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Water heating load forms a significant share of the total domestic demand.
Figure 18 The OCP optimization of a water heating load profile with the dominant evening peak.
In this case study, we evaluate the tool's ability to optimize switching programs for two different water heating load profiles.
Figure 15 shows two aggregate uncontrolled water heating load curves obtained using variable and constant values for ambient and cold water temperatures.
An operator uses this tool to determine the available domestic water heating load in a controlled area, and predict the potential reduction in peak load.
A power system operator can use this tool to determine the available domestic water heating load in a controlled area, and predict the potential reduction in peak load.
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Both use the default switching program configuration to produce optimized switching programs that are applied to the same set of water heating loads.
The tool estimates the available domestic water heating loads in a controlled area, determines optimal switching programs and presents results to the user in a graphical form.
The hot water cylinder model then calculates controlled water heating loads (I/O 5) by applying the user-selected switching program (I/O 4) to the hot water consumption profiles (I/O 2).
A Markovian jump-driven model of individual electric water heating loads is employed where the mean field effect is mediated through the quadratic cost function parameters under the form of an integral error.
This paper describes a rooftop wind solar hybrid heat pump system for building hot water, heating and cooling loads and presents energy and exergy analyses as well as an environmental benefit assessment.
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