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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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    1. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    2. Huva, Robert & Dargaville, Roger & Caine, Simon, 2012. "Prototype large-scale renewable energy system optimisation for Victoria, Australia," Energy, Elsevier, vol. 41(1), pages 326-334.
    3. Kim, Wonuk & Jeon, Seung Won & Kim, Yongchan, 2016. "Model-based multi-objective optimal control of a VRF (variable refrigerant flow) combined system with DOAS (dedicated outdoor air system) using genetic algorithm under heating conditions," Energy, Elsevier, vol. 107(C), pages 196-204.
    4. Singh, Parm Pal & Singh, Sukhmeet, 2010. "Realistic generation cost of solar photovoltaic electricity," Renewable Energy, Elsevier, vol. 35(3), pages 563-569.
    5. Wang, Weimin & Katipamula, Srinivas & Ngo, Hung & Underhill, Ronald & Taasevigen, Danny & Lutes, Robert, 2015. "Field evaluation of advanced controls for the retrofit of packaged air conditioners and heat pumps," Applied Energy, Elsevier, vol. 154(C), pages 344-351.
    6. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    7. Carapellucci, Roberto & Giordano, Lorena, 2013. "A new approach for synthetically generating wind speeds: A comparison with the Markov chains method," Energy, Elsevier, vol. 49(C), pages 298-305.
    8. Sharafi, Masoud & ELMekkawy, Tarek Y., 2014. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach," Renewable Energy, Elsevier, vol. 68(C), pages 67-79.
    9. Mehleri, E.D. & Sarimveis, H. & Markatos, N.C. & Papageorgiou, L.G., 2013. "Optimal design and operation of distributed energy systems: Application to Greek residential sector," Renewable Energy, Elsevier, vol. 51(C), pages 331-342.
    10. Karellas, Sotirios & Terzis, Konstantinos & Manolakos, Dimitrios, 2011. "Investigation of an autonomous hybrid solar thermal ORC–PV RO desalination system. The Chalki island case," Renewable Energy, Elsevier, vol. 36(2), pages 583-590.
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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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