Optimal design of energy conversion units for residential buildings considering German market conditions

Schütz, Thomas; Schraven, Markus Hans; Remy, Sebastian; Granacher, Julia; Kemetmüller, Dominik; Fuchs, Marcus; Müller, Dirk

doi:10.1016/j.energy.2017.08.024

Cited by 18 publications

(11 citation statements)

References 39 publications

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“…In order to validate the new model formulation for many different systems with acceptable computational resources, an easily comprehensible simple Mixed-Integer Linear Program has been selected as the system model. More precise models that consider more detailed the size effects of units and investment costs can be found in Bahl et al [13], Elsido et al [23], Gabrielli et al [24] and Schütz et al [61].…”

Section: Appendix a System Modelingmentioning

confidence: 99%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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The optimization-based design of renewable energy systems is a computationally demanding task because of the high temporal fluctuation of supply and demand time series. In order to reduce these time series, the aggregation of typical operation periods has become common. The problem with this method is that these aggregated typical periods are modeled independently and cannot exchange energy. Therefore, seasonal storage cannot be adequately taken into account, although this will be necessary for energy systems with a high share of renewable generation.To address this issue, this paper proposes a novel mathematical description for storage inventories based on the superposition of inter-period and intra-period states. Interperiod states connect the typical periods and are able to account their sequence. The approach has been adopted for different energy system configurations. The results show that a significant reduction in the computational load can be achieved also for long term storage-based energy system models in comparison to optimization models based on the full annual time series.

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Section: Appendix a System Modelingmentioning

confidence: 99%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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“…Therefore, many different optimization models have been proposed for determining the cost optimal investment decisions and operation of building supply systems: either as Linear Programs (LP) [31,32] with the advantage of good computational tractability but the disadvantage of not being able to account for economies of scale; or as a Mixed Integer Linear Program (MILP) model [33][34][35][36][37][38][39]. Furthermore, two-level approaches that determine at least a part of investment decisions with a meta-heuristic solver and operation with a simulation or optimization are popular [40][41][42][43][44][45].…”

Section: Building Optimizationmentioning

confidence: 99%

“…All models would enable the analysis of the impact of technology adoption and operation on the local infrastructures: for instance, Lindberg et al [37] apply a MILP to design the supply system of a Multi-Family-House (MFH) and analyze the resulting electricity grid load for costoptimal system operation under current German regulations. Schütz et al [39] use a model to evaluate the optimal technology adoption with currently considered incentives and market conditions for the case of three reference buildings.…”

Section: Building Optimizationmentioning

confidence: 99%

Bottom-up energy supply optimization of a national building stock

Kotzur¹,

Markewitz²,

Robinius³

et al. 2020

Energy and Buildings

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The installation and operation distributed energy resources (DER) and the electrification of the heat supply significantly changes the interaction of the residential building stock with the grid infrastructure. Evaluating the mass deployment of DER at the national level would require analyzing millions of individual buildings, entailing significant computational burden.To overcome this, this work proposes a novel bottom-up model that consists of an aggregation algorithm to create a spatially distributed set of typical residential buildings from census data. Each typical building is then optimized with a Mixed-Integer Linear Program to derive its cost optimal technology adoption and operation, determining its changing grid load in future scenarios.The model is validated for Germany, with 200 typical buildings considered to sufficiently represent the diversity of the residential building stock. In a future scenario for 2050, photovoltaic and heat pumps are predicted to be the most economically and ecologically robust supply solutions for the different building types. Nevertheless, their electricity generation and demand temporally do not match, resulting in a doubling of the peak electricity grid load in the rural areas during the winter. The urban areas can compensate this with efficient co-generation units, which are not cost-efficient in the rural areas.

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“…Two of the most interesting and thorough contributions are commented as follows. Schütz et al [53] proposed an optimization model for the design, sizing and operation of energy systems supplying electricity and heat to residential buildings with a precise modelling of many specific German regulations, such as feed-in limitation and remuneration, levies, tax exemptions, subsidies, among others. Pinto et al [54] presented a detailed synthesis model for CCHP systems in residential buildings including PV and wind turbines and used the model to compare different Spanish grid regulations.…”

Section: Legal Constraints In the Synthesis Of Energy Systems For Buildingsmentioning

confidence: 99%