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000858675 005__ 20240711101524.0
000858675 0247_ $$2Handle$$a2128/21115
000858675 0247_ $$2URN$$aurn:nbn:de:0001-2019020614
000858675 0247_ $$2ISSN$$a1866-1793
000858675 020__ $$a978-3-95806-370-9
000858675 037__ $$aFZJ-2018-07520
000858675 1001_ $$0P:(DE-Juel1)168451$$aKotzur, Leander$$b0$$eCorresponding author$$gmale$$ufzj
000858675 245__ $$aFuture Grid Load of the Residential Building Sector$$f- 2018-11-15
000858675 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2018
000858675 300__ $$axxi, 213 S.
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000858675 4900_ $$aSchriften des Forschungszentrums Jülich Reihe Energie & Umwelt / Energy & Environment$$v442
000858675 502__ $$aRWTH Aachen, Diss., 2018$$bDr.$$cRWTH Aachen$$d2018
000858675 520__ $$aThe installation and operation of distributed energy resources in the form of photovoltaics, co-generation units, or batteries, and the electrification of the heat supply are seen as promising options to reduce greenhouse gas emissions of residential buildings. Nevertheless, their uptake significantly changes the interaction of the residential building stock with the electricity grid and the centralized supply infrastructure and questions their current design. Therefore, the objective of this work is to derive the future residential electricity grid load spatially and temporally resolved to define a decision basis for future grid and market designs. In order to generally predict the future structure, design and operation of residential supply systems and efficiency measures, a Mixed-Integer Linear Program is introduced that minimizes the total annual energy supply cost of a single buildings since the technology adoption is mainly economically driven. The minimization of the greenhouse gas footprint can be added as second objective. The optimization model accounts for the temporal occupant activities, their related device usage, tolerated room temperature levels, limited roof capacities, or different levels of additional insulation. Since the variety of investment and operation options make the model computationally challenging, clustering based times series aggregation techniques are developed and introduced to reduce the complexity of the model. A novel aggregation algorithm based on Mixed-Integer Quadratic Programs is introduced to scale the technology adoption and operation from the single building perspective to a nationwide scope by creating a spatially resolved archetype building stock from Census data and building databases. 200 archetype buildings are concluded to sufficiently represent the diversity of building types in the different municipalities in Germany. These archetype buildings are optimized for the weather years 2010 until 2015 and the results are validated to residential energy consumption value from public statistics, whereby the regional demand impact of different weather years is illustrated. Afterwards, a scenario frame for 2050 is defined and the buildings are optimized to reach a carbon neutral building stock with minimal cost. As result, at least 130 GWof photovoltaic are deployed and above 90 TWh/a of the generated electricity are used for self-consumption in the residential buildings. Nevertheless, the total demand for electricity significantly increases since 17 to 26 GWel of heat pumps are installed to replace combustion boilers, while only 30 % of space heat are saved by refurbishment measures. The spatially resolved archetype building stock allows new insights: The urban areas can compensate the increasing electricity demand by efficient co-generation units, e.g. in form of fuel cells. Nevertheless, those are not cost efficient in the rural areas where the photovoltaic generation and the heatpump demand temporally disjoin, resulting in a doubling of the peak electricity loadin the winter hours.
000858675 536__ $$0G:(DE-HGF)POF3-134$$a134 - Electrolysis and Hydrogen (POF3-134)$$cPOF3-134$$fPOF III$$x0
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