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The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

Author

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  • Matthew Rowe

    (School of Systems Engineering, University of Reading, Whiteknights, Reading,Berkshire RG6 6AH, UK)

  • Timur Yunusov

    (School of Systems Engineering, University of Reading, Whiteknights, Reading,Berkshire RG6 6AH, UK)

  • Stephen Haben

    (Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory, Woodstock Road, Oxford OX2 6GG, UK)

  • William Holderbaum

    (School of Systems Engineering, University of Reading, Whiteknights, Reading,Berkshire RG6 6AH, UK)

  • Ben Potter

    (School of Systems Engineering, University of Reading, Whiteknights, Reading,Berkshire RG6 6AH, UK)

Abstract

Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid’s infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, where the objective is to achieve the greatest peak reduction in demand, for a given storage device specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-point controller and bench marked against a control algorithm with a perfect forecast. A specific case study, using storage on the LV network, is presented, and the results of each algorithm are compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

Suggested Citation

  • Matthew Rowe & Timur Yunusov & Stephen Haben & William Holderbaum & Ben Potter, 2014. "The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction," Energies, MDPI, vol. 7(6), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:3537-3560:d:36668
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    References listed on IDEAS

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    1. Haben, Stephen & Ward, Jonathan & Vukadinovic Greetham, Danica & Singleton, Colin & Grindrod, Peter, 2014. "A new error measure for forecasts of household-level, high resolution electrical energy consumption," International Journal of Forecasting, Elsevier, vol. 30(2), pages 246-256.
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    3. Soon-Ryul Nam & Sang-Hee Kang & Joo-Ho Lee & Seon-Ju Ahn & Joon-Ho Choi, 2013. "Evaluation of the Effects of Nationwide Conservation Voltage Reduction on Peak-Load Shaving Using SOMAS Data," Energies, MDPI, vol. 6(12), pages 1-13, December.
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    Cited by:

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    2. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2018. "Impact of optimised distributed energy resources on local grid constraints," Energy, Elsevier, vol. 142(C), pages 878-895.
    3. 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.
    4. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
    5. Thijs Van der Klauw & Johann L. Hurink & Gerard J. M. Smit, 2016. "Scheduling of Electricity Storage for Peak Shaving with Minimal Device Wear," Energies, MDPI, vol. 9(6), pages 1-19, June.
    6. Peter Horan & Mark B. Luther & Hong Xian Li, 2021. "Guidance on Implementing Renewable Energy Systems in Australian Homes," Energies, MDPI, vol. 14(9), pages 1-24, May.
    7. 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.
    8. Olivier Rebenaque, 2020. "An economic assessment of the residential PV self-consumption support under different network tariffs," Working Papers hal-02511136, HAL.
    9. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    10. Stefano Pietrosanti & William Holderbaum & Victor M. Becerra, 2016. "Optimal Power Management Strategy for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 9(3), pages 1-17, March.
    11. Maximilian J. Zangs & Peter B. E. Adams & Timur Yunusov & William Holderbaum & Ben A. Potter, 2016. "Distributed Energy Storage Control for Dynamic Load Impact Mitigation," Energies, MDPI, vol. 9(8), pages 1-20, August.
    12. Mohamed A. Eltawil & Maged Mohammed & Nayef M. Alqahtani, 2023. "Developing Machine Learning-Based Intelligent Control System for Performance Optimization of Solar PV-Powered Refrigerators," Sustainability, MDPI, vol. 15(8), pages 1-35, April.
    13. 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.
    14. Izaskun Garrido & Aitor J. Garrido & Stefano Coda & Hoang B. Le & Jean Marc Moret, 2016. "Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV)," Energies, MDPI, vol. 9(8), pages 1-14, August.
    15. Feras Alasali & Stephen Haben & Husam Foudeh & William Holderbaum, 2020. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 13(10), pages 1-19, May.
    16. Yunusov, Timur & Frame, Damien & Holderbaum, William & Potter, Ben, 2016. "The impact of location and type on the performance of low-voltage network connected battery energy storage systems," Applied Energy, Elsevier, vol. 165(C), pages 202-213.
    17. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    18. Olivier Rebenaque, 2020. "An economic assessment of the residential PV self-consumption support under different network tariffs," Working Papers 2001, Chaire Economie du climat.
    19. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.

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