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Energy- and Cost-Efficient Pumping Station Control

Timon V. Kanters, Frans A. Oliehoek, Michael Kaisers, Stan R. van den Bosch, Joep Grispen, and Jeroen Hermans. Energy- and Cost-Efficient Pumping Station Control. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 3842–3848, February 2016.

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Abstract

With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.

BibTeX Entry

@inproceedings{Kanters16AAAI,
    author =    {Timon V. Kanters and
                Frans A. Oliehoek and
                Michael Kaisers and
                Stan R. van den Bosch and
                Joep Grispen and
                Jeroen Hermans},
    title =     {Energy- and Cost-Efficient Pumping Station Control},
    booktitle = AAAI16,
    year =      2016,
    month =     feb,
    pages =     {3842--3848},
    abstract = {
    With renewable energy becoming more common, energy prices fluctuate more
    depending on environmental factors such as the weather. Consuming energy
    without taking volatile prices into consideration can not only become
    expensive, but may also increase the peak load, which requires energy
    providers to generate additional energy using less environment-friendly
    methods. In the Netherlands, pumping stations that maintain the water
    levels of polder canals are large energy consumers, but the controller
    software currently used in the industry does not take real-time energy
    availability into account. We investigate if existing AI planning
    techniques have the potential to improve upon the current solutions. In
    particular, we propose a light weight but realistic simulator and
    investigate if an online planning method (UCT) can utilise this simulator
    to improve the cost-efficiency of pumping station control policies. An
    empirical comparison with the current control algorithms indicates that
    substantial cost, and thus peak load, reduction can be attained.
    }
}

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