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A genetic algorithm approach for modelling low voltage network demands

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  • Giasemidis, Georgios
  • Haben, Stephen
  • Lee, Tamsin
  • Singleton, Colin
  • Grindrod, Peter

Abstract

Distribution network operators (DNOs) are increasingly concerned about the impact of low carbon technologies on the low voltage (LV) networks. More advanced metering infrastructures provide numerous opportunities for more accurate load flow analysis of the LV networks. However, such data may not be readily available for DNOs and in any case is likely to be expensive. Modelling tools are required which can provide realistic, yet accurate, load profiles as input for a network modelling tool, without needing access to large amounts of monitored customer data. In this paper we outline some simple methods for accurately modelling a large number of unmonitored residential customers at the LV level. We do this by a process we call buddying, which models unmonitored customers by assigning them load profiles from a limited sample of monitored customers who have smart meters. Hence the presented method requires access to only a relatively small amount of domestic customers’ data. The method is efficiently optimised using a genetic algorithm to minimise a weighted cost function between matching the substation data and the individual mean daily demands. Hence we can show the effectiveness of substation monitoring in LV network modelling. Using real LV network modelling, we show that our methods perform significantly better than a comparative Monte Carlo approach, and provide a description of the peak demand behaviour.

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  • Giasemidis, Georgios & Haben, Stephen & Lee, Tamsin & Singleton, Colin & Grindrod, Peter, 2017. "A genetic algorithm approach for modelling low voltage network demands," Applied Energy, Elsevier, vol. 203(C), pages 463-473.
    • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:463-473
    DOI: 10.1016/j.apenergy.2017.06.057
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    References listed on IDEAS

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    Cited by:

    1. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs & Jiannan Liu, 2024. "Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies," Energies, MDPI, vol. 17(12), pages 1-14, June.
    2. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
    3. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos, 2022. "ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting," Energies, MDPI, vol. 15(10), pages 1-18, May.
    4. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    5. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos & Constantinos Hilas, 2023. "Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network," Energies, MDPI, vol. 16(10), pages 1-20, May.

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