Elsevier

Energy and Buildings

Volume 163, 15 March 2018, Pages 79-91
Energy and Buildings

New 3D model based urban energy simulation for climate protection concepts

https://doi.org/10.1016/j.enbuild.2017.12.019Get rights and content

Abstract

Climate protection concepts for cities and regions are designed to establish CO2 emission baselines and develop measures for climate change mitigation. Up to now such concepts were based on aggregated consumption and emission data and only qualitative estimations of the effect of measures were possible. To better quantify the impact of mitigation measures, a large amount of data on the building stock is needed. Very powerful analysis possibilities for an energetic and economic evaluation of scenarios arise, if continuously growing data stock organized in geographical information systems are combined with simulation models of buildings and energy systems. In this work, 3D data models in CityGML format of the entire building stock of Ludwigsburg, a German county with 34 municipalities, were used and enriched with building’s year of construction and its function to allow an automatized quantifying the climate protection indicators. In this regard, the heating demand of each individual building in the region in the current state and after two refurbishment scenarios are calculated. In addition, the local solar photovoltaic potential is determined, as the exact size and orientation of each building surface is in the 3D model available. Besides, some new methodologies are described to better quantify the costs and benefits of CO2 mitigation strategies on a local or regional level and to support decision making.

Introduction

Many cities and regions are committed to develop climate protection concepts and have set ambitious CO2 reduction targets. In many international urban networks information and best practice examples are exchanged, such as the Cities for Climate Protection Program (CCP) established in 1993 or the C40 network founded in 2005. Local authorities are crucial actors in climate change mitigation, as they can regulate, advise, and facilitate action by local communities and stakeholders, and have considerable experience in addressing environmental impacts within the fields of energy management, transport, and planning [1]. A precondition for action is a model for local energy use and greenhouse gases, because without detailed information on urban energy flows strategy development and management of measures is not possible [2].

The determination of the baseline energy consumption and CO2 emissions in climate protection concepts is today mainly based on aggregated statistics or average values. Whereas, in a bottom up approach specific values related to the building space and the building footprint as a function of buildings’ type and age are used to determine the status quo of consumption [[3], [4], [5]]. Such approaches have the advantage to include occupant behaviour and macroeconomic and socioeconomic effects in the consumption, but its reliance on historical consumption information does not necessarily allow projections for the future.

It is a major challenge to quantify and predict the urban energy demand [6]. The two main modeling strategies for building energy consumption are top-down and bottom-up methods [[7], [8]]. Depending on the input data and structure, bottom-up methods apply either statistical or physical models [9]. The former has some limitations, like the need of a large sample group, and not specifying the impact of the energy conservation measures, which is important for urban energy strategies. Modeling the thermal energy consumption with a physical approach is based on algorithms like quasi-steady state or dynamic hourly models using geometrical and semantic data [10].

Urban energy modeling is computationally intensive, due to the increasing level of detail and amount of building data, such as construction and usage data, attached. Either the processing power can be improved by implementing cloud-based or parallel computing solutions [11] or the urban models can be simplified [12]. A range of simplified tools are available with different scope of application and functionalities [13].

The use of GIS is very useful for integration and structuring the large urban data set [[14], [15]]. Virtual 3D city models generated from airborne laser scanning or photogrammetry technologies can provide an excellent dataset for bottom-up physical modelling, storing geometrical and semantic data of entire cities [16]. Based on such city models, several urban heat demand analyses have been recently carried out in some European cities like Berlin [[17], [18]], Karlsruhe and Ludwigsburg [19], Trento and Ferrara [20].

Modelling a growing number of cities, regions and even countries (Germany), virtual 3D city models represent a powerful support for public authorities and engineering companies to tackle the urgently required energy transition. Among the 3D city model formats, the open standard CityGML stands out as the reference, providing an excellent and flexible spatial-semantic data structure for 3D geospatial visualization, multi-domain analysis and exploration [[21], [22]]. The CityGML data model is the basis of the new urban energy simulation platform SimStadt developed at the University of Applied Sciences Stuttgart during the last years. This platform aims to support urban planners and city managers with defining and coordinating low-carbon energy strategies for their cities, with a variety of multi-scale energy analyses. The platform integrates simulation algorithms and makes it possible to test the effects of various data sources [23].

The input data quality is crucial for the accuracy of the results and several European directives and projects dealing with urban data management are working on this issue, like INSPIRE [24], 2007 or SUNSHINE [25]. Investigating the impact of the different input variables on the result accuracy enables the identification of the most influential input data and the analysis of data uncertainty [[6], [26]]. As a result, intelligent and adequate data collecting strategies can be designed, assigning resources to the most important parameters, while parameters with minor influence can be assessed with coherent benchmarking values.

A study of Nouvel et al. [27] investigates the influences of data quality on the urban energy platform used in this study. According to this study the most affecting data is the year of construction as well as refurbishment of the building, base on which the thermal properties i.e. the ratio of the heat transmission of the building is determined. Applying a high quality of such a data for the heat demand simulations can results in a more accurate calculation of the contributions of renewable energies to cover this demand. This requires the simulation of the energy provided by solar energy conversion system and to relate this produced energy to the demand of each building in question. This is particularly important when solar thermal energy use, which is currently only directly used by each building and usually not fed into heat distribution grid, is considered. But also for photovoltaic electricity generation it is increasingly important to determine the level of self-consumption in each building, as this determines the economics of the system operation.

The scope of this paper is to employ an automated urban energy platform on a case study in Ludwigsburg, a German county with 34 municipalities, for developing a measures and initiatives for a climate protection concept in this city. Firstly, the automated method of heat demand calculation in this platform is explained. Then the case study is explained and the location-specific method of electricity demand and CO2-emission are described. The electricity demand for Ludwigsburg is extracted from concession bills, where available and otherwise is evaluated using typical statistical consumption data based on net floor area and building’s usage. In the next section, the results of the platform for the heat demand of Ludwigsburg are presented. The heat demand is simulated from geometry and building construction information for each of almost 177,000 residential and non-residential buildings and then building efficiency scenarios with various insulation standards were analyzed. Finally, two examples of measures or initiatives based on the platform results are explained, and it is shown how a climate protection concept has been developed based on these measures for Ludwigsburg, our case study. The PV potential analyses as a measure is calculated for each individual building. The method is explained in detail.

Section snippets

Heat demand simulation method and data requirements

The urban simulation platform SimStadt [23] is applied to investigate the current status of energy demand, evaluate prospective energy demand scenarios and serve as an advance base to design concrete measures for action.

Within the space heating workflow, the thermal energy demand is calculated for each building, where some basic data like year of construction, building usage as well as proper 3D geometry is available. Geometry data are provided in the CityGML data format, an open data model for

Case study integrated climate protection concept of county Ludwigsburg

The district of Ludwigsburg is located in the South-West of Germany with a total population of 353.042 inhabitants (2013). This number represents about 5% of total population of the German federal state Baden-Württemberg. The total surface of the rural district is 68,682 ha with more than 54% used agriculturally, 18% forest area, 24% settlement and traffic area and 4% of the surface is water or other landscapes. The district of Ludwigsburg is the third largest in Germany and 34 municipalities

Simulation results and validation

The simulation process was performed for each municipality of the Ludwigsburg district. Fig. 1 illustrates the heating demand of one of the municipalities in a building level.

Fig. 2 depicts that the total heat demand of the 34 municipalities is equivalent to 973,955 tons CO2 emissions for simulated buildings. The overall CO2 emissions are dominated by the residential sector followed by transport. In the Trade and Industry sector, the electricity related CO2 emissions are significantly higher

Derivation of measures and initiatives from the analysis of results

An important part of climate protection concepts is the definition of strategies to decrease CO2 emission. Initiatives to reduce the energy demand (e.g. of buildings) can lead to direct or indirect CO2 savings. Another approach is to implement renewable energy supply systems to replace conventional systems, which can significantly lower CO2 emissions too. Using the simulation results of the SimStadt urban modeling platform, it is possible to quantify the effect of CO2 saving measures. The

Conclusions

Urban 3D models enriched with data on building’s construction, thermal properties and functions, like residential or office, offer excellent support for establishing climate protection concepts by allowing to quantify measures to improve energy efficiency and integrate renewables. In this study, SimStadt, an automated platform, has been employed for developing a climate protection concept for the case study of Ludwigsburg with almost 177,000 residential and non-residential buildings. SimStadt

Acknowledgements

This work has been carried out in the framework of the national project SimStadt funded by the German Ministry of Economics and the development of a climate protection concept for the region Ludwigsburg in a consortium led by Drees and Sommer. Here we would like to thank Athanasios Koukofikis, for his efforts for producing the 3D model presented in this paper in Fig. 1.

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