Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles
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 pri...
| Authors: | , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | Brasil |
| Institution: | Universidade Estadual Paulista (UNESP) |
| Repository: | Repositório Institucional da UNESP |
| Language: | English |
| OAI Identifier: | oai:repositorio.unesp.br:11449/223094 |
| Online Access: | http://dx.doi.org/10.1016/j.energy.2021.122884 http://hdl.handle.net/11449/223094 |
| Access Level: | Open access |
| Keyword: | Electric vehicles Microgrids Multi-objective optimization Renewable energy Stochastic optimization |
| Summary: | 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. |
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