An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling |
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| Affiliation: | 1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;2. Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;1. Dr. B.C. Roy Engineering College, Durgapur, West Bengal 713206, India;2. National Institute of Technology-Agartala, Tripura 799046, India;3. The Department of Electrical Engineering, Jadavpur University, Kolkata, West Bengal 700032, India;1. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala 147004, Punjab, India;2. School of Mathematics, Thapar University, Patiala 147004, Punjab, India;1. Electrical Engineering Department, Dr. BC Roy Engineering College, Durgapur, Durgapur 713206, India;2. Electrical Engineering Department, Dumkal Institute of Engineering and Technology, Basantapur, Murshidabad, India;3. Electrical Engineering Department, IIEST, Shibpur, Howrah 711103, India;1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;2. Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China |
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| Abstract: | Short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a set of constraints, which plays an important role in power system operations. In this paper, we propose to use an adaptive chaotic artificial bee colony (ACABC) algorithm to solve the SHS problem. In the proposed method, chaotic search is applied to help the artificial bee colony (ABC) algorithm to escape from a local optimum effectively. Furthermore, an adaptive coordinating mechanism of modification rate in employed bee phase is introduced to increase the ability of the algorithm to avoid premature convergence. Moreover, a new constraint handling method is combined with the ABC algorithm in order to solve the equality coupling constraints. We used a hydrothermal test system to demonstrate the effectiveness of the proposed method. The numerical results obtained by ACABC are compared with those obtained by the adaptive ABC algorithm (AABC), the chaotic ABC algorithm (CABC) and other methods mentioned in literature. The simulation results indicate that the proposed method outperforms those established optimization algorithms. |
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| Keywords: | Hydrothermal scheduling Artificial bee colony algorithm Adaptive Constrained optimization Swarm intelligence |
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