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Article . 2019 . Peer-reviewed
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Energy for Sustainable Development
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Preprint . 2019
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How much energy can optimal control of domestic water heating save?

Authors: Cloete, Andrew; Apperley, Mark; Engelbrecht, J.A.A.; Booysen, MJ; Ritchie, Michael;

How much energy can optimal control of domestic water heating save?

Abstract

Scheduled control of domestic electric water heaters, designed to cut energy use while minimising the impact on users' comfort and convenience, has been fairly common for some time in a number of countries. The aim is usually load-shifting (by heating water at off-peak times) and/or maximising time-of-use pricing benefits for users. The scheduling tends not to be linked to actual hot water usage and depends largely on stored thermal energy. Heat losses therefore tend to be greater than if the heater ran without a break. The effect of such a control strategy is thus to worsen the energy loss and in most cases increase greenhouse gas emissions. Many developing countries have flat-pricing (no time-of-use incentives) and rely heavily on energy from fossil fuels, making these considerations even more pressing. We explore three strategies for optimal control of domestic water heating that do not use thermostat control: matching the delivery temperature in the hot water, matching the energy delivered in the hot water, and a variation of the second strategy which provides for Legionella sterilisation. For each of these strategies we examine the energy used in heating, the energy delivered at the tank outlet, and issues of convenience to the user. The study differs from most previous work in that it uses real daily hot-water usage profiles, ensures like-for-like comparison in delivered energy at the point of use, and includes a daily Legionella avoidance strategy. We tackled this as an optimal control problem using dynamic programming. Our results demonstrate a median energy saving of between 8\ and 18% for the three strategies. Even more savings would be realised if intended and unintended usage events are correctly classified, and the optimal control only plans for intended usage events.

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bepress|Engineering|Electrical and Computer Engineering|Signal Processing, engrXiv|Engineering|Electrical and Computer Engineering|Signal Processing, engrXiv|Engineering, bepress|Engineering, bepress|Engineering|Electrical and Computer Engineering, bepress|Engineering|Electrical and Computer Engineering|Power and Energy, engrXiv|Engineering|Electrical and Computer Engineering, engrXiv|Engineering|Electrical and Computer Engineering|Power and Energy

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Top 10%
Top 10%
Top 10%
hybrid