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A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques

Author

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  • Yuqing Yang

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney 2052, New South Wales, Australia)

  • Stephen Bremner

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney 2052, New South Wales, Australia)

  • Chris Menictas

    (School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney 2052, New South Wales, Australia)

  • Merlinde Kay

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney 2052, New South Wales, Australia)

Abstract

This paper presents a mixed receding horizon control (RHC) strategy for the optimal scheduling of a battery energy storage system (BESS) in a hybrid PV and wind power plant while satisfying multiple operational constraints. The overall optimisation problem was reformulated as a mixed-integer linear programming (MILP) problem, aimed at minimising the total operating cost of the entire system. The cost function of this MILP is composed of the profits of selling electricity, the cost of purchasing ancillary services for undersupply and oversupply, and the operation and maintenance cost of each component. To investigate the impacts of day-ahead and hour-ahead forecasting for battery optimisation, four forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA), were applied for both day-ahead and hour-ahead forecasting. Numerical simulations demonstrated the significant increased efficiency of the proposed mixed RHC strategy, which improved the total operation profit by almost 29% in one year, in contrast to the day-ahead RHC strategy. Moreover, the simulation results also verified the significance of using more accurate forecasting techniques, where ARIMA can reduce the total operation cost by almost 5% during the whole year operation when compared to the persistence method as the benchmark.

Suggested Citation

  • Yuqing Yang & Stephen Bremner & Chris Menictas & Merlinde Kay, 2019. "A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques," Energies, MDPI, vol. 12(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2326-:d:240737
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    References listed on IDEAS

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    Cited by:

    1. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    2. João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    3. Robert Małkowski & Marcin Jaskólski & Wojciech Pawlicki, 2020. "Operation of the Hybrid Photovoltaic-Battery System on the Electricity Market—Simulation, Real-Time Tests and Cost Analysis," Energies, MDPI, vol. 13(6), pages 1-21, March.
    4. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Prasad, Abhnil Amtesh & Yang, Yuqing & Kay, Merlinde & Menictas, Chris & Bremner, Stephen, 2021. "Synergy of solar photovoltaics-wind-battery systems in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    6. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    7. Bechlenberg, Alva & Luning, Egbert A. & Saltık, M. Bahadır & Szirbik, Nick B. & Jayawardhana, Bayu & Vakis, Antonis I., 2024. "Renewable energy system sizing with power generation and storage functions accounting for its optimized activity on multiple electricity markets," Applied Energy, Elsevier, vol. 360(C).

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