Date

2012

Author

Özgüner, Erdem

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With the aim for higher economical efficiency, considerable and radical changes have occurred in the worldwide electricity sector since the beginning of 1980s. By that time, the electricity sector has been controlled by the state-owned vertically integrated monopolies which manage and control all generation, transmission, distribution and retail activities and the consumers buy electricity with a price set by these monopolies in that system. After the liberalization and restructuring of the electricity power sector, separation and privatization of these activities have been widely seen. The main purpose is to ensure competition in the market where suppliers and consumers compete with each other to sell or buy electricity from the market and the consumers buy the electricity with a price which is based on competition and determined according to sell and purchase bids given by producers and customers rather than a price set by the government. Due to increasing competition in the electricity market, accurate electricity price forecasts have become a very vital need for all market participants. Accurate forecast of electricity price can help suppliers to derive their bidding strategy and optimally design their bilateral agreements in order to maximize their profits and hedge against risks. Consumers need accurate price forecasts for deriving their electricity usage and bidding strategy for minimizing their utilization costs. This thesis presents the determination of system day ahead price (SGOF) at the day ahead market and system marginal price (SMF) at the balancing power market in detail and develops artificial neural network models together with multiple linear regression models to forecast these electricity prices in Turkish electricity market. Also the methods used for price forecasting in the literature are discussed and the comparisons between these methods are presented. A series of historical data from Turkish electricity market is used to understand the characteristics of the market and the necessary input factors which influence the electricity price is determined for creating ANN models for price forecasting in this market. Since the factors influencing SGOF and SMF are different, different ANN models are developed for forecasting these prices. For SGOF forecasting, historical price and load values are enough for accurate forecasting, however, for SMF forecasting the net instruction volume occurred due to real time system imbalances is needed in order to increase the forecasting accuracy.

Subject Keywords

Electric power consumption., Electric power consumption, Electric power distribution.

URI

http://etd.lib.metu.edu.tr/upload/12615169/index.pdf
https://hdl.handle.net/11511/22194

Collections

Graduate School of Natural and Applied Sciences, Thesis