An Effective Spinning Reserve Allocation Method Considering Operational Reliability With Multi-Uncertainties

• Published in: IEEE transactions on power systems Vol. 39; no. 1; pp. 1 - 13
• Main Authors: Sun, Bin, Dai, Wei, Zhang, Dongdong, Goh, Hui Hwang, Zhao, Jingyi, Shi, Bochen, Wu, Thomas
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational efficiency; Computing costs; condition-dependent outage replacement rate (CDORR); Contingency management; Electric equipment; expected energy not served (EENS); Generators; Load fluctuation; Load modeling; McCormick envelope; multi-uncertainties; Power system reliability; Relaxation method (mathematics); Resource management; Risk; Spinning reserve; System reliability; Uncertainty; Wind forecasting
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2023.3262240

Spinning reserve (SR) is a crucial resource for ensuring power system reliability by lowering operational risk. The operational risk is contingent on the unpredictability of renewable energy as well as the replacement rate of electrical devices in relation to operating conditions. Previous research on SR allocation ignoring the condition-dependent outage replacement rate (CDORR) of electrical equipment may have allocated insufficient SR capacity that cannot adjust for unanticipated power imbalances. In addition, the introduction of CDORR and various multi-uncertainty scenarios into existing SR allocation models may incur a significant computational cost. This work presents an efficient SR allocation model that takes operational reliability under multi-uncertainties into account (e.g., wind output randomness, load fluctuations, generator and transmission contingencies with CDORR). Analytical expected energy not served (EENS) formulations based on the sensitivity method are derived to estimate the operational risk under multi-uncertainties, thereby obviating the need for iterations in large uncertainty scenarios. We present an improved relaxation method based on the McCormick envelope to further enhance the model's tractability. The proposed tangent plane cut collection strategy improves the computational efficiency by reducing the redundant envelope region. Results demonstrate that the proposed method benefits the economy by considering operational reliability and accelerates computational speed with reasonable accuracy.