  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.
