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Smart storage scheduling and forecasting for peak reduction on low-voltage feeders

Yunusov, T., Giasemidis, G. and Haben, S. (2018) Smart storage scheduling and forecasting for peak reduction on low-voltage feeders. In: Energy Management—Collective and Computational Intelligence with Theory and Applications. Studies in Systems, Decision and Control, 149. Springer, pp. 83-107. ISBN 9783319756905

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To link to this item DOI: 10.1007/978-3-319-75690-5_5

Abstract/Summary

The transition to a low carbon economy will likely bring new challenges to the distribution networks, which could face increased demands due to low-carbon technologies and new behavioural trends. A traditional solution to increased demand is network reinforcement through asset replacement, but this could be costly and disruptive. Smart algorithms combined with modern technologies can lead to inexpensive alternatives. In particular, battery storage devices with smart control algorithms can assist in load peak reduction. The control algorithms aim to schedule the battery to charge at times of low demand and discharge, feeding the network, at times of high load. This study analyses two scheduling algorithms, model predictive control (MPC) and fixed day-ahead scheduler (FDS), comparing against a set-point control (SPC) benchmark. The forecasts presented here cover a wide range of techniques, from traditional linear regression forecasts to machine learning methods. The results demonstrate that the forecasting and control methods need to be selected for each feeder taking into account the demand characteristics, whilst MPC tend to outperform the FDS on feeders with higher daily demand. This chapter contributes in two main directions: (i) several forecasting methods are considered and compared and (ii) new energy storage control algorithm, MPC with half-hourly updated (rolling) forecasts designed for low voltage network application, is introduced, analysed and compared.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of the Built Environment > Construction Management and Engineering > Transition Pathways to a Low-Carbon Economy
Interdisciplinary centres and themes > Centre for Technologies for Sustainable Built Environments (TSBE)
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Centre for the Mathematics of Human Behaviour (CMOHB)
Interdisciplinary centres and themes > Energy Research
ID Code:76200
Publisher:Springer

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