Unit Commitment Scheduling by using the Neural Network-Weighted Frequency Bin Blocks Model based-Next Day Load Forecasting

U.B. Filik and M. Kurban (Turkey)


Power System Planning, Power Economics, Load Forecast ing, Artificial Neural Network and Unit Commitment.


Unit commitment (UC) and load forecasting analyses are important because low-cost generation is one of the most significant points in power systems. Since UC solves for an optimum schedule of generating units based on load fore casting data, an accurate load forecasting is also very im portant in power system optimal planning and operation. Scheduling improperly the generating units due to under forecasting or over forecasting will result in the require ment of purchasing power from spot market or an unneces sary commitment of generating units. Therefore, the load forecasting is made as the first step to enhance the UC so lution. Artificial Neural Network (ANN) and ANN model with Weighted Frequency Bin Blocks (WFBB) are used for the load forecasting. Then UC problem is solved by us ing the SA method and simulation results of these methods are compared. Comparing to these total costs show that load forecasting is important for unit commitment. Four unit Tuncbilek thermal plant which is in Kutahya region in Turkey, is used for this analysis. The data used in the anal ysis is taken from Turkish Electric Power Company and Electricity Generation Company. All the analyses are im plemented using MATLAB.

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