Browsing by Author "Richie, Michael"

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    Usage-based optimal energy control of residential water heaters
    (Stellenbosch : Stellenbosch University, 2021-12) Richie, Michael; Booysen, Thinus; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Water heating contributes up to 40% of a household’s total electricity usage and places a substantial burden on the electricity grid due to high power ratings and users’ largely simultaneous hot water usage. It is estimated that South Africa uses 38.5 GWh/day on water heating. Tanked water heaters can store thermal energy for long periods of time with a portion lost to the environment. Although many demand response strategies have been proposed to mitigate costs from time-of-use pricing of electricity, many developing countries have flat-rate pricing and are heavily reliant on the burning of fossil fuels for electricity. This thesis explores optimisation to develop a system that provides electric water heaters (EWHs) with the optimal heating schedule to minimise overall electrical energy usage without compromising the comfort of the customer. The efficacy of energy management techniques that model water heaters and the accuracy of their simulation results rely on representative hot water usage profiles. A probabilistic data-driven model for modelling individualised hot water profiles and an accompanying hot water usage simulator is proposed in this thesis. The model is trained and evaluated using high quality data gathered from 77 residential households which is first refined by a data cleaning process. A hot water demand predictor is also developed on top of the probabilistic model to predict future hot water profiles that can be used for optimisation. A novel dynamic programming (DP) approach is presented to achieve optimal control of a single node EWH. To accommodate the natural stratification that occurs, a novel A* search algorithm approach is also presented to achieve optimal control of an EWH with stratification. The DP and A* approaches produce the optimal plan for hot water usage profiles with perfect foreknowledge of water drawn from the EWH. The A* approach is further utilised to produce an optimal plan for the hot water usage profile when water drawn is predicted. All three conditions are tested with a simulator that is equipped with a novel temperature feedback controller to minimise the effects of mispredictions and model inaccuracies between the optimal planning and the execution, and a reactive water mixer to simulate the user behaviour. Three strategies are explored for optimal control of domestic water heating that do not depend on the default thermostat control, namely: 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 to determine how much energy an EWH can save. It was concluded that the most energy savings is achieved when the energy matching strategy is used, where 21.9 % is saved when water usages are perfectly known and 9.6 % is saved when they are predicted.