Marcus Fuchs’s research while affiliated with RWTH Aachen University and other places

City districts have a large potential to reduce greenhouse gas emissions by usage of energy efficiency measures. Urban energy models (UEM) can be useful to analyze the impact of different energy efficiency actions on city districts. While simulation of demand data with high spatial and temporal resolution is often necessary to evaluate retrofit measures, the city's complex structure and lack of data often prevents a reliable application of such methods. This paper presents an urban energy modeling approach based on open-source geographical information system (GIS) datasets to reduce input data uncertainty and simplify city district modeling. We present a method to automatically extract basic city district data from OpenStreetMap (OSM) and enrich these datasets based on national building stock statistics. Building models with representative geometries and physical properties are automatically generated based on building archetype information. These models enable thermal simulation on urban scale. The approach is demonstrated for a use case in Germany, where a reference city district model has been generated with OSM data extraction and enrichment. The reference city district model has been used to perform a space heating net energy demand uncertainty analysis. The demand values simulated with the reference model show a sufficient fit with measured consumption values. The approach provides a fast and structured methodology to model city districts and simulate space heating energy demand on urban scale.

A well-known problem in the dynamic simulation of buildings energy consumption are the discrepancies between the simulated and measured data, which call for calibration techniques to obtain more accurate and reliable building models. The most recognized calibration techniques use statistical indices to assess and improve the quality of simulation models. While there are already well known statistical indices available to evaluate the simulation outputs, the combination of indices offers potential for further improvements in this field. To assess the procedure of calibrating building simulation models, we present a ranking of six statistical indices and their combinations (63 statistical metrics), produced by an automated evaluation procedure, in the specific case of calibrating to annual heat demand curves. The developed evaluation procedure is also able to account for eventual deterioration of other statistical metrics, which are not tuned during the calibration. We apply the new method in dynamic, hourly simulations to a use case with 200 buildings, for which extensive measurement data are available. Based on the generated ranking, we recommend using combinations of four statistical indices: the Coefficient of Variation of Root Mean Square Error (CV(RMSE)), the Normalized Mean Error (NME), the standardized contingency coefficient and the coefficient of determination (R^2). In our use case, these combinations lead to better results than the commonly used indices CV(RMSE) and Normalized Mean Bias Error (NMBE). In addition, we could show that it is beneficial to use another index for evaluation than for calibration, because it detects eventual deterioration of the simulation output results.

The design of decentralized energy conversion systems in smart residential microgrids is a challenging optimization problem due to the variety of available generation and storage devices. Common measures to reduce the problem's size and complexity are to reduce modeling accuracy, aggregate multiple loads or change the temporal resolution. However, since these attempts alter the optimization problem and consequently lead to different solutions as intended, this paper presents and analyses a decomposition method for solving the original problem iteratively. The decomposed method is verified by comparison with the original compact model formulation, proving that both models deviate by less than 1.8%. Both approaches furthermore lead to similar energy systems that are operated similarly, as well. The findings also show that the compact model formulation is only applicable to small- and medium-scale microgrids due to current limitations of computing resources and optimization algorithms, whereas the distributed approach is suitable for even large-scale microgrids. We apply the decomposed method to a large-scale microgrid in order to evaluate economic and ecological benefits of interconnected buildings inside the grid. The results show that with local electricity exchange, costs can be reduced by 4.0% and emissions by even 23.7% for the investigated scenario.

This paper presents a framework for the optimal operation of data centers, leveraging their Heating, ventilation, and air conditioning unit, delay-tolerant IT workload and battery storage system for participating in demand response programs. In this context, an MPC-based control framework has been developed that guarantees the reliable operation of the data center core activities. We derive a modeling approach to represent the dynamics of the data center subsystems and validate it for a data center test-bed via practical experiments. Hereby, the thermal subsystem leads to deviations of less than 0.60 K in the modeled outlet temperature. The validated model is used for incremental prototyping of the proposed control via simulations under uncertainties. The results demonstrate a mean absolute error of the relative deviations between the data center consumption and the target load profile of 2.71% for an incentive-based scenario and a cost reduction of 3.86% for a price-based scenario. IEEE

Bidirectional low temperature networks are a novel concept that promises more efficient heating and cooling of buildings. Early research shows theoretical benefits in terms of exergy efficiency over other technologies. Pilot projects indicate that the concept delivers good performance if heating and cooling demands are diverse. However, the operation of these networks is not yet optimized and there is no quantification of the benefits over other technologies in various scenarios. Moreover, there is a lack of understanding of how to integrate and control multiple distributed heat and cold sources in such networks. Therefore, this paper develops a control concept based on a temperature set point optimization and agent-based control which allows the modular integration of an arbitrary number of sources and consumers. Afterwards, the concept is applied to two scenarios representing neighborhoods in San Francisco and Cologne with different heating and cooling demands and boundary conditions. The performance of the system is then compared to other state-of-the-art heating and cooling solutions using dynamic simulations with Modelica. The results show that bidirectional low temperature networks without optimization produce 26% less emissions in the San Francisco scenario and 63% in the Cologne scenario in comparison to the other heating and cooling solutions. Savings of energy costs are 46% and 27%, and reductions of primary energy consumption 52% and 72%, respectively. The presented operation optimization leads to electricity use reductions of 13% and 41% when compared to networks with free-floating temperature control and the results indicate further potential for improvement. The study demonstrates the advantage of low temperature networks in different situations and introduces a control concept that is extendable for real implementation.

Many countries have passed governmental action plans to support the installation of renewable energy sources. However, most studies dealing with the optimization of building energy systems neglect a precise modeling of such subsidies, although these subsidies are specifically designed to strongly influence system setups. Therefore, this paper extends a model for the optimization of energy systems by a more accurate consideration of storage units and enhance both models by accounting for major German pieces of legislation aimed at supporting renewable energies. Additionally, we consider typical German market characteristics, in particular the availability of multiple gas and electricity tariffs. We compare our model with the original formulation regarding a pure cost minimization and a forced reduction of CO2 emissions for three new buildings located in Germany. The results imply that the considered subsidies strongly support the installation of PV modules and CHP units. Without these subsidies, batteries and solar thermal collectors become more important. Additionally, the findings illustrate that the new storage model is slightly more accurate, but only marginally affects the total annual costs and required computing times. The conducted sensitivity analysis has shown that the obtained results are relatively robust to variations in energy tariff costs and demands.

Cities are responsible for more than 70 % of global greenhouse gas emissions. Thus, cities can play a major part within the CO 2 emission reduction goals of the Paris agreement. Lack of technical knowledge and solutions has often been seen as major challenge for energy efficiency implementation. However, findings of the International Energy Agency (IEA) Annex 51 – Case Studies & Guidelines for Energy Efficient Communities – showed that the primary challenges result from inefficient organizational processes and unsupportive framework for implementation. Thus, solutions have to be found how the energy and urban planning can act more efficiently to successfully support the implementation of energy strategies within urban areas. Within the IEA Energy in Buildings and Communities (EBC) Program, the Annex 63 – Implementation of Energy Strategies in Communities – aims at giving recommendations for an optimized energy and urban planning process to support decision makers as well as planners. Therefore, existing legal frameworks, processes and case studies within energy planning in communities were analysed. This paper shows first results of the Annex 63 to serve as orientation for decision makers and other interested persons in the field of urban energy planning.

The energy supply of buildings in urban contexts is undergoing significant changes. The increase of renewable sources for electrical and thermal energy generation will require flexible and secure supply systems. To reflect and consider these changes in energy systems and buildings, dynamic simulation is one key element. Sparse and limited access to detailed building information as well as computing time are challenges for building simulation on urban-scale. In addition, data acquisition and modelling for building performance simulation (BPS) are time-consuming and error-prone. To enable the use of BPS on urban-scale, this paper presents TEASER, an open framework for urban energy modelling of building stocks (open-source at https://github.com/RWTH-EBC/TEASER). TEASER provides an interface for multiple data sources, data enrichment and export of ready-to-run Modelica simulation models. The paper presents TEASER's methodology and package structure. Three use cases show TEASER's capabilities on the building, neighbourhood and urban scales. © 2017 International Building Performance Simulation Association (IBPSA)

Modeling and optimization of mixed use areas. This article deals with the development of a planning tool for mixed use areas within the project ”En-Eff:Stadt – Bottrop, Welheimer Mark“. The city district Welheimer Mark is a mixed use area within the InnovationCity Ruhr of Bottrop, Germany. The main aim was the energetic optimization of the city district to reduce greenhouse gas emissions. Thus, methods for complex city district modeling has been developed and used within Welheimer Mark district. Simulated thermal and electrical demands only showed a difference of 6.4 % related to energy consumption values. While a sufficient method for generation of electrical load profiles of non-residential buildings could not be identified, a method for generation of thermal load profiles shows a good fit between generated and measured loads. An optimization model has been used to identify an optimized energy system distribution. A greenhouse gas emission reduction up to 50 % is possible at cost increase of 6 % to 11 %. Copyright © 2017 Ernst & Sohn Verlag für Architektur und technische Wissenschaften GmbH & Co. KG, Berlin http://onlinelibrary.wiley.com/doi/10.1002/bapi.201710001/epdf