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

Author(s)

Zandrazavi, Seyed Farhad ^[1] ; Guzman, Cindy Paola ^[2] ; Tabares Pozos, Alejandra ^[3] ; Quiros-Tortos, Jairo ^[4] ; Franco, John Fredy ^{[1, 5]}

Affiliation

^[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

• Document type: Journal article
• Source: ENERGY; v. 241, FEB 15 2022.
• Web of Science Citations: 0

Abstract

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