备支持，它将面临经济和环境方面的挑战。针对这些问题，根据发电、负荷、储能的不同运行特点，提出了一种多类型的IMG备用方案。利用条件风险值(CVaR)方法对储量短缺造成的操作风险进行了建模。考虑了输入变量之间的相关性，对电力系统和负荷的预测误差建模，采用拉丁超立方抽样(LHS)生成预测误差的随机情景，避免了传统大规模情景抽样方法带来的维度灾难。在此基础上，建立了考虑风险型备用决策的能源和备用联合日前最优调度模型，以协调IMG运行的安全性和经济性。最后，对不同方案的数值结果进行了比较，验证了所提出的方案和模型的合理性和有效性。
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.