Inertia constants for individual power plants

TL;DR

This paper introduces methods combining optimization and machine learning to accurately estimate inertia constants of individual power plants, enhancing transparency and decision-making in power system stability and market operations.

Contribution

It develops novel reverse-engineering techniques to determine individual inertia constants from historical data, improving system inertia forecasting and market signals.

Findings

Recovered inertia data aids understanding TSO balancing decisions.

Estimated inertia constants improve system inertia prediction.

Methods demonstrated on Great Britain's power system.

Abstract

Keeping the power system stable is becoming more challenging with the growing share of renewable energy sources of low or negligible inertia. Inertia constants for individual power plants are generally not known and are roughly estimated by considering the type of power generation technology and the nameplate capacity of plants. More accurate knowledge of inertia constants of individual power plants would give greater transparency to decisions of the transmission system operator (TSO) and to auctions for the procurement of inertia services. Additionally, a more accurate forecast or estimation of system inertia would improve the price signals to the power market well in advance of balancing actions taken by the TSO. We develop methods based on a combination of mathematical optimisation and machine learning that reverse-engineer the inertia constants of individual power plants from the historical values of their power production and from aggregate values of inertia in a power system. We demonstrate the methods for the power system of Great Britain (GB), where historical values for aggregate inertia are published by the TSO. We show that the recovered inertia data is crucial in understanding certain individual balancing decisions by the TSO. We use the reverse-engineered inertia constants to predict system inertia which gives valuable information to the power market.