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Home Load-Side Management in Smart Grids Using Global Optimization

Home Load-Side Management in Smart Grids Using Global Optimization

Abdelmadjid Recioui
Copyright: © 2019 |Pages: 35
ISBN13: 9781522580300|ISBN10: 1522580301|ISBN13 Softcover: 9781522590538|EISBN13: 9781522580317
DOI: 10.4018/978-1-5225-8030-0.ch005
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MLA

Recioui, Abdelmadjid. "Home Load-Side Management in Smart Grids Using Global Optimization." Handbook of Research on Smart Power System Operation and Control, edited by Hassan Haes Alhelou and Ghassan Hayek, IGI Global, 2019, pp. 127-161. https://doi.org/10.4018/978-1-5225-8030-0.ch005

APA

Recioui, A. (2019). Home Load-Side Management in Smart Grids Using Global Optimization. In H. Alhelou & G. Hayek (Eds.), Handbook of Research on Smart Power System Operation and Control (pp. 127-161). IGI Global. https://doi.org/10.4018/978-1-5225-8030-0.ch005

Chicago

Recioui, Abdelmadjid. "Home Load-Side Management in Smart Grids Using Global Optimization." In Handbook of Research on Smart Power System Operation and Control, edited by Hassan Haes Alhelou and Ghassan Hayek, 127-161. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8030-0.ch005

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Abstract

Demand-side management (DSM) is a strategy enabling the power supplying companies to effectively manage the increasing demand for electricity and the quality of the supplied power. The main objectives of DSM programs are to improve the financial performance and customer relations. The idea is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times. The DSM controls the match between the demand and supply of electricity. Another objective of DSM is to maintain the power quality in order to level the load curves. In this chapter, a genetic algorithm is used in conjunction with demand-side management techniques to find the optimal scheduling of energy consumption inside N buildings in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost. The simulations reveal that the adopted strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.

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