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A Receding Predictive Horizon Approach to the Periodic Optimization of Community Batery Energy Storage Systems

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Open access
Authors
Wolfs, Peter
Reddy, S.
Date
2012
Type
Conference Paper

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Wolfs, P. and Reddy, S. 2012. A Receding Predictive Horizon Approach to the Periodic Optimization of Community Batery Energy Storage Systems, in 22nd Australasian Universities Power Engineering Conference 2012 (AUPEC), Sep 26-29 2012. Bali, Indonesia: IEEE.
Source Title
Proceedings of the 22nd Australasian Universities Power Engineering Conference
Source Conference
22nd Australasian Universities Power Engineering Conference
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Department of Electrical and Computer Engineering
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Abstract

Community scale battery energy storage systems can improve the utilization of network assets and increase the uptake of intermittent renewable energy sources. This paper presents an efficient algorithm for optimizing the cyclic diurnal operation of battery storages in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble a 24 hour load profile. A diurnal charge profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing future forecasts in load and PV generation.