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Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

Author

Listed:
  • Wei Wei

    (State Grid Hubei Marketing Service Center (Measurement Center), Wuhan 443080, China)

  • Li Ye

    (State Grid Hubei Marketing Service Center (Measurement Center), Wuhan 443080, China)

  • Yi Fang

    (State Grid Hubei Marketing Service Center (Measurement Center), Wuhan 443080, China)

  • Yingchun Wang

    (State Grid Hubei Marketing Service Center (Measurement Center), Wuhan 443080, China)

  • Xi Chen

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Zhenhua Li

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

Abstract

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K -means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9544-:d:1170744
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    References listed on IDEAS

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    1. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    2. Zhou, Yuzhou & Zhai, Qiaozhu & Yuan, Wei & Wu, Jiang, 2021. "Capacity expansion planning for wind power and energy storage considering hourly robust transmission constrained unit commitment," Applied Energy, Elsevier, vol. 302(C).
    3. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    4. Kong, Xiangyu & Xiao, Jie & Liu, Dehong & Wu, Jianzhong & Wang, Chengshan & Shen, Yu, 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties," Applied Energy, Elsevier, vol. 279(C).
    5. Li, Shenglin & Zhu, Jizhong & Dong, Hanjiang & Zhu, Haohao & Fan, Junwei, 2022. "A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids," Applied Energy, Elsevier, vol. 309(C).
    6. Tang, Rui & Dore, Jonathon & Ma, Jin & Leong, Philip H.W., 2021. "Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network," Applied Energy, Elsevier, vol. 299(C).
    7. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    8. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    9. Maysam Abbasi & Ehsan Abbasi & Li Li & Ricardo P. Aguilera & Dylan Lu & Fei Wang, 2023. "Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods," Energies, MDPI, vol. 16(1), pages 1-36, January.
    10. 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).
    11. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
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    Cited by:

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    2. Alessandro Pracucci & Laura Vandi & Francesco Belletti & Amanda Ramos Aragão Melo & Marios Vlachos & Angelos Amditis & Maria Teresa Calcagni & David Seixas Esteves, 2024. "Integration of Piezoelectric Energy Harvesting Systems into Building Envelopes for Structural Health Monitoring with Fiber Optic Sensing Technology," Energies, MDPI, vol. 17(7), pages 1-31, April.
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    4. Binqiao Zhang & Junwei Huang, 2024. "Shared Energy Storage Capacity Configuration of a Distribution Network System with Multiple Microgrids Based on a Stackelberg Game," Energies, MDPI, vol. 17(13), pages 1-21, June.

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