Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology

• Published in: IEEE transactions on power systems Vol. 36; no. 4; pp. 3701 - 3714
• Main Authors: Zhao, Changfei, Wan, Can, Song, Yonghua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Confidence intervals; Cost function; Forecasting; Integer programming; Intervals; Linear programming; Machine learning; Mathematical models; Mixed integer; Mixed integer linear programming; Operating costs; Operating reserve; prediction interval; probabilistic forecasting; Probabilistic logic; Reserves; Statistical analysis; Stochastic processes; Uncertainty; Wind forecasting; Wind power; Wind power generation
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2021.3053847

Adequate reserves are urgently needed to hedge against wind power forecasting uncertainties in power systems. Traditional reserve quantification sequentially acquires statistical features of wind power and then determines reserve amounts. This paper establishes a novel integrated probabilistic forecasting and decision (IPFD) methodology to simultaneously optimize the wind power prediction intervals (PIs) and probabilistic reserve quantification. Upward and downward reserve quantities are defined to cover the wind power forecasting uncertainties within the PIs. A cost function evaluating the reserve provision payment and deficit penalty is elaborated to realize cost-benefit trade-offs of reserve decision. Nonparametric wind power PIs are constructed based on extreme learning machine, which minimizes the reserve cost function subject to eligible target coverage probability constraint. The confidence level and quantile proportions associated with wind power PIs can be jointly tuned to reduce the operational cost of reserves. Benefited from extreme learning machine, the IPFD model is reformulated as a mixed integer linear programming problem. A feasible region tightening strategy that shrinks the large constant coefficients and eliminates the redundant binary variables is proposed to accelerate model training. Numerical experiments based on actual wind power data demonstrate the remarkable cost-effective advantages of the IPFD based reserve quantification, as well as the high computational efficiency for online application.