The optimization of demand response programs in smart grids |
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| Affiliation: | 1. Department of Mathematics, Sri Ramakrishna Institute of Technology, Coimbatore 641010, India;2. Department of Mathematics, Sungkyunkwan University, Suwon 440-746, South Korea;3. Graduate School of Science and Technology, Tokai University, 9-1-1, Toroku, Kumamoto 862-8652, Japan;1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;2. Key Laboratory of System Control and Information Processing, Ministry of Education, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;2. School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China |
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| Abstract: | The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques. |
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| Keywords: | Demand response Smart grids Pricing strategy TLBO SFL algorithm |
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