-A Smart Grid has a two-way digital communication system and it encourages customers to actively participate in different types of Demand Response (DR) programs. In the Smart Grid market, both the supplier and broker agent earn profit while distributing the electrical energy. They have to balance the supply and demand during the distribution of energy. They also participate in energy trading to...

Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. However, due to its time variant behavior and non-linear and non-stationary nature, electricity...

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York: New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniqu...

A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA) and ...

The expertise of electricity load forecasting has developed over decades. Some of the best load forecasting models use this expertise to improve the load forecasting accuracy by splitting the forecasting problem into sub-problems such as for weekend/weekday and peak/off peak. This research is designed to evaluate a method based on boosting algorithms to split the data into sub-problems for pric...

In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently the occurrence of extreme movements in the spot price represents a major source of risk to retailers and the accurate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approach...

This paper proposes a novel non-parametric approach for the analysis and prediction of electricity price curves by applying the manifold learning methodology. Cluster analysis based on the embedded low-dimensional manifold of the original price data is employed to identify characteristics of the price curve shape. The proposed price curve model performs well in forecasting both short-term price...

This chapter presents some results of an econometric analysis (developed in Patrick and Wolak 2001b) of customer-level demands for electricity of large and medium-sized industrial and commercial customers purchasing electricity under half-hourly spot prices and demand charges coincident with system peaks in the England and Wales (E&W) electricity market. These estimates can be used to forecast ...

In a truly smart grid, system load would be known in advance with a high degree of confidence. Currently, this goal of “smart forecasting” is far from being realized. In the Pacific Gas and Electric (PG&E) aggregation area managed by the California Independent System Operator (ISO), the root mean squared day-ahead forecast error was about 3.8 percent of mean actual load over the period 1 April ...