CHARACTERIZATION AND DATA COLLECTION OF CITIES ENERGY SYSTEMS AND NETWORKS:

Size: px

Start display at page:

Download "CHARACTERIZATION AND DATA COLLECTION OF CITIES ENERGY SYSTEMS AND NETWORKS:"

Erick Matthews
5 years ago
Views:

Energy Transitions Consumers Profiles & Energy Efficiency New Technologies & Low Carbon Practices CHARACTERIZATION AND DATA COLLECTION OF CITIES ENERGY SYSTEMS AND NETWORKS: INSIGHTS FROM INSMART

1 Energy Transitions Consumers Profiles & Energy Efficiency New Technologies & Low Carbon Practices CHARACTERIZATION AND DATA COLLECTION OF CITIES ENERGY SYSTEMS AND NETWORKS: INSIGHTS FROM INSMART PROJECT Policy Support Luís Pereira Dias S. Simões,. P. Gouveia, J. Seixas ENERGY & CLIMATE Integrative Energy City Planning Climate Mitigation/ Adaptation CO 2 Acknowledgements: 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 Workshop on Tools and Methodologies for Municipal Sustainable Energy Planning Kiev, Ukraine 10th and 11th of July 2017

2 AGENDA Context InSmart project (recap) City energy planning structure Methods & tools for data-driven integrated energy planning Buildings and transport Urban Spaces and public buildings & services analysis Energy supply system Renewable energy source potential Conclusions and recomendations

3 CONTEXT 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

INSMART PROJECT Vision Cities sustainable energy future are achievable by: bringing together cities, scientific and industrial organizations, considering the integration of the components of the city

4 INSMART PROJECT Vision 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. Purpose Design comprehensive data-driven methods for enhancing the city s sustainable planning, addressing the current and future city energy needs. Implement an integrative planning tool to identify the optimum mix of short, medium and long term measures for a sustainable energy future for the city. Address the efficiency of energy flows across all city sectors considering spatial patterns and economic, environmental and social criteria. Engage city agents to pave the implementation of priority actions.

METHODS & TOOLS FOR DATA-DRIVEN INTEGRATED ENERGY PLANNING First: Analysis of existing sustainable policies and data availability for each city identification of data gaps and challenges and propose

5 METHODS & TOOLS FOR DATA-DRIVEN INTEGRATED ENERGY PLANNING First: Analysis of existing sustainable policies and data availability for each city identification of data gaps and challenges and propose specific measures either in the form of necessary actions, (e.g. necessary preliminary studies, data acquisition, monitoring) as well as organisational restructuring of the city administration in order to achieve the sustainability targets. Include different departments of the municipality to contribute

6 METHODS & TOOLS FOR DATA-DRIVEN INTEGRATED ENERGY PLANNING Door-to-door Buildings and Transport and Mobility Surveys Comprehensive GIS energy city database (present situation and future scenarios) Cities buildings stock characterized and modeled through a typology approach (using Energy Plus modeling tools) Extensive data from smart meters (relying on a sample of the EDP Distribution S.A., INOVGRID project) Transport based energy and carbon model Integrated modeling with TIMES (The Integrated Markal-Efom System) technoeconomic optimization modeling tool. used to analyse the mix of measures required to meet sustainable energy targets Selected measures assessed with respect to non-technical criteria using a multicriteria decision making method (PROMEΤHEE (Preference Ranking Organization Method for the Enrichment of Evaluations) to address economic, environmental as well as social issues.

7 TRANSPORT AND RESIDENTIAL SECTORS Model building energy consumption 1. Initial data collection -data and local knowledge already exists -> census records, spatial datasets, municipal records and national surveys; 2. Identify building typologies identify a set of building typologies, based on construction period and built form, to represent the city s housing stock (e.g. modern detached houses built after 1980 or historic terraced or row housing built before 1900). 3. Detailed housing surveys Conduct a survey of city households to collect the data required for the construction of energy models for each city s building typologies, based on a representative sample of each city s housing stock according to its distribution. The surveys collected a wide range of information including details of the built structure (materials, insulation, internal floorplans, glazing), its occupants (age, income, employment status), heating/cooling systems presence and use, electrical appliances and lighting; Nottingham around 600 surveys were performed, and in Évora around Detailed electricity use analysis 5. Energy demand modelling

8 TRANSPORT AND RESIDENTIAL SECTORS Model building energy consumption 4. Detailed electricity use analysis When smart meters data are available, they are highly valuable in understanding electricity use within each building typology. Due to its high temporal granularity, data from smart meters shows how energy is consumed over the course of a day and how this varies over the course of a year. identify potential fuel poverty within certain types of housing and/or in particular areas of a city. Important to calibrate the energy demand modelling and to guide the selection of measures while respecting the city s socio-economic features. 5. Energy demand modelling Create simulation models using building energy modelling software. EnergyPlus was used for the four INSMART cities but other similar software tools could also be used. Sensitivity analysis is performed to identify the variables that have a signifcant impact on energy demand Gouveia, J.P., Seixas, J. (2016). Unraveling electricity consumption profiles in households through clusters: Combining smart meters and door-to-door surveys. Energy and Buildings. 116,

9 TRANSPORT AND RESIDENTIAL SECTORS Housing retrofit modelling Identify potential retrofit options for each building typology. These include options for upgrades to heating/cooling systems, addition of insulation to walls, roofs or floors, draught-proofing measures or the addition of shading devices. The impact of each retrofit option is simulated using the building energy models Map of simulated total energy demand for residential buildings in Nottingham

10 TRANSPORT AND RESIDENTIAL SECTORS The model developed as part of the INSMART project is an easy to use, but complex model, requiring minimal input data and computational time, but still providing sufficient sophistication to produce meaningful outputs for a variety of scenarios. The model includes processes for: Trip Generation from household numbers and floor space information for non-residential locations; Modal Choice between highway and public transport. Route Choice for both highway and public transport, allowing for the testing of new roads, traffic restrictions and new or altered service patterns and routes. Splitting vehicular demand into detailed Fleet Types, by vehicle and fuel type and Euro class rating, to allow for detailed emissions calculation. Fuel Consumption calculations for the entire city, split by movements between city zones, plus the zone where the fuel is consumed. Data on demand flows, vehicle kilometers and key emissions such as CO2, Hydrocarbons and PM10s is produced. Modelling process Creation of a Base Year model Running the model forward to 2030 Forecast scenarios Representing the current energy usage situation in each city; Transport surveys were undertaken in each city (a minimum of 400 surveys was required) to assess the different trip-making purposes and patterns demonstrated the effect of changes in population and the vehicle fleet over time as people switched to more efficient vehicles and represented a Do Nothing scenario to which all others could be compared Run wide range of forecast scenarios providing changes in demand, energy usage and emissions compared to the Do Nothing scenario

11 TRANSPORT AND MOBILITY

12 METHODS AND TOOLS APPROACH Analysis of the cities energy systems and networks: Energy supply Data Acquisition: Municipality teams + technical teams contacted local stakeholders for data collection (also created the opportunity to introduce/open the project to local community).

13 URBAN SPACES AND PUBLIC BUILDINGS & SERVICES ANALYSIS AND ENERGY SUPPLY SYSTEM Urban spaces Water and sewage system Municipal Solid Waste chain City Energy supply system Buildings under Municipal Management Private services Buildings

URBAN SPACES Characterization of the public urban spaces with relative

areas location in Évora municipality Type/electricity consumption Peak (kwh)

Irrigation 67 915 9 504 77 419 Other 29 433 36 100 65 533 Fountains 30 175 42

14 URBAN SPACES Characterization of the public urban spaces with relative importance consumption of energy within the city Public lighting Gardens/green areas location in Évora municipality Type/electricity consumption Peak (kwh) Off-peak (kwh) Total Gardens Lighting Irrigation Other Fountains Electricity consumption of gardens and fountains in 2013 in Évora municipality (kwh)

WATER SUPPLY, SEWAGE AND MSW SYSTEM Évora water system facilities Trikala

kwh Production year 2014 kwh Plant operating hours per day Quantity of

Depurator DEP PIEVESESTINA 298 265-24 466 464 Depurator DEP CALABRINA 23

15 WATER SUPPLY, SEWAGE AND MSW SYSTEM Évora water system facilities Trikala Sewage systems (MWh/yr) Plant Name of the Plant Energy consumption 2014 kwh Production year 2014 kwh Plant operating hours per day Quantity of treated sewage Depurator DEP CESENA Depurator DEP PIEVESESTINA Depurator DEP CALABRINA Depurator FITO CALABRINA Depurator FITO BAGNILE

SERVICES SECTOR Buildings managed by the Municipality Electricity consumption of the buildings and equipment s managed by Évora municipality in 2013 Location of Évora schools Sector Peak Off-peak

16 SERVICES SECTOR Buildings managed by the Municipality Electricity consumption of the buildings and equipment s managed by Évora municipality in 2013 Location of Évora schools Sector Peak Off-peak Total annual (MWh) (MWh) (MWh) Municipal buildings Education Dwellings for social housing Churches and monuments (lighting) Sport facilities (e.g. swimming pools, sports halls) Leisure (e.g. Teatro Garcia de Resende)

SERVICES SECTOR Private Buildings Retail: Large Food supermarkets and local shops (those within the residential areas); Location of small services buildings in Évora municipality Office buildings

17 SERVICES SECTOR Private Buildings Retail: Large Food supermarkets and local shops (those within the residential areas); Location of small services buildings in Évora municipality Office buildings Education: Primary, secondary and college/ University; Leisure: Restaurants, hotels and cinemas Health: Hospitals and health centers Energy consumption in 2013 (GJ) Fuel Offices Retail Leisure Education Health Electricity Natural Gas LPG Gasoline Diesel Total

geothermal). 4) Wind resources in the city.

18 ENERGY SUPPLY SYSTEM Cesena natural gas network Assessment of RES resource potential 1) Solar technologies (PV and solar water heaters) 2) Geothermal (low enthalpy geothermal). 4) Wind resources in the city. 5) Biogas from the sewage treatment system and the landfill. 6) Biomass in areas surrounding the city. Geothermal potential in Greece

19 ESTIMATE OF SOLAR POTENTIAL IN THE CITIES Solar thermal Solar technologies Solar photovoltaic Diverse PV technologies Monocrystalline silicon Multicrystalline silicon HIT (Heterojunction with Intrinsic Thin Layer) Amorphous silicon (nontransparency type) Rooftop Façade Plant size Residential Services Residential Residential Due to the growing importance of solar technologies as decentralized energy supply technologies a supplementary assessment was made in order to identify each city technical potential.

ESTIMATE OF SOLAR PV POTENTIAL IN THE CITIES PV utility scale Dias, L., Lourenço, P., Gouveia, J., Seixas, J. 2016.

20 ESTIMATE OF SOLAR PV POTENTIAL IN THE CITIES PV utility scale Dias, L., Lourenço, P., Gouveia, J., Seixas, J Interplay between photovoltaic systems potential and agriculture uses. Land Use Policy (under resubmit process after revisions) Location of PV-track systems for different land scenarios (1 MW project size)

21 ESTIMATE OF SOLAR PV POTENTIAL IN THE CITIES Technical potential for all the cities Per Building typologies (Évora) 314 MW Per city zone (Nothingham)

22 CONCLUSIONS (AND RECOMMENDATIONS) Challenges on the harmonization of data at different scales (e.g. point data on public lighting to areal information); All cities faced some difficulties on data privacy issues from the companies and on response time; The data collection allowed a important interaction and engagement with key city stakeholders, and the opportunity for dissemination of the project Engage the different city management departments in the early stages of project: data collection and measures co-creation and validation; Validate the cost-benefit allocation of work/time to model detail specific sectors/buildings; Endogenous renewable energy sources potential assessment is highly important;

com/ THANK YOU Policy Support ENERGY & CLIMATE Integrative Energy City Planning Climate

23 Consumers Profiles & Energy Efficiency Energy Transitions New Technologies & Low Carbon Practices THANK YOU Policy Support ENERGY & CLIMATE Integrative Energy City Planning Climate Mitigation/ Adaptation CO 2 Luís Pereira Dias luisdias@fct.unl.pt Workshop on Tools and Methodologies for Municipal Sustainable Energy Planning Kiev, Ukraine 10th and 11th of July 2017

Cesena (Italy) Deka Trikala (Greece) Évora (Portugal) Nottingham (U.K.)

Cesena (Italy) Deka Trikala (Greece) Évora (Portugal) Nottingham (U.K.) Work Package 4 - Analysis of the cities energy systems and networks Energy Consumption in Municipal Services and Estimation of solar