-This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.
This paper presents a windows-based educational simulator with user-friendly graphical user interface (GUI) for the education and training of particle swarm optimization (PSO) technique for mathematical optimization problems and economic dispatch (ED) applications. The main objective for developing the simulator is to provide information with the electrical engineering undergraduate students that the up-to-date artificial intelligent (AI) techniques including PSO are actively used in power system optimization problems. The simulator can be used as a lecturing tool to stimulate an interest in the power system engineering of the undergraduate students. The students can be more familiar with the optimization problems including power system ED problem through the iterative uses of the simulator. Also, they can increase understandings on PSO mechanism by the homework on the optimal design of several control parameters such as inertia weight, acceleration coefficients, and the number of population, etc. In the developed simulator, instructors and students can select the optimization functions and set the parameters that have an influence on PSO performance. The simulator is applied not only to mathematical optimization functions but also to economic dispatch (ED) problems with nonsmooth cost functions, which is designed so that users can solve other mathematical functions through simple additional MATLAB coding.Index Terms-Educational simulator, power system optimization, economic dispatch, particle swarm optimization.
-This paper presents an economic analysis of the energy efficiency programs in Korea. Economic evaluation was performed by applying the California Standard Practice Test. Additionally, the energy efficiency programs were reviewed using a levelized cost evaluation methodology based on the marginal cost of the saved energy, and comparative analysis was performed with the cost of the supply resources. Finally, the future needs and development directions of the Korean energy efficiency programs are suggested.
This paper presents a novel and efficient method for solving the economic dispatch problems with non-smooth cost functions, by integrating the particle swarm optimization (PSO) with the chaotic sequences. The proposed improved particle swarm optimization (IPSO) combines the particle swarm optimization algorithm with chaotic sequences technique. A particle swarm optimization is one of the most powerful methods for solving global optimization problems. The application of chaotic sequences in PSO is an efficient strategy to improve the global searching capability and escape from local minima. To show the effectiveness of the proposed method, the numerical studies have been performed for three different sample systems. The proposed IPSO outperforms other state-of-the-art algorithms in solving economic dispatch problems with valvepoint and multi-fuel effects.
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