An optimality assessment methodology for home energy management system approaches based on uncertainty analysis
Aachen / E.ON Energy Research Center, RWTH Aachen University (2018) [Book, Dissertation / PhD Thesis]
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The emergence of ICT based devices enables houses and buildings to become an active part of their environment, by shifting their thermal or electrical consumption/production at different time period according to the grid requirements. Considering the rise of interests for time-dependent electricity tariffs, Home Energy Management System (HEMS), a computer-aided tool with communication ability, is a promising tool for helping prosumers to optimize their device operation accordingly to their comfort and the given electricity price. This dissertation first delivers an overview on the different HEMS approaches, their typical objective functions, their formulations and the considered flexible devices in the literature. This literature review highlights the various HEMS forms and the difficulty to compare them because of the specificity of their evaluation conditions. For this purpose, this dissertation presents an assessment methodology which considers the HEMS evaluation conditions, typically time-series, as uncertain parameters. An uncertainty analysis method for uncertain time-series is developed for fast uncertainty analysis according to stochastic optimization theory. It is shown that for a HEMS approach, the results of 10 000 Monte Carlo simulations can be achieved by 3 simulation runs per uncertain parameters with an appropriated selected set of representative scenarios. Finally, this assessment method is used for comparing and quantifying the saving potential of two different HEMS: an optimization and a market-based control, which both are compared to a conventional control, taken as reference case. The specific saving potential associated to each flexible devices is also studied as well as the sensitivity of these results to a forecast error. All the presented results take into account different user profiles for electrical and domestic hot water demand and consider 5 years of historical data for the temperature and the irradiation in Germany.