Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.

This paper describes an optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices. The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy-consumption level, maximum and minimum hourly load levels, and ramping limits on such load levels. Price uncertainty is modeled through robust optimization techniques. The model materializes into a simple linear programming algorithm that can be easily integrated in the Energy Management System of a household or a small business. A simple bidirectional communication device between the power supplier and the consumer enables the implementation of the proposed model. Numerical simulations illustrating the interest of the proposed model are provided.

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.

This paper proposes a methodology to determine the required level of spinning and nonspinning reserves in a power system with a high penetration of wind power. The computation of the required reserve levels and their costs is achieved through a stochastic programming market-clearing model spanning a daily time horizon. This model considers the network constraints and takes into account the cost of both the load shedding and the wind spillage. The methodology proposed is illustrated using an example and a realistic case study. Some conclusions are finally drawn.