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Appl. Sci. 2017, 7(5), 438; doi:10.3390/app7050438

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan
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Academic Editors: Emanuele Giovanni Carlo Ogliari and Sonia Leva
Received: 25 February 2017 / Revised: 17 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Abstract

The demand response and accommodation of different renewable energy resources are essential factors in a modern smart microgrid. This paper investigates the energy management related to the short-term (24 h) unit commitment and demand response in a factory power system with uncertain photovoltaic power generation. Elastic loads may be activated subject to their operation constraints in a manner determined by the electricity prices while inelastic loads are inflexibly fixed in each hour. The generation of power from photovoltaic arrays is modeled as a Gaussian distribution owing to its uncertainty. This problem is formulated as a stochastic mixed-integer optimization problem and solved using two levels of algorithms: the master level determines the optimal states of the units (e.g., micro-turbine generators) and elastic loads; and the slave level concerns optimal real power scheduling and power purchase/sale from/to the utility, subject to system operating constraints. This paper proposes two novel encoding schemes used in genetic algorithms on the master level; the point estimate method, incorporating the interior point algorithm, is used on the slave level. Various scenarios in a 30-bus factory power system are studied to reveal the applicability of the proposed method. View Full-Text
Keywords: demand response; genetic algorithm; renewable energy; unit commitment; uncertainty demand response; genetic algorithm; renewable energy; unit commitment; uncertainty
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Hong, Y.-Y.; Yo, P.-S. Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies. Appl. Sci. 2017, 7, 438.

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