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

Yuqing Yang, Stephen Bremner, Chris Menictas and Merlinde Kay
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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

Energies, 2019, vol. 12, issue 12, 1-25

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.

Keywords: battery energy storage system; hybrid PV and wind power plant; receding horizon control; Elman neural network; wavelet neural network; autoregressive integrated moving average (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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