Download WP7 * Dissemination Activities

Low carbon energy transition in urban energy
systems – from energy modelling to urban planning
InSmart Project
S. Simoes, L. Dias, J. P. Gouveia, J. Seixas
G. Giannakidis, R.De Miglio, A. Chiodi, M. Gargiulo, G. Long, M.
Pollard, D. Irons, N. Bilo, A. Whitley, S. Burioli, L. Anthopoulos, V.
Nunes and all other members of InSmart consortium
This project has received funding from the European
Union’s Seventh Framework Programme for research,
technological development and demonstration under
grant agreement no 314164
July 5th 2017
1
European funded project (FP7) [2013-2016]
working in partnership towards a sustainable
energy future.
Cesena (Italy)
96 758 inhabitants
3.71 t CO2/ capita
Deka Trikala (Greece)
61 154 inhabitants
1.12 t CO2/ capita
Évora (Portugal)
56 596 inhabitants
3.71 t CO2/ capita
Nottingham (U.K.)
314 300 inhabitants
3.60 t CO2/ capita
VISION
more than half of
global population
80% of the world’s
GDP in 2013
two-thirds of primary
energy demand
70% of total energyrelated CO2 emissions
70% in 2050

Cities sustainable energy future are achievable by:
•
bringing together cities, scientific and industrial organizations,
•
considering the integration of the components of the city’s
energy system,
•
selecting cost-effective options from multiple data sources and
integrated tools,
•
choosing the best social-accepted technologies and measures.
InSmart Approach
Buildings managed by the municipality
Public lighting
Residential & commercial buildings
Expansion of bike lane
Transport
Waste, water & wastewater
Renewable electricity
City Energy system modelling
Per typology
Modelling an urban energy system
Based on household
level of income
Mobility
•
•
•
By type of fuel
By age (Euro Norm)
By origin-destiny matrice
 results are more easily traced and commented upon
Generate Sustainable Future (2030) Energy
(realistic) pathways
-
New district in the city (all buildings in class B + district heating)
Standards on refurbishment measures in the building sector
Production / Consumption of a certain fraction of electricity from RES
Development of new bike lanes
New entertainment centre or shopping mall in zone “x”
Reorganization of school schedule
10% of work from home for Municipality workers
Communication campaigns on efficiency and renewable development
 different combinations of actions generate
“Alternative planning hypotheses”
Multi-criteria decision with
stakeholders

Data gathering (rather intensive)

2+1 stakeholder workshops in March and September 2016
(Multi Criteria Decision Analysis):
1. Future technologies and measures towards low-carbon city by 2030
generated using the integrated city’s energy system modeling
2. Assessed through multi-criteria decision analysis with public bodies,
private companies, NGOs, city planners and decision-makers
cost optimality, improvement of living conditions in
the city, local development including job creation, minimization of
environmental effects, local renewables production
3. Priorities for development of Sustainable Energy
Action Plan including financing possibilities
Lessons learnt by the cities
“In terms of the privately rented housing sector, then there is considerable investment required in relatively
simple insulation measures. This is a gap as there is no UK funding currently available for private rented
accommodation.”
Nottingham, UK
“The primary benefit has been how the INSMART model has calculated a baseline for the urban energy system
in 2012, and demonstrated how local activities impact on energy demand.”
Trikala, Greece
The multi-model approach used in the project has provided a rationale for involving a multi-disciplinary team. By
including all the relevant sectors and views in the same storyline at the outset, the response (the minimization of
the whole energy system costs coupled with a ranking analysis) delivered by the methodology has ensured
“effectiveness” and “integration”. This will be used to screen the SEAP”
Cesena, Italy
“Thinking ahead about energy consumption in Évora as an “integrated urban energy system” highlighted new
priorities instead of those traditionally taken under municipal management, which is a challenge for a new
generation of local energy policies.”
Évora, Portugal
Urban planning slowly becames energy planning
www.cense.fct.unl.pt
www. sites.fct.unl.pt/[email protected]
pt
http://www.insmartenergy.com/
This project has received funding from the European
Union’s Seventh Framework Programme for research,
technological development and demonstration under
grant agreement no 314164
11
Key “quantitative” outputs of the model
for the multi-criteria analysis
The indicators that are used in the MCDA analysis as criteria in order to rank the
alternative scenarios were defined in close consultation with the local stakeholders
Trikala
- Implementation Cost (Million Euros)
- Energy Savings (kWh)
- Implementation cost efficiency (Euro/kWh)
- Operation and maintenance cost (million Euros)
- Revenue generation (million Euros)
Nottingham
- Total cost associated with a scenario over the
projected time horizon (£)
- Energy reduction potential relating to the scenario
(%)
-
-
Low carbon energy generated (TJ)
The cost efficiency in terms of its decarbonisation
potential ($/tonnes CO2)
Reduction in carbon (CO2) emissions (tonnes CO2)
Cesena
- Investments (and maintenance) costs (until
2030) (kEuro)
- Energy consumption in the building sector in
2030 (TJ)
- Total CO2 emissions in 2030 (t)
- Total particulate emissions in 2030 (kg)
- Onsite renewable production of energy in
2030 (TJ)
Evora
- Financial effort of total investment cost +
annual operation and maintenance cost (Euro)
- Reduction of energy consumption (PJ of saved final
and primary energy)
-
Reduction of GHG emission
emissions)
(t CO2 of avoided
4 zones
Geographically explicit
21 zones
20 zones
15 zones
identification of the basic geographical entities (zones) representing the most
suitable geographical distribution of the information for planning purposes