Day-Ahead Electricity Market State-Space Model and Its Power Production, Demand and Price Forecasting Algorithm Using H-infinity Filter

Abstract

Development of an electricity market model is very important step of forecasting power of generators and client demand. This paper proposes a day-ahead state-space power system model which is obtained by a set of partial differential equations. After simplifications, the 4th order user-friendly state-space power system model is obtained where the measurements are obtained by a set of sensors. Secondly, we proposed an H-infinity based power system states forecasting algorithm where process and measurement noise covariances are not need to know. In each iteration, the residual error between true and forecasted states are minimised lead to an accurate forecasted system states. Numerical simulation illustrates that the proposed scheme can able to forecast the system states within 1–12 seconds.