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Wind-PV-storage optimal environomic design using multi-objective artificial bee colony

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Abstract
This paper proposes a multi-objective optimization formulation to design a hybrid wind-photovoltaic-storage system to supply the demand. This design problem aims to minimize the annual cost of the overall system as well as the CO2 emissions, and is solved by Artificial Bee Colony (ABC) algorithm. Solar irradiation, wind speed, and load data are assumed deterministic. Prices are all empirical and components of hybrid system are commercially available. A test system in the Northwestern Iran is investigated. The presented technique yields the optimal number of system devices such that the economic and environmental profits are maximized. A fuzzy decision making (FDM) method is applied for finding the best compromise solution from the set of Pareto-optimal solutions obtained by ABC.
Keywords
METHODOLOGY, OPTIMIZATION, POWER-GENERATION, SYSTEMS

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MLA
Shayeghi, H., et al. “Wind-PV-Storage Optimal Environomic Design Using Multi-Objective Artificial Bee Colony.” 2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), IEEE, 2015, doi:10.1109/APPEEC.2015.7381057.
APA
Shayeghi, H., Moradzadeh, M., Hashemi, Y., Saif, M., & Vandevelde, L. (2015). Wind-PV-storage optimal environomic design using multi-objective artificial bee colony. 2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC). Presented at the IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Brisbane, Australia. https://doi.org/10.1109/APPEEC.2015.7381057
Chicago author-date
Shayeghi, H, Mohammad Moradzadeh, Y Hashemi, M Saif, and Lieven Vandevelde. 2015. “Wind-PV-Storage Optimal Environomic Design Using Multi-Objective Artificial Bee Colony.” In 2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC). IEEE. https://doi.org/10.1109/APPEEC.2015.7381057.
Chicago author-date (all authors)
Shayeghi, H, Mohammad Moradzadeh, Y Hashemi, M Saif, and Lieven Vandevelde. 2015. “Wind-PV-Storage Optimal Environomic Design Using Multi-Objective Artificial Bee Colony.” In 2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC). IEEE. doi:10.1109/APPEEC.2015.7381057.
Vancouver
1.
Shayeghi H, Moradzadeh M, Hashemi Y, Saif M, Vandevelde L. Wind-PV-storage optimal environomic design using multi-objective artificial bee colony. In: 2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC). IEEE; 2015.
IEEE
[1]
H. Shayeghi, M. Moradzadeh, Y. Hashemi, M. Saif, and L. Vandevelde, “Wind-PV-storage optimal environomic design using multi-objective artificial bee colony,” in 2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), Brisbane, Australia, 2015.
@inproceedings{7040052,
  abstract     = {{This paper proposes a multi-objective optimization formulation to design a hybrid wind-photovoltaic-storage system to supply the demand. This design problem aims to minimize the annual cost of the overall system as well as the CO2 emissions, and is solved by Artificial Bee Colony (ABC) algorithm. Solar irradiation, wind speed, and load data are assumed deterministic. Prices are all empirical and components of hybrid system are commercially available. A test system in the Northwestern Iran is investigated. The presented technique yields the optimal number of system devices such that the economic and environmental profits are maximized. A fuzzy decision making (FDM) method is applied for finding the best compromise solution from the set of Pareto-optimal solutions obtained by ABC.}},
  author       = {{Shayeghi, H and Moradzadeh, Mohammad and Hashemi, Y and Saif, M and Vandevelde, Lieven}},
  booktitle    = {{2015 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC)}},
  isbn         = {{978-1-4673-8132-1}},
  keywords     = {{METHODOLOGY,OPTIMIZATION,POWER-GENERATION,SYSTEMS}},
  language     = {{eng}},
  location     = {{Brisbane, Australia}},
  pages        = {{5}},
  publisher    = {{IEEE}},
  title        = {{Wind-PV-storage optimal environomic design using multi-objective artificial bee colony}},
  url          = {{http://doi.org/10.1109/APPEEC.2015.7381057}},
  year         = {{2015}},
}

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