TY - JOUR
T1 - Multi-objective centralization-decentralization trade-off analysis for multi-source renewable electricity generation expansion planning
T2 - A case study of Iran
AU - Toloo, Mehdi
AU - Taghizadeh-Yazdi, Mohammadreza
AU - Mohammadi-Balani, Abdolkarim
N1 - Funding Information:
This work is partially supported by the High-Performance Computing Research Center (HPCRC) at Amirkabir University of Technology ( ISI-DCE-DOD-Cloud-700101-3359 ) and the Czech Science Foundation ( GAČR 19-13946S ).
Publisher Copyright:
© 2021 Elsevier Ltd
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PY - 2022/2
Y1 - 2022/2
N2 - Countries need robust long-term plans to keep up with the global pace of transitioning from pollutant fossil fuels towards clean, renewable energies. Renewable energy generation expansion plans can be either centralized, decentralized, or a combination of these two. This paper presents a novel approach to obtain an optimal multi-period plan for generating each type of renewable energy (solar, wind, hydro, geothermal, and biomass) via multi-objective mathematical modeling. The proposed model has integrated with Autoregressive Integrated Moving Average (ARIMA) econometric method to forecast the country's demand during the planning horizon. The optimal energy mix based on several socio-economic aspects of renewable sources was obtained using the Passive and Active Compensability Multicriteria ANalysis (PACMAN) multi-attribute decision-making method. The model has been solved by a Non-dominated Sorting Genetic Algorithm (NSGA-II) metaheuristic algorithm. Each solution in the Pareto front contains a plan for each electricity generation region under a certain combination of centralization and decentralization strategies.
AB - Countries need robust long-term plans to keep up with the global pace of transitioning from pollutant fossil fuels towards clean, renewable energies. Renewable energy generation expansion plans can be either centralized, decentralized, or a combination of these two. This paper presents a novel approach to obtain an optimal multi-period plan for generating each type of renewable energy (solar, wind, hydro, geothermal, and biomass) via multi-objective mathematical modeling. The proposed model has integrated with Autoregressive Integrated Moving Average (ARIMA) econometric method to forecast the country's demand during the planning horizon. The optimal energy mix based on several socio-economic aspects of renewable sources was obtained using the Passive and Active Compensability Multicriteria ANalysis (PACMAN) multi-attribute decision-making method. The model has been solved by a Non-dominated Sorting Genetic Algorithm (NSGA-II) metaheuristic algorithm. Each solution in the Pareto front contains a plan for each electricity generation region under a certain combination of centralization and decentralization strategies.
KW - Autoregressive Integrated Moving Average (ARIMA)
KW - Decentralization
KW - Electricity generation expansion planning
KW - Non-dominated Sorting Genetic Algorithm (NSGA-II)
KW - Passive and Active Compensability Multicriteria ANalysis (PACMAN)
KW - Renewable energy
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U2 - 10.1016/j.cie.2021.107870
DO - 10.1016/j.cie.2021.107870
M3 - Article
AN - SCOPUS:85121965773
SN - 0360-8352
VL - 164
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
M1 - 107870
ER -
