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A New Hybrid Approach Using the Simultaneous Perturbation Stochastic Approximation Method for the Optimal Allocation of Electrical Energy Storage Systems

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  • Guido Carpinelli

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Napoli, Italy)

  • Fabio Mottola

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Napoli, Italy)

  • Christian Noce

    (ENEL Global Infrastructure and Networks Srl, Italy)

  • Angela Russo

    (Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, Italy)

  • Pietro Varilone

    (Dipartimento di Ingegneria Elettrica e dell′Informazione “Maurizio Scarano”, Università di Cassino e del Lazio Meriodionale, 03043 Cassino, Italy)

Abstract

This paper deals with the optimal allocation (siting and sizing) of distributed electrical energy storage systems in unbalanced electrical distribution systems. This problem is formulated as a mixed, non-linear, constrained minimization problem, in which the objective function involves economic factors and constraints address the technical limitations of both network and distributed resources. The problem is cumbersome from the computational point of view due to the presence of both constraints of an intertemporal nature and a great number of state variables. In order to guarantee reasonable accuracy-although limiting the computational efforts-a new approach is proposed in this paper: it is based on a Simultaneous Perturbation Stochastic Approximation (SPSA) method and on an innovative inner algorithm, which allows it to quickly carry out the daily scheduling (charging/discharging) of the electrical energy storage systems. The proposed method is applied to a medium voltage (Institute of Electrical and Electronics Engineers) IEEE unbalanced test network, to demonstrate the effectiveness of the procedure in terms of computational effort while preserving the accuracy of the solution. The obtained results are also compared with the results of a Genetic Algorithm and of an exhaustive procedure.

Suggested Citation

  • Guido Carpinelli & Fabio Mottola & Christian Noce & Angela Russo & Pietro Varilone, 2018. "A New Hybrid Approach Using the Simultaneous Perturbation Stochastic Approximation Method for the Optimal Allocation of Electrical Energy Storage Systems," Energies, MDPI, vol. 11(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1505-:d:151524
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    References listed on IDEAS

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    1. Motalleb, Mahdi & Reihani, Ehsan & Ghorbani, Reza, 2016. "Optimal placement and sizing of the storage supporting transmission and distribution networks," Renewable Energy, Elsevier, vol. 94(C), pages 651-659.
    2. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    3. Saboori, Hedayat & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2015. "Reliability improvement in radial electrical distribution network by optimal planning of energy storage systems," Energy, Elsevier, vol. 93(P2), pages 2299-2312.
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    Cited by:

    1. Dong Zhang & GM Shafiullah & Choton Kanti Das & Kok Wai Wong, 2023. "Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks," Energies, MDPI, vol. 16(20), pages 1-35, October.
    2. Pairach Kitworawut & Nipon Ketjoy & Tawat Suriwong & Malinee Kaewpanha, 2023. "Best Practice in Battery Energy Storage for Photovoltaic Systems in Low Voltage Distribution Network: A Case Study of Thailand Provincial Electricity Authority Network," Energies, MDPI, vol. 16(5), pages 1-23, March.

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