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A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks

Author

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  • Ma, Yiju
  • Azuatalam, Donald
  • Power, Thomas
  • Chapman, Archie C.
  • Verbič, Gregor

Abstract

Battery storage, particularly residential battery storage coupled with rooftop photovoltaics (PV), is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management (HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.

Suggested Citation

  • Ma, Yiju & Azuatalam, Donald & Power, Thomas & Chapman, Archie C. & Verbič, Gregor, 2019. "A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s030626191931356x
    DOI: 10.1016/j.apenergy.2019.113669
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    References listed on IDEAS

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    1. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2015. "Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks," Applied Energy, Elsevier, vol. 146(C), pages 122-134.
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    Citations

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    Cited by:

    1. A.S. Jameel Hassan & Umar Marikkar & G.W. Kasun Prabhath & Aranee Balachandran & W.G. Chaminda Bandara & Parakrama B. Ekanayake & Roshan I. Godaliyadda & Janaka B. Ekanayake, 2021. "A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration," Energies, MDPI, vol. 14(20), pages 1-24, October.
    2. Yiju Ma & Kevin Swandi & Archie Chapman & Gregor Verbic, 2019. "Multi-Stage Compound Real Options Valuation in Residential PV-Battery Investment," Papers 1910.09132, arXiv.org.
    3. Ma, Yiju & Swandi, Kevin & Chapman, Archie C. & Verbič, Gregor, 2020. "Multi-stage compound real options valuation in residential PV-Battery investment," Energy, Elsevier, vol. 191(C).
    4. Ma, Yiju & Chapman, Archie C. & Verbič, Gregor, 2022. "Valuation of compound real options for co-investment in residential battery systems," Applied Energy, Elsevier, vol. 318(C).
    5. Guerrero, Jaysson & Gebbran, Daniel & Mhanna, Sleiman & Chapman, Archie C. & Verbič, Gregor, 2020. "Towards a transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    6. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    7. Ferreira, Willian M. & Meneghini, Ivan R. & Brandao, Danilo I. & Guimarães, Frederico G., 2020. "Preference cone based multi-objective evolutionary algorithm applied to optimal management of distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 274(C).

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