Positive Energy Districts Making City Project Klaus ...
Transcript of Positive Energy Districts Making City Project Klaus ...
Positive Energy Districts
Making CityProject
Klaus Känsälä, VTT
26/03/2019 VTT – beyond the obvious 1
• +eDistrict manages its energy consumption and the energy flow
between buildings in the district and the wider energy system
• Healthy lifestyle &attractive place to live
• Positive Energy Districts have an annual positive energy balance
Definition of +eDistrict : boundaries
+eDistrict, SET concept http://smartenergytransition.fi/
Carbon-Free district vision 2050
Solar heat
collectors
Electrical
storage
Wind turbines
District heating network, temperature approx. 65 °C
Industrial heat pump
Old residential buildings with
solar PV, EVs, heat pumps and hot water storage tanks
New residential buildingswith solar PV and EVsOffice buildings,
data centers etc. with solar PV, EVs and heat pumps
Flexible bio-CHP (back-up power)
District cooling network Seasonal heat storage
School building withsolar PV
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Smart CitiesEuropean Cities
43Smart Cities portfolio
5M€
Oulu Lighthouse City
Helsinki Lighthouse City
Tampere Lighthouse CityKerava Follower City
Tampere Nature-Based
solutions Living lab
Smart City Performance
measurement system
Making City
EU 2020 Lighthouse project
22 M€, City of Oulu; City of Gröningen
Oulu: Kaukovainio• 8M€ budget ; 12.12.2018-31.12 2023
• 15000 m2;
• 200 apartments
• Supermarket
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Buildings refurbished using smart energy-efficient systems.
Demonstration of solutions like heat recovery, building energy modelling
and other advanced ICT schemes.
Combined heating and cooling energy systems introduced to reduce and
recycle heat waste
Electric vehicles and charging stations
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Main innovations: Zero Energy Block
Local heat disribution pipeline (T<60C) connecting all buildings
Heat power plant powered by solar energy, harvesting from
district heating pipeline
Local heat storages based on PCM (Phase Change Material)
Efficient use of waste energy
Supermarket used as a storage and supply for energy
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8Local District heating network T +35 / +65 °C
Old residential buildings/retrofitted with solar PV, EVs, heat pumps and hot water storage tanks
New Sivakka residential buildings with solar PV andEVs
Commercial Building
Arina with solar PV and heat recovery
District cooling network
School building with Solar
Panels
+eDistrict, best practices: Oulu case
Energy storage, ground well + heat pump
PCM tank
PCM tank
High efficiency - heat pump 250 kW nominal output
Heat recovery from City District Heating return pipe
New YIT residentialbuildings with PCMtank and EVs
+eDistrict, best practices: Oulu case
Heat distribution between the
buildings on site (Local LT Pipeline)
Solar thermal plant (270 kW
nominal output) with very low carbon
footprint
High efficiency - Heat pump
systems with CO2
Retrofitting of old building. Low
temperature distribution
Decentralized PV system
Energy storages (wells, PCM tanks)
Heat recovery from buildings and
from regional heating pipeline
Definition of +eDistrict: boundaries
10
energy use for space
heating, space
cooling, domestic hot
water, ventilation,
built-in lighting and
other technical
building systemsEnergy use of home appliances may or may not be included, countries can decide
Picture: Kurnitski J (ed) (2013) REHVA nZEB technical definition and system boundaries for nearly zero energy buildings, 2013 revision for uniformed national implementation of EPBD recast prepared in cooperation with European Standardization Organization CEN. REHVA Report No. 4, 2013. www.rehva.eu
Data collection & management
Based on open source code
platforms and DB solutions
Supports Python, AI and
machine learning tools
Provides non Vendor lock
approach to all system
components
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MySQL /MariaDB
Node.js
Nginx
HeidiSQL R-Studio
SQL
CSV
HTML
QueriesMQTT / REST API for read/write operations
Server environment
Staticfiles
-Reverse proxy-Web server
Visualisation web pages
Data storage
Off-line analytics
External import /Export tools
Web server for datamanipulation and SQL queries
Objectives: project evaluation
Robust monitoring and evaluation protocols for projectactions and interventions
Validation of PED through measurement of the performance of actions
Evaluation of the impact at 1. project level 2.city level Based on « CityKEYS » and « SCIS » -> KPI Selection -> Data Collection procedures
All performance data (i.e. KPIs) will be incorporated into SCIS database
• Strong cooperation with the lighthouse cities• https://www.smartcities-infosystem.eu/
Project level KPIs:Building and energy cluster
Reduction in annual final energy consumption
Renewable energy production
Annual CO2 emission reduction
Peak load reduction
Calculation methodology of the technical KPIsmust be checked. The basic equations mayhave to be modified depending on the case.
Project level KPIs:ICT cluster
Availability of real time data
Quality of open data
Use of standard interfaces
Improved interoperability between systems
Number of smart apps developed using open data
platforms
Project level KPIs:E-mobility cluster
Number of public EV charging stations
Renewable energy use
Annual CO2 emission reduction
kWh recharged in the EV charging stations
City level KPIs:Common city level cluster
Involvement of city administration
Number of ways in which citizens can communicate
with the municipality
Local community involvement in implementation
phase
Increase in online municipal services
Increased consciousness of citizenship
Stimulating an innovative environment
City level KPIs:Long-term effects
Interoperability of solutions
Payback period
Total investment costs vs. subsidies
Market demand
Diffusion to other locations
New forms of financing
VTT has studied the possibilities of circular energy
CASE studies covered homes, public and business buildings
Most interesting result were obtained from grocery stores
Main innovations were:• Heat pump technology for cooling/heating
• Heat dwells for energy storaging and heat generation
• CO2 used instead of F-gases; higher COP
• Renewable energy (solar&heat) used
RESULT 50% reduction in total energy consumption
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Two Shops comparison: Shop1 (2000 m2)
Shop2: 7 year older design (1750 m2)
Results:
Advanced cooling technology saves up to 50% of energy bill
In this case study the savings are 200 000 euro/year
Energy consumption was reduced to average 240 kWh/m2/year
Payback time for investment was less than two years
In Finland this supermarket chain has got 1800 shops
They consume 1.1 TWh energy annually
This could be reduced to 0.7 TWh with the new technology
Thank you, any questions?
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