Generation scheduling in smart grid environment using global best artificial bee colony algorithm |
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| Affiliation: | 1. Dept. of Computing, University of Oviedo, Spain;2. Dept. of Mathematics, Statistics and Computing, University of Cantabria, Spain;3. Independent researcher;1. Department of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, Western Australia 6102, Australia;2. School of Electronic, Electrical and Systems Engineering, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK;1. School of Management, Hefei University of Technology, Hefei 230009, China;2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China;3. Qingdao Power Supply Company Control Center, Shandong Electric Power Group, Qingdao 266300, China;4. Hunan Electric Power Corporation Research Institute, State Grid Corporation, Anti-Icing and Reducing-Disaster Technology, Key Laboratory of Power Transmission and Transformation Equipments, Changsha 410007, China;1. Department of Mechanical & Biomedical Engineering, Boise State University, Boise, ID, 83725, United States;2. Department of Mechanical Engineering, University of Idaho, Moscow, ID, 83844, United States |
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| Abstract: | Generation scheduling is an important concern of the current power system which is suffering from many obstacles of limited generation resources, grown energy demand and fuel price, inconsistent load demand and fluctuations of available wind power in case of the thermal–wind system. Smart grid system has a great potential of tumbling existing power system difficulties with intelligent infrastructure and computation technologies. Three different distributed energy resources, namely, distributed generation, demand response and gridable vehicles are used in this paper to overcome the power system hitches. The classical generation scheduling is solved with insertion of the cost of demand response and the cost model pertaining to underestimation and overestimation of fluctuating wind power. The modified optimization problem is solved using an efficient Global best artificial bee colony algorithm for 10 generating units test system. Generation scheduling in the smart grid environment yields a significant reduction in the total cost. |
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| Keywords: | Distributed energy resources (DERs) Demand response program (DRP) Global best artificial bee colony (GABC) algorithm Gridable vehicle (GV) Unit commitment problem (UCP) Weibull distribution function |
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