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Holistic District Heating Grid Design with SimulationX / Green City
Torsten SchwanRené UngerEA Systems Dresden GmbH
ESI SimulationX User ForumDresden, November 24th, 2016
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Copyright © ESI Group, 2017. All rights reserved.
www.esi-group.com
Note:
This presentation was published together with a technical paper. The full paper can be downloaded here.Paper Abstract Buildings are central elements of future smart grids. Heating and cooling demand are predictable within reason, building mass as well as heating and hot water systems provide inherent storage capacity. Additionally, the fluctuation between peak and average power of a building is much more friendly to the grid than of other network nodes like wind power or electric mobility.A local heating grid partially supplied by renewable solar heat is currently being built in a town in Bavaria. Heat pump systems provide additional storage capacity for electric grid surplus while they serve as wind energy dump for the local utility company. Cogeneration plants and peak-power boilers provide heat and power in times of low energy coverage. The low temperature heating grid supplies decentral heat pumps, which provide required heat at a much higher temperature level to each building.The paper describes basic modeling aspects for district heating grids with SimulationX & Green City. An interesting solar-aided grid example helps to identify benefits of a new modeling approach.
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Introduction and MotivationHolistic Design and District Heating Grids
• District Heating Grids – Advantages and Challenges?
• Holistic System Design – What does it stand for?
Centralization of heat and powergeneration
Utilization of synergy effects (e.g.cogeneration)
Maximization of individual operationtime
Minimization of system costs
Maximization of storage capacities
Consideration of all possible influencing
characteristics
Costs and Financing
Renewable Energy
Availability
Local Grid Structure
Heat, Cold and Power Consumption
Individual Requirements
Local Weather Conditions
Technical Feasibility
Reliability
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Introduction and MotivationRequirements on Simulation Systems
• Why System Simulation?• Individual human behavior• Volatile renewable energy availability• Condition-based multi-storage behavior• Frequently changing energy costs• … Good decision-making requires suitable simulation models
• Simulation System Requirements:• Reduced set of easy-to-get system parameters• Fast running simulation models with adequate numerical stability• Easy-to-handle model set up• Reduced set of evaluation parameters for decision-making• … System Simulation acquires increased importance, but still acceptable time period
for simulation analyzes is very short
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GreenCity / SimulationXModelica-based City Quarter and Power Grid Simulation
Easy-to-use and freely-available input data sets
• Climate data
• Building usage
• Simple buildingconfiguration
• Storage media
• eMobility fleet behavior
Holistic system design of district heating grids incl.
Power grid connection and eMobility charging
infrastructure
Modular Simulation Package
• Multi-voltage level grid
• Multi-temperaturedistrict heating andcooling grids
• City quarter sizeheating, cooling andpower supply incl.renewables andstorages
“Green City“ ModelicaTM Simulation Package, distributed via ESI ITI GmbH
• Gains and Consumption
• Dimensions and Feasibility
• Costs and Profitableness
• Strategies for Energy Management andStorage
Easy decision-making regarding system design and investment costs
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Green City Simulation Library – new Models forSmart-Grids & City Quarters: Power-Heating-Cooling-Storage-eMobility
Climate Datenew Formats
pipes
cable Street lighting
renewableswindpark, hydroelectric,photovoltaics
Distribution gridstransformersDC, storages
heating- / cooling machinesIce storageenviromental heat absorbers
HVAC components
parametric building templatesDefault buildings
Vehicle fleets& charging infrastructure
The Green City philosophy:flexible, easy, performant …
…getting your work done
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Green City vs. Green BuildingAdvancement of Building Simulation in SimulationX
Green Building
LV
MV
Green City
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New Residential Area – Osterfeld, City of HaßfurtSmall Town Detached Housing and Appartement Buildings
[google maps]
[Stadt Haßfurt]
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Additional Challenge of Local Utility CompanyBalancing Surplus Electrical Power via District Heating Grid
• Surplus in city grid caused by windpark and photovoltaics in local utility company‘s power grid
Balancing power requires additional costs when surplus electrical power is fed to grid
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Initial Energy ConceptMulti Temperature Heating Grid, Powered by Solar Thermal and Heat Pumps
Local Cogeneration(balancing energy)
Solar Thermal Collectors Heat Pump and Buffer
(negative balancing)
Medium Temperature Grid (55/30 °C)
Low Temperature grid (25/10 °C)with Heat Pumps
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Simulation Model of Heat & Power Supply
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Analyzed Grid Variants and Sample ResultsHigh temperature grid
direct heatingLow temperature griddecentral heat pumps
High temperature main grid with subgrid heatpump
High temperature main grid, heatpump / heat exchanger
Dropped due to high investment cost!losses
demand
CHP
Solar
grid heat:
Boiler
2.394 MWh 1.600 MWh 2.420 MWh
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Solar thermal absorbers and ground heat recovery withlow temperature return cold grid
0 kW
100 kW
200 kW
300 kW
400 kW
500 kW
600 kW
700 kW
31.12 30.1 1.3 1.4 1.5 1.6 1.7 1.8 31.8 1.10 31.10 30.11 31.12
Wärmelast Neubaugebiet
Solarabsorber therm. Ertrag
Heat load vs absorbers• Grid heat losses today have a significant share on overall energy consumption due to better building insulation
• Decreasing grid temperatures highly reduce overall grid losses
Heat recovery potential of cold return very low
Heat Load District Heating Grid
Solar Thermal Gains
Return Temperature cold grid (25/10 °C) Return Temperature warm grid (55/30 °C)
Ground Temperature 4 m Ground Temperature 1.5 m
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Surplus Power Heat Pump and StorageOptimization of collector size, storages and heat pump dependingon grid configuration
System Configuration with 800 m2 Solar Thermal Collector and different heat pump (30/50/100/300 kW) and storages sizes (30/50 m3)
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Detailed Analysis of Heat Pump Size vs. Utilization
• Storage sizes only insignificantly affect heat pump utilization
• Heat Pump utilization always lower than 500 h per year low negative balancing power vs. comparatively high investment costs
Heat Pump and Storages concept rejected
• Cold Storage rarely used due to comparatively high solar collector temperatures
• Maximum size heat storage highly increases solar thermal collector utilization
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Final ConfigurationSolar-aided low temperature district heating grid with decentral heat pumps
• Floating grid flow temperature depending on outdoor temperature (heat loss reduction)
• Space heating directly via district heating grid
• Domestic water supply via decentral heat pumps
• 100 m2 solar thermal collector with 30 m3 heat storage
• Maximum CHP size, power-controlled
• Solar collectors mainly compensate grid heat lossses in summer and transient times
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Conclusions
• New Green City library in ESI ITI‘s SimulationX helps to identify optimal configurations of district heating grids as well as heat, cold and power supply for whole city quarters
• Upcoming challenges of future energy supply require complex solutions (heat-cold-power cogeneration incl. renewables in city quarter size)
• Complex energy concepts require intensive simulation analyzes
• Presented simulation approach firstly used for a district heating grid in Hassfurt (northern bavaria)
• Final solution has been built from the beginning of 2015, will be extended from the beginning of 2017
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Copyright © ESI Group, 2017. All rights reserved.
www.esi-group.com
Download the Paper
This presentation was published together with a technical paper. The full paper can be downloaded here.Paper Abstract Buildings are central elements of future smart grids. Heating and cooling demand are predictable within reason, building mass as well as heating and hot water systems provide inherent storage capacity. Additionally, the fluctuation between peak and average power of a building is much more friendly to the grid than of other network nodes like wind power or electric mobility.A local heating grid partially supplied by renewable solar heat is currently being built in a town in Bavaria. Heat pump systems provide additional storage capacity for electric grid surplus while they serve as wind energy dump for the local utility company. Cogeneration plants and peak-power boilers provide heat and power in times of low energy coverage. The low temperature heating grid supplies decentral heat pumps, which provide required heat at a much higher temperature level to each building.The paper describes basic modeling aspects for district heating grids with SimulationX & Green City. An interesting solar-aided grid example helps to identify benefits of a new modeling approach.
Thank you for your attention
[Esters2012]
Contact:EA Systems Dresden GmbHWürzburger Str. 1401187 Dresden+49-351-467-136-52 [email protected]
