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The solution of power system constraints is an important issue that has to be addressed to achieve a fair operation of a competitive electricity market. In the Spanish electricity market, the System Operator (SO) is in charge of determining the technical feasibility of the generation dispatch provided by the Market Operator (MO). An optimal solution method of power system constraints in the Spanish market must take into account the connection of off-line units to solve both branch overloads and voltage constraints, the adjustment of voltage control resources, the solution of the postulated n-I and n-2 contingencies with a preventive criteria and the coupling of the solution in the 24 hourly scenarios due to the start-up cost of nonconnected units. This paper details the management of power system constraints in the Spanish market, and contains a comprehensive review of solution methods of power system constraints. In addition, the paper proposes a novel approach to solve Spanish power system constraints that overcomes the limitations of existing methods, by decoupling the solution of branch overloads and voltage constraints. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. An adaptive wavelet neural network (AWNN) is proposed in this paper for short-term price forecasting (STPF) in the electricity markets. A commonly used Mexican hat wavelet has been chosen as the activation function for hidden-layer neurons of feed-forward neural network (FFNN). To demonstrate the effectiveness of the proposed approach, day-ahead prediction of market clearing price (MCP) of Spain market, which is a duopoly market with a dominant player, and locational marginal price (LMP) forecasting in PJM electricity market, are considered. The forecasted results clearly show that AWNN has good prediction properties compared to other forecasting techniques, such as wavelet-ARIMA, multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as recently proposed fuzzy neural network (FNN). [ABSTRACT FROM AUTHOR]

In this paper, an efficient method based on a new fuzzy neural network is proposed for short-term price fore- casting of electricity markets. This fuzzy neural network has inter-layer and feed-forward architecture with a new hypercubic training mechanism. The proposed method predicts hourly market-clearing prices for the day-ahead electricity markets. By combination of fuzzy logic and an efficient learning algorithm, an appropriate model for the nonstationary behavior and outliers of the price series is presented. The proposed method is examined on the Spanish electricity market. It is shown that the method can provide more accurate results than the other price forecasting techniques, such as ARIMA time series, wavelet-ARIMA, MLP, and RBF neural networks. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic methodology for estimating the energy costs in the market for wind generators associated with wind prediction errors is proposed. Generators must buy or sell energy production deviations due to prediction errors when they bid in day-ahead or hour-ahead energy markets. The prediction error is modeled through a probability density function that represents the accuracy of the prediction model. Production hourly energy deviations and their associated trading costs are then calculated. Three study cases based on real wind power installations in Spain are analyzed. The three study cases show that the error prediction costs can reach as much as 10% of the total generator energy incomes. [ABSTRACT FROM AUTHOR]