A VIRTUAL REALITY TOOL FOR PLANNING ENERGY EFFICIENT CITIES

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Meryl Moore
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1 A VIRTUAL REALITY TOOL FOR PLANNING ENERGY EFFICIENT CITIES European Energy Research Alliance EERA 2016 Dipl.-Ing. Nicole Mencke Fraunhofer Institute for Factory Operation and Automation IFF Magdeburg

2 AGENDA 1. Motivation 2. Fundamentals 3. Planning energy efficient cities Virtual reproduction of cites System modelling Supply planning 4. Conclusion 5. Future work

1. Motivation Nationwide availability and supply of the population and economy with electric energy Goal Optimisation of energy efficiency Increase of independent energy

3 1. Motivation Nationwide availability and supply of the population and economy with electric energy Goal Optimisation of energy efficiency Increase of independent energy supply Basic parameters to be pointed out Scientific progress Change of legal parameters Increased attention of the population regarding impacts and the right to have a voice 3

2. Fundamentals - Virtual Reality (VR) Immersion Computer-generated simulated 3-D environment One or more users may interact in real time Virtual environments offer different levels of immersion that

4 2. Fundamentals - Virtual Reality (VR) Immersion Computer-generated simulated 3-D environment One or more users may interact in real time Virtual environments offer different levels of immersion that means the extent of physical and psychological attachment of the user Imagination is the basis for the (virtual) experience and depends on the quality of interaction and immersion Interaction I³ Imagination 4

5 2. Fundamentals - Process steps Planning energy efficient cities Virtual reproduction System modelling Supply planning Semi automatic generation of 3D environments - Terrain model - Buildings - Vegetation Implementation of a simulation models for buildings - Energy producer - Energy consumer - Climatic parameters Adjustment of parameters - exchange of producers - Scaling consumption - Multi level analysis 5

triangulated irregular network (TIN) based on an elevation model Texture generation by using aerial

6 3.1 Virtual reproduction of cites Raw data Raw data Digital ground model High-resolution aerial photo, pictures GIS-data, topographic maps Digital basic town map Data processing Generation of a triangulated irregular network (TIN) based on an elevation model Texture generation by using aerial photo Detailed presentation of urban areas and infrastructures based on e.g. the digital basic town map 6

7 3.1 Virtual reproduction of a cite Process steps I 1. Ground and environment model 2. Infrastructure 3. Buildings and other constructional structures Block model Simple model Complex model Architectural model 7

Movable objects Vehicle and human models

8 3.1 Virtual reproduction of a cite Process steps II 4. Vegetation and other objects Different tree species, street lamps, etc. 5. Movable objects Vehicle and human models 6. Entry points for presentations and interactions Viewpoints and flights 8

9 3.1 Virtual reproduction of a cite - Examples 9

3.2 System modelling - Approach System components (electrical and thermal) Energy production Wind power Water power Biomass and waste materials Photovoltaics as building installation, Solar heat

10 3.2 System modelling - Approach System components (electrical and thermal) Energy production Wind power Water power Biomass and waste materials Photovoltaics as building installation, Solar heat and heat pump Energy consumption Initial coupling of generation and consumption values with buildings Consideration of building parameters Consideration of climatic parameters Storage in an database 10

3.2 System modelling Consumer (electrical vs.

Load type Residental building 1 9.000-35.000 3.000-5.

337-13.035 Electrical and thermal (heat and cold) Car park 112.

11 3.2 System modelling Consumer (electrical vs. thermal) Building type Volume in [m³] Building useful area in [m²] Load type Residental building Electrical and thermal (heat) Office building Electrical and thermal (heat and cold) Car park Electrical Production facility Electrical and thermal (heat) 11

12 3.2 Building modelling Energy production values Energy consumption values Climatic parameters SimulationX - Model 12

13 3.3 Supply planning - Visualisation Multi level information analysis and presentation Buildings Building cluster Town 13

14 3.3 Supply planning Planning / Optimisation I System model adjustment Adding new producer Coupling with buildings Independent from buildings Exchange of producers: planning variants Visual scaling of buildings including scaling of consumption values Adding new buildings by using a library (including simulation data) If necessary simulation of initial values for new building types 14

Citizen beliefs Presentation of additional information, e.g.

15 3.3 Supply planning Planning / Optimisation II Consideration of basic parameters during adaption Natural conditions Laws Political goals Citizen beliefs Presentation of additional information, e.g. Initial costs Operating costs Realisation efforts Possible energetic savings Expansions stages 15

4. Conclusion VR based planning tool Initial situation Steering of planning processes Goal Scalable zero or positive energy balance Solution Visualisation of

16 4. Conclusion VR based planning tool Initial situation Steering of planning processes Goal Scalable zero or positive energy balance Solution Visualisation of all cite and energy system components Analysis and calculation of energy efficiency Optimisation of energy efficiency Increase of independent energy supply 16

17 5. Further Work Integration of energy storages Algorithm for system based optimisation proposals, e.g. Using the spatial resistance map respectivily priority area definition for proposals to place energy producing facilities Automatic variant planning for technical realisation of producer (e.g. photovoltaic system with different energy efficiency levels) Integration of additional ressources (cold, fresh air, ) Further application domains (industrial estates, ) Further developement towards a public communication and participation portal 17

Sandtorstraße 22 D-39106 Magdeburg Head: Univ.-Prof. Dr.-Ing. habil. Prof. E.

18 Thanks for your attention! Fraunhofer Institute for Factory Operation and Automation IFF Magdeburg Sandtorstraße 22 D Magdeburg Head: Univ.-Prof. Dr.-Ing. habil. Prof. E. h. Dr. h. c. mult. Michael Schenk Contact person: Dipl.-Ing. Nicole Mencke Tel.: Mail: 18

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