Abstract
The aim of this paper is to develop an optimal energy management strategy for electric water heaters (EWHs), based on operational data such as photovoltaic (PV) production, ambient temperature (AT), water demand (WD), and fixed appliance consumption (FAC), electricity tariffs and user preferences. The proposed strategy uses a multi-objective Tabu Search (TS) algorithm to determine the optimal domestic hot water output over the next 24 hours based on this prior data. The aim is to minimize the electricity bill while maintaining a desired level of comfort, by keeping the water temperature within a range compatible with the user’s thermal comfort. In addition, the temperature setpoint was varied according to operating conditions. A comparison with particle swarm optimization (PSO)-based management, which uses a similar strategy but employs the PSO algorithm for optimization, reveals that the proposed strategy achieves a significant 33.2% reduction in electricity costs and a 3.8% reduction in carbon emissions.
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