Abstract
Demand Response (DR) is the strategy adopted by the electric utility company to shift the energy consumption plan hours to the off-peak period. Usually, the optimization models of DR plan to cut down the energy consumption at peak period to reduce the customer cost of electricity. The need of the problem is the real-time optimization that focuses on residents with uncompromised electricity usage and reduced cost. With the advent of smart meters the customers can participate in the Dynamic Demand Response (DDR) program offered by the utilities. In this paper, the task of optimal load scheduling is formulated as the optimization problem and an improved genetic algorithm is applied to solve this problem. The performance of the proposed approach has been evaluated by the load data set and the simulation results are reported.
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Jeyaranjani, J., Devaraj, D. (2021). Genetic Algorithm Based Resident Load Scheduling for Electricity Cost Reduction. In: Bajpai, M.K., Kumar Singh, K., Giakos, G. (eds) Machine Vision and Augmented Intelligence—Theory and Applications. Lecture Notes in Electrical Engineering, vol 796. Springer, Singapore. https://doi.org/10.1007/978-981-16-5078-9_46
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DOI: https://doi.org/10.1007/978-981-16-5078-9_46
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