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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles

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Zandrazavi, Seyed Farhad [1] ; Guzman, Cindy Paola [2] ; Tabares Pozos, Alejandra [3] ; Quiros-Tortos, Jairo [4] ; Franco, John Fredy [1, 5]
Total Authors: 5
[1] Sao Paulo State Univ, Dept Elect Engn, Sao Paulo - Brazil
[2] Univ Estadual Campinas, Dept Syst & Energy, UNICAMP, Campinas - Brazil
[3] Los Andes Univ, Dept Ind Engn, Bogota - Colombia
[4] Univ Costa Rica, Sch Elect Engn, San Jose - Costa Rica
[5] Sao Paulo State Univ, Sch Energy Engn, Rosana - Brazil
Total Affiliations: 5
Document type: Journal article
Source: ENERGY; v. 241, FEB 15 2022.
Web of Science Citations: 0

Microgrids (MGs) contribute to the integration of renewable energy-based distributed generation (DG) units and electric vehicles (EVs) in a smart, secure, sustainable, and economic fashion. However, the unbalanced nature of MGs along with the probabilistic nature of renewable energy, electricity prices, and EV demand complicate the energy management process. To overcome that challenge, a stochastic multi objective optimization model for grid-connected unbalanced MGs is proposed here to minimize the total operational cost and the voltage deviation. The epsilon-constraint method and fuzzy satisfying approach are used to solve the multi-objective optimization problem and to obtain compromise solutions. Uncertainties are considered by employing the roulette wheel mechanism for generating scenarios regarding renewable energy generations, EV charging demands, electric loads, and electricity prices. In addition, to avoid adopting infeasible and impractical solutions, a three-phase power flow is integrated in the proposed model. The proposed method is assessed in a modified IEEE 34-bus test system consisting of EVs, battery systems, wind turbine units, photovoltaic units, and diesel generators. The results show the effectiveness and benefits of the proposed model for handling uncertainties while minimizing both operational cost and voltage deviation index and providing more realistic and reliable solutions that can be applied by MG operators. (c) 2021 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 18/20990-9 - New optimization models to solve the distribution system planning problem in the context of active networks
Grantee:Alejandra Tabares Pozos
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 15/21972-6 - Optimization of the operation and planning in transmission and distribution systems
Grantee:Rubén Augusto Romero Lázaro
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 17/02831-8 - Application of optimization methods to the planning of electrical distribution systems
Grantee:John Fredy Franco Baquero
Support Opportunities: Regular Research Grants
FAPESP's process: 18/08008-4 - Coordinated energy resource management under uncertainty considering electric vehicles and demand flexibility in distribution systems
Grantee:Rubén Augusto Romero Lázaro
Support Opportunities: Regular Research Grants