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The level of secondary reserve needed in a power system is traditionally settled by system operators according to engineering criteria. This paper proposes a novel methodology to determine the optimum level of secondary reserve based on both engineering and economic criteria. Within the proposed approach a price elastic-quantity demand curve for the secondary reserve is built. This approach assumes that the provision of secondary reserve is made under a competitive market. In this market, the supply curve that collects the bids from generators providing secondary reserve is matched with the calculated elastic demand cost curve. The cross of the supply and demand curves determines the optimal level of secondary reserve and the price for provision of the service. The developed approach is illustrated with a case study based on the current day-ahead secondary reserve market in Spain. [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]