Probabilistic day-ahead inertia forecasting

Heylen, E., Browell, J. and Teng, F. (2022) Probabilistic day-ahead inertia forecasting. IEEE Transactions on Power Systems, 37(5), pp. 3738-3746. (doi: 10.1109/TPWRS.2021.3134811)

Abstract

Power system inertia is declining and is increasingly variable and uncertain in regions where the penetration of non-synchronous generation and interconnectors is growing. This presents a challenge to power system operators who must take appropriate actions to ensure the stability and security of power systems relying on short-term forecasts of the system’s inertial response. Existing models to forecast inertia fail to quantify uncertainty, which may prevent their utilization given the risk aversion of the system operators when handling stability issues. This paper is the first to develop a model to produce calibrated, data-driven probabilistic forecasts of the inertia contribution of transmission-connected synchronous generators. The model provides a necessary tool for system operators to quantify forecast uncertainty, allowing them to manage the risk of frequency instability cost-effectively. The paper demonstrates that the assumption of a Gaussian distribution of uncertainty applied in existing models is not acceptable to accurately forecast the inertial response and provides a satisfactory forecast model by combining non-parametric density forecasting with parametric tail distributions. Moreover, the paper shows that satisfactory predictive performance can only be achieved by adopting a rolling horizon forecast approach to deal with the rapidly changing characteristics of the inertial response in power systems.

• Item Type: Articles
• Additional Information: The work of Evelyn Heylen and Fei Teng was supported from Network Innovation Allowance under Grant NIA_NGSO0020. The work of Jethro Browell was supported by an EPSRC Innovation Fellowship under Grant EP/R023484/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Browell, Dr Jethro
• Authors: Heylen, E., Browell, J., and Teng, F.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: IEEE Transactions on Power Systems
• Publisher: IEEE
• ISSN: 0885-8950
• ISSN (Online): 1558-0679
• Published Online: 12 December 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Power Systems 37(5):3738-3746
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher
• Data DOI: 10.5281/zenodo.4708136