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Optimal sizing of renewable energy generations in a community microgrid using Markov model

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  • Hong, Ying-Yi
  • Chang, Wen-Chun
  • Chang, Yung-Ruei
  • Lee, Yih-Der
  • Ouyang, Der-Chuan

Abstract

The installation of renewable energy generation resources (such as photovoltaic arrays and wind-turbine generators) in a microgrid is important because a microgrid can increase the penetration of renewable energies in a smart grid. A community may be a grid-tied microgrid in which an energy management system may dispatch elastic loads (such as air conditioning systems). This paper investigates the optimal sizing of renewable energy generation resources in a community microgrid. The cost of renewables and community welfare are optimized while the comfort zone of indoor temperature in all homes is maintained using air conditioning systems. Community welfare is ensured by minimizing the purchased power from and maximizing the sold power to the utility grid with different time-of-use electricity tariffs. Since the problem of interest involves a large number of variables and chronological constraints, Markov models of photovoltaic power generation, wind generation, load and temperature are utilized to reduce the numbers of variables and constraints. The Markov-based optimization problem is then solved using the interior-point algorithm. The simulation results, based on a smart community of 50 homes, reveal the applicability of the proposed method.

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  • Hong, Ying-Yi & Chang, Wen-Chun & Chang, Yung-Ruei & Lee, Yih-Der & Ouyang, Der-Chuan, 2017. "Optimal sizing of renewable energy generations in a community microgrid using Markov model," Energy, Elsevier, vol. 135(C), pages 68-74.
    • Handle: RePEc:eee:energy:v:135:y:2017:i:c:p:68-74
    DOI: 10.1016/j.energy.2017.06.098
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    Cited by:

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    3. Sun, Qirun & Wu, Zhi & Gu, Wei & Zhu, Tao & Zhong, Lei & Gao, Ting, 2021. "Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach," Energy, Elsevier, vol. 226(C).
    4. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Ghasemi, Mostafa & Dashti, Reza, 2018. "Designing a decision model to assess the reward and penalty scheme of electric distribution companies," Energy, Elsevier, vol. 147(C), pages 329-336.
    6. Amir, Vahid & Azimian, Mahdi, 2020. "Dynamic Multi-Carrier Microgrid Deployment Under Uncertainty," Applied Energy, Elsevier, vol. 260(C).
    7. Ping Liu & Zexiang Cai & Peng Xie & Xiaohua Li & Yongjun Zhang, 2019. "A Computationally Efficient Optimization Method for Battery Storage in Grid-connected Microgrids Based on a Power Exchanging Process," Energies, MDPI, vol. 12(8), pages 1-19, April.
    8. Ghanaee, Reza & Akbari Foroud, Asghar, 2019. "Enhanced structure and optimal capacity sizing method for turbo-expander based microgrid with simultaneous recovery of cooling and electrical energy," Energy, Elsevier, vol. 170(C), pages 284-304.
    9. Meena, Nand K. & Yang, Jin & Zacharis, Evan, 2019. "Optimisation framework for the design and operation of open-market urban and remote community microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Swaminathan, Siddharth & Pavlak, Gregory S. & Freihaut, James, 2020. "Sizing and dispatch of an islanded microgrid with energy flexible buildings," Applied Energy, Elsevier, vol. 276(C).
    11. Hao Liu & Nadali Mahmoudi & Kui Chen, 2018. "Microgrids Real-Time Pricing Based on Clustering Techniques," Energies, MDPI, vol. 11(6), pages 1-12, May.
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