Outline
- I. Introduction
- II. Methodology
- III. Conclusions
رئوس مطالب
- چکیده
- کلیدواژه ها
- I. مقدمه
- II. متدولوژی
- III. نتیجه گیری ها
Abstract
Price forecasting has become an important tool in the planning and operation of restructured power systems. This paper develops a new short-term electricity price forecasting scheme based on a state space model of the power market. A Gauss-Markov process is used to represent the stochastic dynamics of the electricity market. Kalman and H ∞ filters, two methods based on the state space model, are applied in order to estimate the electricity price and compare the quality of their state estimates. Our results show that performance measures for the H ∞ filter are generally superior to those for the standard Kalman filter.
Conclusions
This paper considered short term electricity price forecasting. It developed a new scheme for the electricity price forecast based on a state space model of the power market. A Gauss-Markov process is used to represent the stochastic dynamics of the electricity market system. Using the periodogram and curve fitting, the state space model of power market is developed.
The Kalman filter and H∞ filter are then used to estimate the electricity price. Performance measures indices are defined and calculated for both methods in order to evaluate the accuracy. Simulation results shows that, the H∞ filter can forecast the price more precisely than the Kalman filter in the presence of significant model uncertainty. For future work, we will evaluate the effects of nonlinearity in the system described by PSDF on the estimation error using nonlinear filters such as the extended Kalman filter and the unscented Kalman filter.