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Title: Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power
Authors: Biswas, Partha Pratim
Suganthan, Ponnuthurai Nagaratnam
Qu, Bo Yang
Amaratunga, Gehan A. J.
Keywords: Economic-environmental Dispatch
DRNTU::Engineering::Electrical and electronic engineering
Wind Power Generator
Issue Date: 2018
Source: Biswas, P. P., Suganthan, P. N., Qu, B. Y., & Amaratunga, G. A. J. (2018). Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power. Energy, 150, 1039-1057. doi:10.1016/
Series/Report no.: Energy
Abstract: Economic-environmental power dispatch is one of the most popular bi-objective non-linear optimization problems in power system. Classical economic power dispatch problem is formulated with only thermal generators often ignoring security constraints of the network. But importance of reduction in emission is paramount from environmental sustainability perspective and hence penetration of more and more renewable sources into the electrical grid is encouraged. However, most common forms of renewable sources are intermittent and uncertain. This paper proposes multiobjective economic emission power dispatch problem formulation and solution incorporating stochastic wind, solar and small-hydro (run-of-river) power. Weibull, lognormal and Gumbel probability density functions are used to calculate available wind, solar and small-hydro power respectively. Some conventional generators of the standard IEEE 30-bus system are replaced with renewable power sources for study purpose. Network security constraints such as transmission line capacities and bus voltage limits are also taken into consideration alongwith constraints on generator capabilities and prohibited operating zones for the thermal units. Decomposition based multiobjective evolutionary algorithm and summation based multiobjective differential evolution algorithm are applied to the problem under study. An advanced constraint handling technique, superiority of feasible solutions, is integrated with both the multiobjective algorithms to comply with system constraints. The simulation results of both the algorithms are summarized, analyzed and compared in this study.
ISSN: 0360-5442
DOI: 10.1016/
Rights: © 2018 Elsevier Ltd. All rights reserved. This paper was published in Energy and is made available with permission of Elsevier Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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