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

Matthew Rowe, Timur Yunusov, Stephen Haben, William Holderbaum and Ben Potter
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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

Energies, 2014, vol. 7, issue 6, 1-24

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.

Keywords: DNO; storage; control; stochastic optimisation; model predictive control; receding horizon; forecast (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: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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