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Aging Cost Optimization for Planning and Management of Energy Storage Systems

Author

Listed:
  • Saman Korjani

    (Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy)

  • Mario Mureddu

    (Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy)

  • Angelo Facchini

    (IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy)

  • Alfonso Damiano

    (Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Italy, Via Marengo 1, 09123 Cagliari, Italy)

Abstract

In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs. The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.

Suggested Citation

  • Saman Korjani & Mario Mureddu & Angelo Facchini & Alfonso Damiano, 2017. "Aging Cost Optimization for Planning and Management of Energy Storage Systems," Energies, MDPI, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1916-:d:119704
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    References listed on IDEAS

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    1. Angelo Facchini, 2017. "Distributed energy resources: Planning for the future," Nature Energy, Nature, vol. 2(8), pages 1-2, August.
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    Cited by:

    1. Korjani, Saman & Casu, Fabio & Damiano, Alfonso & Pilloni, Virginia & Serpi, Alessandro, 2022. "An online energy management tool for sizing integrated PV-BESS systems for residential prosumers," Applied Energy, Elsevier, vol. 313(C).
    2. Scala, Antonio & Facchini, Angelo & Perna, Umberto & Basosi, Riccardo, 2019. "Portfolio analysis and geographical allocation of renewable sources: A stochastic approach," Energy Policy, Elsevier, vol. 125(C), pages 154-159.
    3. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    4. Nisitha Padmawansa & Kosala Gunawardane & Samaneh Madanian & Amanullah Maung Than Oo, 2023. "Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept," Energies, MDPI, vol. 16(12), pages 1-18, June.

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