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Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties

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  • Chen, Xianqing
  • Dong, Wei
  • Yang, Qiang

Abstract

Microgrid is considered an efficient paradigm for managing the massive number of distributed renewable generation and storage facilities. The optimal microgrid capacity planning is a non-trivial task due to the impact of randomness and uncertainties of renewable generation sources, and the adopted energy management strategies. In this paper, an optimal capacity planning model for the grid-connected microgrid is developed fully considering the renewable generation uncertainties through efficient scenario generation and reduction based on the deep convolutional generative adversarial network (DCGAN) and improved k-medoids clustering algorithm, as well as the microgrid energy management strategy. The proposed solution optimizes the capacity planning for the maximization of renewable energy utilization efficiency, and minimizes the economic cost and carbon emissions. The proposed solution is assessed using a case study of a microgrid (MG) project in northern China through a comparative study and the numerical results confirm the cost-effectiveness of the proposed solution.

Suggested Citation

  • Chen, Xianqing & Dong, Wei & Yang, Qiang, 2022. "Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties," Applied Energy, Elsevier, vol. 323(C).
  • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009436
    DOI: 10.1016/j.apenergy.2022.119642
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    as
    1. Ghadi, Mojtaba Jabbari & Rajabi, Amin & Ghavidel, Sahand & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2019. "From active distribution systems to decentralized microgrids: A review on regulations and planning approaches based on operational factors," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Hakimi, Seyed Mehdi & Hasankhani, Arezoo & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market," Applied Energy, Elsevier, vol. 298(C).
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    5. Bernal-Agustín, José L. & Dufo-López, Rodolfo & Rivas-Ascaso, David M., 2006. "Design of isolated hybrid systems minimizing costs and pollutant emissions," Renewable Energy, Elsevier, vol. 31(14), pages 2227-2244.
    6. Abdelkader, Abbassi & Rabeh, Abbassi & Mohamed Ali, Dami & Mohamed, Jemli, 2018. "Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage," Energy, Elsevier, vol. 163(C), pages 351-363.
    7. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José, 2016. "Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants," Applied Energy, Elsevier, vol. 162(C), pages 21-30.
    8. Ehsan, Ali & Yang, Qiang, 2019. "Scenario-based investment planning of isolated multi-energy microgrids considering electricity, heating and cooling demand," Applied Energy, Elsevier, vol. 235(C), pages 1277-1288.
    9. Zhou, Wei & Lou, Chengzhi & Li, Zhongshi & Lu, Lin & Yang, Hongxing, 2010. "Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems," Applied Energy, Elsevier, vol. 87(2), pages 380-389, February.
    10. Dong, Wei & Chen, Xianqing & Yang, Qiang, 2022. "Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability," Applied Energy, Elsevier, vol. 308(C).
    11. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
    12. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island," Applied Energy, Elsevier, vol. 121(C), pages 149-158.
    13. Mukhopadhyay, Bineeta & Das, Debapriya, 2021. "Optimal multi-objective expansion planning of a droop-regulated islanded microgrid," Energy, Elsevier, vol. 218(C).
    14. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    15. Guo, Li & Hou, Ruosong & Liu, Yixin & Wang, Chengshan & Lu, Hai, 2020. "A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid," Applied Energy, Elsevier, vol. 263(C).
    16. Hart, Elaine K. & Jacobson, Mark Z., 2011. "A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables," Renewable Energy, Elsevier, vol. 36(8), pages 2278-2286.
    17. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
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    5. Wei Wei & Li Ye & Yi Fang & Yingchun Wang & Xi Chen & Zhenhua Li, 2023. "Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    6. Tan, Mao & Li, Zibin & Su, Yongxin & Ren, Yuling & Wang, Ling & Wang, Rui, 2024. "Dual time-scale robust optimization for energy management of distributed energy community considering source-load uncertainty," Renewable Energy, Elsevier, vol. 226(C).
    7. Fabian Zuñiga-Cortes & Eduardo Caicedo-Bravo & Juan D. Garcia-Racines, 2023. "Reference Framework Based on a Two-Stage Strategy for Sizing and Operational Management in Electrical Microgrid Planning," Sustainability, MDPI, vol. 15(19), pages 1-27, October.
    8. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2022. "Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 866-880.

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