Abstract
Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.
Highlights
Unit Commitment (UC) is a complicated integrational optimization method used in power systems
A steady rise in fuel charges and a rapid fossil fuels depletion have opened the way for the use of renewable sources for power generation
With the deployment of renewable sources, the UC issue becomes more complicated, providing obvious differences in behavioral and technical restrictions on traditional thermal generation systems that need to be resolved, as renewable generation will be integrated in the electrical network
Summary
Unit Commitment (UC) is a complicated integrational optimization method used in power systems. A recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. Unit Commitment (UC) is considered to be one of the most significant remarkable problems in a power system, as it aims to decide the best schedule and the rate of the generating unit’s production in the power system for a specific time interval by facing given forecasted load data [1]. The only optimizing pattern in deciding the UC schedule is the generation cost, which must be minimized over a planning cycle while satisfying all the system constraints resulting from the physical capacities of the generating unit and the network design of the transmission system. Each generator has different limitations—such as maximum and minimum generation limits, minimum down-up time, the ramp rates limit, and so forth
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