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Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids

Author

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  • Yong-Rae Lee

    (Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 006974, Korea)

  • Hyung-Joon Kim

    (Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 006974, Korea)

  • Mun-Kyeom Kim

    (Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 006974, Korea)

Abstract

As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.

Suggested Citation

  • Yong-Rae Lee & Hyung-Joon Kim & Mun-Kyeom Kim, 2021. "Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids," Energies, MDPI, vol. 14(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:470-:d:481925
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    References listed on IDEAS

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    1. Kyu-Hyung Jo & Mun-Kyeom Kim, 2018. "Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty," Energies, MDPI, vol. 11(4), pages 1-17, March.
    2. Ho-Sung Ryu & Mun-Kyeom Kim, 2020. "Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty," Energies, MDPI, vol. 13(22), pages 1-25, November.
    3. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    4. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & Guerrero, Josep M. & Vasquez, Juan C., 2014. "Microgrids: Experiences, barriers and success factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 659-672.
    5. Kim, H.Y. & Kim, M.K., 2017. "Optimal generation rescheduling for meshed AC/HIS grids with multi-terminal voltage source converter high voltage direct current and battery energy storage system," Energy, Elsevier, vol. 119(C), pages 309-321.
    6. Alvarado-Barrios, Lázaro & Rodríguez del Nozal, Álvaro & Boza Valerino, Juan & García Vera, Ignacio & Martínez-Ramos, Jose L., 2020. "Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage," Renewable Energy, Elsevier, vol. 146(C), pages 2060-2069.
    7. Vatanpour, Mohsen & Sadeghi Yazdankhah, Ahmad, 2018. "The impact of energy storage modeling in coordination with wind farm and thermal units on security and reliability in a stochastic unit commitment," Energy, Elsevier, vol. 162(C), pages 476-490.
    8. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
    9. Rae-Kyun Kim & Mark B. Glick & Keith R. Olson & Yun-Su Kim, 2020. "MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations," Energies, MDPI, vol. 13(8), pages 1-17, April.
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    Cited by:

    1. Yong-Rae Lee & Hyung-Joon Kim & Mun-Kyeom Kim, 2022. "Correction: Lee et al. Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids. Energies 2021, 14 , 470," Energies, MDPI, vol. 15(6), pages 1-2, March.
    2. Wonpoong Lee & Myeongseok Chae & Dongjun Won, 2022. "Optimal Scheduling of Energy Storage System Considering Life-Cycle Degradation Cost Using Reinforcement Learning," Energies, MDPI, vol. 15(8), pages 1-19, April.

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