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1

Guidebook

Training Module 1

Input data for mapping

Version 0.37 – 12 december 2017

Organisation name of lead contractor for this deliverable: TUD

Dissemination Level (Specify with “X” the appropriate level)

PU Public

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

X

CO Confidential, only for members of the consortium (including the Commission Services)

1

Table of Contents List of abbreviations / Nomenclature ............................................................................................... 2 1 Introduction to the mapping module ........................................................................................ 4

1.1 Why is mapping needed? ................................................................................................. 4 1.2 What are final results of the mapping process?................................................................. 6 1.3 General approach to the mapping process ....................................................................... 8 1.4 Energy demand and potential energy supply .................................................................... 8 1.5 Supply and demand temperature .................................................................................... 10 1.6 Introduction to the specific technologies and best practice examples .............................. 13

2 Mapping data specifications .................................................................................................. 17 2.1 Introduction ..................................................................................................................... 17 2.2 What is there to map in cities? ........................................................................................ 17 2.3 General form of the mapping data – shape and size ....................................................... 19 2.4 Which data is crucial for mapping of the heating and cooling demand? .......................... 20 2.5 Which data is crucial for mapping the potential of local renewable energy sources? ....... 22

3 Available databases .............................................................................................................. 23 3.1 Possible issues on finding the needed data .................................................................... 23 3.2 Presentation of the available databases ......................................................................... 23 3.3 Additional sources of information on national level .......................................................... 27

4 References ............................................................................................................................ 31

Guidebook Training Module 1: The Mapping Module 2

List of abbreviations / Nomenclature

Abbreviation Definition

WP Work package

TUD Delft University of Technology

RINA-C RINA Consulting Group

DAPP D'Appolonia

UNIZAG FSB University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture

GEO Geonardo

GA Grant Agreement

UHI Urban Heat Island

NOA National Observatory of Athems

KPI Key Performance Indicator

DHN District Heating Network

CHP Combined Heat and Power

H&C Heating & Cooling

kWh Kilowatthour

MWhe Megawatthour electric

MWhth Megawatthour thermal

Px Pixel

ATS Advanced Thermal Storage

DHW Domestic Hot Water

COP Coefficient of Performance

HDH Heating Degree Hours

CDH Cooling Degree Hours

GIS Geographic Information System

CMM City Mapping Module

DMM District Mapping Module

GUI Graphical User Interface

CO2 Carbon dioxide


This document is a first draft of the guidebook for training module 1 of the PLANHEAT tool, as a result of WP5 and (part of deliverable D5.2) of the PLANHEAT project. It will constantly be updated during the project and evolution of the tool.


Guidebook Training Module 1: The Mapping Module

1 Introduction to the mapping module

1.1 Why is mapping needed? On district, urban or regional level there are several unused renewable energy sources available to supply the demand for heating or cooling of buildings. In order to match this (unused) supply and demand, the energy should be available:

in the right amount

in the right form and quality (at the right temperature)

at the right time

at the right place

Generally, this is not the case, so the energy should be converted, stored and/or transported. All these actions cost materials, money and energy and the question is, which choices lead to responsible and optimal solutions for thermal energy systems.

Figure 1: Matching energy demand and supply: Local conversion of solar radiation into heat by solar collectors, transported to the consumers through a district heating network or stored for later use in a borehole seasonal thermal storage (https://permies.com). District heating and cooling networks (DHN) are used to transport heat or cold from the source to the place where it is needed. In fact DHNs match in a spatial way the supply and demand. To make an optimal match, we need information about the location of the demand for heating or cooling and of the supply (source). With this information, the energy transmission losses in the


network, the extra energy demand for (heat)pumps, the dimensions of the components and the costs can be estimated in order to compare different strategies and plans for an optimal thermal distribution network.

Figure 2: Matching heat demand and supply from different sources in a heat grid of Vienna (Duurzame warmte en koude, 2017) In order to plan for DHN’s, we need to know how much energy of a certain temperature is needed and at what time it is needed (e.g. summer, winter, day or night). We also need information about the source; at what time is the energy available and at what temperature? If the time doesn’t match we need additional thermal storage (diurnal or seasonal). If the temperature doesn’t match the demand temperature, we need conversion to the requested temperature (e.g. by a heat pump or absorption chiller). Thermal storage and conversion techniques are often an integrated part of district heating and cooling networks. It is not always easy to collect all the information. The PLANHEAT mapping module will support you in mapping all this information. The first need in the process of selecting and assessing future H&C scenarios is to have a clear picture of the local framework in terms of H&C demand and available energy sources. According to this need, the entry point to the PLANHEAT platform will be the mapping module, software dedicated to the visualization of information about local H&C demand (for residential and services sector), different categories of energy sources and related supply potential on georeferenced maps. The mapping module will work on the basis of inputs provided by users and it will be able to adapt to data availability providing both:


Default values (in case of missing data);

The possibility to perform analysis at different level of detail according either to the granularity of the available data or to specific needs of the user.

A high-quality mapping process is crucial in order to plan precise heating and cooling scenarios and achieve accurate results of the simulation.

1.2 What are final results of the mapping process?

The final result of the mapping process are maps on city and district level with several forms of geo-referenced information including heating and cooling demand, and renewable energy potentials. The resulting maps will serve as a starting point for the planning and simulation module. The maps visualize the distribution of energy sources over the city (raster maps); tables can be derived for spatial subdivisions that can be chosen by the user, making an aggregation of all pixel values located within that region. This allows making a ranking of the energy saving potential and the various low carbon heating and cooling sources per subarea. With the tool, total heating or cooling demands can be retrieved per district and per function. Next to maps on heating and cooling demand, maps with renewable potentials will be a result of the mapping process. Also total amounts of supply potentials can be retrieved for each district or chosen area We can see in fig. 3 – 6 in a preliminary version of the mapping module, how demand for heat for Antwerp city is visualized in a GIS environment.

Figure 3: Maps on city and district level with information including heating and cooling demand


Figure 4: Data on city and district level with information including heating and cooling demand

Figure 5: Map with information about biomass potentials in the Antwerp region


Figure 6: Data with information about reusable and renewable potentials in Antwerpen.

1.3 General approach to the mapping process In order to make maps with the PLANHEAT tool, next approach should generally be applied:

Define the purpose of the mapping process e.g.:

o ‘’We want sustainable cooling for our office area” “what are the potentials?”

o ‘’We have industrial waste heat available and want to know where a concentrated

heat demand is in our city, to utilize this in a DHN’’

Define the needed data in order to create maps

Search for available databases and acquire need data, if the data isn’t publicly available

default values or the modelling approach could be used

It is valuable to have more than one source of the information in order to make comparison

which could indicate possible data error

Use the gathered data as the input data in order to create needed maps

1.4 Energy demand and potential energy supply The PLANHEAT mapping module will have two tasks: to map the energy demand and energy supply potential. Energy demand consists of energy needed for heating, cooling and domestic hot water.


Figure 7: heating devices

Figure 8: cooling devices

Figure 9: Domestic Hot Water devices


Energy supply potentials represent locally distributed renewable energy sources such as: solar energy, geothermal and biomass including the waste heat coming from cogeneration plants, industry, and city infrastructure such as city sewage, public transportation, data centres.

1.5 Supply and demand temperature Temperatures in heating and cooling systems For different kind of demands, different temperatures can be required. We can make the following distinction in supply temperatures for the energy demand: 90oC for very-high-temperature space heating, domestic hot water and absorption-cooling 70oC for high-temperature space heating and domestic hot water and adsorption-cooling 50oC for low-temperature space heating 35oC for very low-temperature space heating 25oC as a source for a heat pump to produce heat for space heating and domestic hot water 15oC as a source for a heat pump for heating and domestic hot water and for high-temperature

cooling of spaces 5oC for cooling of spaces and as a source for a heat pump

The larger the difference between supply temperature and the environment of the thermal grid, the higher are the energy losses in this thermal grid. It is important to keep the temperature difference as low as possible. The required temperature depends on the characteristics of the building and the purpose of the demand. Domestic hot water for example needs a temperature of at least 60oC because of the risk of legionella bacterial infection. A disadvantage of combined space heating and heating of domestic hot water with a heat grid is the fact that there are a lot of transmission losses in the grid in summertime when there is no heat needed for heating the buildings. The losses in summer are almost the same as in the winter period, whereas the heat demand is much smaller. For this reason heating of domestic water is often decentralized. In the past a temperature of 90oC was regular for central heating systems with radiators or convectors, but with better insulation and more energy efficient buildings nowadays, we can

heat the buildings with radiators or convectors at temperatures of 70oC. This is also a good temperature for domestic hot water. 50 oC is

not high enough for domestic hot water but can often be used for space heating when using enlarged radiators with a larger heat dissipation surface, convectors with fans to increase heat output or by floor- wall- or ceiling-heating. When the building has hardly any ventilation and

Figure 10: Space heating and domestic hot water from district heating [District energy in cities, 2016].


transmission losses, it is even possible to heat the rooms with 35oC. These temperatures concern the northern and middle part of Europe. For the southern part of Europe even lower temperatures can be used for space heating. Energy supply Next to energy demand, the PLANHEAT mapping module will also have to map the energy supply potential. Energy supply potential represents locally distributed renewable energy sources potential such as: solar energy, geothermal and biomass including the waste heat coming from cogeneration plants, industry, and city infrastructure such as city sewage, public transportation, data centers etc. Also heat or cold from the shallow underground, groundwater or surface water (sea, lake or river) is a potential source. The underground or groundwater can even be used as a storage for heat or cold. The supply temperature of the heating and cooling media in district heating and cooling systems and the time it is available has great impact on the overall efficiency of the system.

Figure 11: Capturing waste heat from sewage and wastewater [District energy in cities, 2016]. Solar collectors are able to deliver temperatures from 30°C up to more than 100°C, even solar collectors are being developed to generate steam useful for industrial processes. If the temperature of the source is too low, for example if we collect the heat from a sewage system (typical 10°C-30°C), the shallow underground (typical 10°C-18°C), groundwater (typical 10°C-18°C) or surface water (typical 10°C-30°C), then we need a heat pump to bring it at a suitable temperature. This will cost some extra energy (most often electricity) that can be generated in a sustainable way, e.g. with wind turbines or PV-panels. It is much easier to store heat or cold then electricity. Excess electricity production, such as from wind or solar generation, can be used and stored for district energy after conversion to heat (up to different desired temperatures), providing valuable demand response for the power system, this is also called power-to-heat. This electricity can power large scale heat pumps which capture low-grade heat (such as from the underground) to produce heat for a heat storage tank or feed it directly into a district heating network.


Figure 12: Connecting renewable electricity generation to a district energy system with thermal storage.

Similarly high-efficiency electric chillers could provide demand response cold water to be stored or directly used in a district cooling network, power-to-cold. Through such means district energy can enable the use of higher shares of renewable energy in energy systems. As mentioned before, cold from a river, lake, sea, the underground or groundwater can be used for district cooling. We can use it for high-temperature cooling (free cooling) or as a source for a heat pump (chiller) to produce lower temperatures efficiently. Cooling with surface water is often combined with an aquifer (ATES) or ground-heat exchanger (BTES) because the temperature of the water in the summer (when most cooling is needed) is too high for free cooling. The aquifer or underground is regenerated in wintertime with the colder surface water. In summer the cold water from the aquifer can be used again.

Figure 13:Connecting sources of free cooling to the district cooling [District energy in cities, 2016].


Figure 14. :Borehole Thermal energy storage (BTES) [www.underground-energy.com]

Figure 15: :Aquifer Thermal energy storage (ATES) [www.underground-energy.com] .

1.6 Introduction to the specific technologies and best practice examples In order to understand the process of matching the supply and demand, basic supply technologies and general working principles will be introduced here. Boilers

Figure 16: A gas, oil and coal boiler for hot water for heating and domestic hot water.


Heat pumps are able to lift the temperature of a source to a certain higher level with the use of some (electric) energy. The efficiency of a heat pump depends on the temperature lift (delta T); the higher the lift, the lower the efficiency is (see figure 17). Because most heat pumps work on electricity, a temperature lift of more than 70K isn’t interesting because it will cost more electricity than heating with a simple electric conductor. Most regular heat pumps are not able to use source temperatures higher than 25oC.

Figure 17: The efficiency of a heat pump depends on the temperature lift. Heat pumps are also able to work reversible which means that they can be used as a chiller to cool the building and heat the source. If there is a source temperature available of 15oC or lower, we can use this for cooling directly (without the heat pump, this is called free cooling). This makes the cooling process 40 times more efficient (only electricity for pumps is required). If we use high-temperature cooling, we need the same dissipation equipment as we need for low temperature heating. The cooling capacity will be limited in comparison to low-temperature-cooling. The temperature for low temperature cooling will be around 5oC.

It is even possible to cool with heat by ab- or adsorption cooling. Absorption cooling needs a source temperature of 90oC while adsorption cooling can even work with a source of 70oC. Some electricity is needed for the process. Sorption chillers are also employable as heat pumps for heating buildings.

Figure 18: Using waste heat from industrial processes for cooling with ad- or absorption chiller.


Direct use of (residual) heat Often the temperature of waste heat is at the right level so there is no extra conversion needed. Residual heat from industrial processes for example, can be transferred directly into the district heating system. It is also possible to incinerate non-recyclable municipal solid waste, instead of sending it to landfills (Figure 19). Pruning and other woody materials (biomass) can be incinerated. The waste heats water into steam, and this heat is transferred into the district heating system. Some larger waste incinerators also have a steam turbine to produce electricity and heat. This is called cogeneration or combined heat and power (CHP) which also can be used with other energy sources. Electricity Plants are large CHP’s, often powered by fossil fuels and produce a large amount of heat that can be used in a district heating system. Instead of fossil fuels, a CHP also can use biomass or biogas (e.g. from manure or residual from sewage treatment) as a fuel.

Figure 19: Heat from waste, biomass or electricity production directly into the district heating system.

Solar thermal energy can be connected to the district heating system, often combined with a seasonal heat storage (Figure 20: Solar thermal connected to the district heating system). The heat is stored at higher temperatures (figure 21 and 22).

Figure 20: Solar thermal connected to the district heating system [District energy in cities, 2016].


Figure 21: Scheme of a heating system based on 800m2 solar collectors connected to a seasonal storage; near Attenkirchen in southern Germany

Figure 22: Near Attenkirchen in southern Germany


2 Mapping data specifications

2.1 Introduction The main functionalities of the PLANHEAT mapping module will be:

Mapping and quantifying H&C demand at city level;

Mapping and quantifying current and future H&C demand at district level;

Mapping and quantifying energy sources potential for low carbon H&C considering conventional sources (solar, biomass, geothermal), unconventional sources (sea, lakes, rivers, sewage, underground network, sewage network, data centers) and industrial waste energy (waste heat coming from industries, ports, waste incinerators and power plants).

With PLANHEAT a user or city is able to map heat and cold demand through 2 different approaches. A Top-down approach and a Bottom-up approach. It depends on the availability of data of energy consumption which approach is to be applied. The top-down approach is a relative simple one and can only be applied if data on energy consumption is available. It provides maps on city scale with annual data. The bottom-up approach is more elaborate and requires other types of data. It provides maps on district scale. This approach also makes it possible to estimate heat and cold curves per hour over a year and estimate future demand. This information can be used in the simulation module of PLANHEAT. For both approaches, algorithms will be available to give users the possibility to perform analysis at different levels of detail according either to the granularity of the available data or to their specific needs.

2.2 What is there to map in cities? There are different types of data that can be mapped in cities to achieve the desired mapping results: Input data for mapping demand at city scale with the top-down approach:

Aggregated energy consumptions (for heating and cooling, like gas use) per sector (residential and commercial are relevant sectors for PLANHEAT purposes) for the whole of the city.

Geospatial indicators to distribute the aggregated consumption over space, like o land use maps o population density maps o building characteristics

Input data for mapping demand at district scale with a bottom-up approach are presented in the following list and in figure 23:


Satellite-derived data:

o Heating Degree Hours (HDH)

o Cooling Degree Hours (CDH)

Information of the evaluated district. These data are based on the information defined by

the Cadaster or GIS of each City:

o Age of each building

o Use of each building

o Geometry of each building from the Cadaster/GIS: perimeter and floor area

o Number of floors of each building

o Geolocation of each building

o Geometry of each building from the Cadaster/GIS

Figure 23: Input requirements for mapping heat and cold demand at district scale (bottom-up approach(

The PLANHEAT Mapping module aims at providing software for mapping:

Yearly and hourly heating, cooling and domestic hot water (DHW) demand; current and

future demand and consumption for residential and tertiary sectors;

Energy potentials for heating and cooling supply from conventional, unconventional and

industrial waste sources available at urban and industrial level. The input data for the

mapping module depends on the mapping algorithm that is used and is typically a

combination of satellite-derived data, geographical data (GIS), key figures and statistical

information. The mapping algorithms can deal with different levels of data availability. Top-

down and bottom-up algorithms will be available respectively in the City Mapping Module

(CMM) and District Mapping Module (DMM).


The PLANHEAT mapping module is composed out of three functional parts:

The Graphical User Interface (GUI) for configuring the structure of the sectors demanding

heating/cooling and the technologies or sources for producing heating/cooling will be

created. The District Mapping Module does not have any GUIs;

A library with mapping algorithms for bottom-up or top-down allocation, mapping and

geographical explicit estimation of the heating, cooling, DHW production, demand,

consumption for the various points/diffuse producers/consumers at the chosen level of

detail;

A functionality to configure the structure of the sectors/technologies and to execute the

corresponding hierarchical computations at different levels of aggregation.

Other relevant input for the PLANHEAT platform is:

Solar radiation maps

Weather data

Subsoil temperature

Industrial waste heat

Local constraints that can limit the availability of the energy sources:

existing urban plans

specific techno-economic constraints

environmental conservation constraints

Already present energy infrastructures such as natural gas distribution networks, district

heating and cooling networks, etc;

Already deployed technology for heating and cooling production such as domestic or

centralized systems;

Already deployed system for heat recovery from waste heat production points.

Street patterns

Data related to H&C technologies:

Efficiency

Cost (installation, operation and maintenance)

Technology life time

Energy tariffs

CO2 taxes

Primary energy factors

2.3 General form of the mapping data – shape and size Data can come in different shapes and sizes.

- Data can exist out of single values or amounts that count for a specific total region.

Examples are values of solar radiation (kWh/m2), total energy consumption of gas (m3/yr)

or heating oil for a city.


- Data can be georeferenced, in that case, the values are connected to a location, so they

have a spatial aspect and are already in the form of maps. Examples are cadastral maps,

maps with existing heat and or cold grids or city sewage plans.

- Data can also have temporal resolution (a yearly aggregated value or set of hourly values

for the whole year)

2.4 Which data is crucial for mapping of the heating and cooling demand? In next graphs will be visualized how PLANHEAT uses different data sets for both approaches in order to provide heat and cold demand as an output. There are 3 different forms of data to be used by PLANHEAT to deliver an output module:

Data provided by PLANHEAT (grey)

Data with default values, by PLANHEAT, based on public available data and often in

relative low resolution; this can be updated by the user if higher resolution data is available

(blue)

Data that is required from the city/user (yellow)

Figure 24: Legend of different data sets Required data following the top-down approach: Required data by users following the top-down approach are:

Boundary of the region, city or district; In a vector map the desired area can be shown in a

cadaster map of the city

Building use; map that indicates what the use of each building is e.g. residential, health

care, office; Or indicates to which sector the building belongs e.g. residential, commercial,

industrial; Information can be found in cadaster maps

Energy consumption per sector for the area of interest; This information may be available

at the city and energy companies

With the use of next publicly available sets or types of data, PLANHEAT is able to provide maps of current energy demand; These maps can be upgraded by the user with city information if available:


A topographical map; showing buildings and street patterns; Originates from

OpenStreetMap

A map of land use; showing land use zones; originates from CORINE

A map of population density; showing land population density; originates from CORINE

Next graph visualizes the top-down approach.

Figure 25: Visualization of PLANHEAT top-down approach to map demand annually Required data following the bottom-up approach: Required data by users following the top-down approach are:

Boundary of the region, city or district; In a vector map the desired area can be shown in a

cadaster map of the city

Building gross floor area; per building; information must be provided by the cadaster

Building age and use; map that indicates age and what the use of each building is e.g.

residential, health care, office; Or indicates to which sector the building belongs e.g.

residential, commercial, industrial; Information can be found in cadaster maps

Number of floors; per building; information must be provided by the cadaster

With the use of publicly available sets or types of data and internal databases, PLANHEAT is able to provide maps of current energy demand also for individual buildings; The public available maps may be of relative low resolution and can be upgraded by the user if higher resolution data of the city is available. Next publicly available sets or types of data are used for input:

A topographical map; showing buildings and street patterns; Originates from

OpenStreetMap

Dimensions of building surfaces; this is based on national building typologies

Thermal transmittance properties of roofs, facades and opening


Finally, the PLANHEAT mapping module has several internal databases that provide requested information on:

Heat degrees hours for each city

Cold degree hours for each city

Climate zones for each location

Annual domestic hot water per gross floor area

Operating schedule for heat/cold/DHW demand

Internal gains for equipment etc.

Thermal losses from ventilation per building

Next graph visualizes the bottom-up approach.

Figure 26: Visualization of PLANHEAT bottom-up approach to map demand hourly

2.5 Which data is crucial for mapping the potential of local renewable energy sources? To be defined in a later stage in the process of developing the PLANHEAT tool.


3 Available databases

3.1 Possible issues on finding the needed data Certain issues may appear when looking for data such as:

o Some of the data isn’t publicly available or it is outdated

o Databases don’t cover specific geographical span

o An issue in finding data may be that data (on energy use) concern a certain area

where e.g. only a part of dwellings is connected to a central grid (e.g. a gas grid) or

if it concerns the use of heating oil in an specific area where buildings may be

heated by wood as well.

3.2 Presentation of the available databases All public databases used by PLANHEAT will be adapted for the PLANHEAT toolkit. No conversion of any kind is required to be done by users of the tool. Some of the most important publically available databases that PLANHEAT will use are:

OpenStreetMap (topography)

CORINE (land use and land coverage e.g. urban, agricultural, forest areas etc.)

GeoDH Europe (geothermal potentials)

Tabula (building typologies by country)

EUROSTAT (European statistics)

Figure 27: CORINE database: how land use and land coverage is divided through Europe.


The database of GeoDH Europe gives up to a certain detail information on existing geothermal aquifers, but also on which cities already apply district heat networks on geothermal energy or on other sources.

Figure 28: GeoDH Europe gives up to a certain detail information on existing geothermal aquifers. The Webtool TABULA provides for many European countries average energy use for heating for different residential building typologies from different year classes.

Figure 29: The Webtool TABULA with Italian typologies, for 8 year classes: 4 different dwelling

typologies are rated with energy use. Many more available databases exist that may be used for the tool. More publically available databases that may be used for the tool are presented in next list.


Type of data Source Reference Finest scale

Covering zone Description

Miscelleanous EUROSTAT EUROSTAT, 2014

Great cities EU28 Miscellaneous

Demographic URBAN AUDIT EUROSTAT, 2015

Great cities EU28 Demographical data for cities and city districts

UN UN, 2010 NUTS-3 EU28 Population counts, future projection

Spatial URBAN ATLAS EEA, 2014 Urban Zones

EU28 Geographical data for cities and urban zones

ArcGIS ESRI, 2014 Building EU28 Urban topographies and coordinate systems

OpenStreetMap OSM, 2016 Building EU28 Urban topographies and coordinate systems

CORINE 2012 EEA, 2012 District EU28 Land coverage and Land use classes

LUCA EUROSTAT, 2009

2x2km² EU28 Land coverage and Land use

NUTS EUROSTAT, 2014

NUTS-3 EU28 Administrative units in Europe (NUTS 2, 3)

GEOSTAT EUROSTAT, 2012

1x1km² EU28 Population density EU28 by kilometer-square

GHS population grid EUROSTAT 1x1km² EU28 Population grid

INSPIRE INSPIRE, 2014

City EU28 Miscellaneous topo-geographic data

European Land Use Map Eurogeo EU28 Land use maps

Energy ODYSSEE-MURE ODYSSEE-MURE, 2014

Country EU28 Energy use and population counts

World Energy Outlook IEA 2008-2016

Country EU28 World of energy outlook, Energy Balance 2008, 2016

ETSAP IEA, 2014 Equipment EU28 Technology data

Danish Energy Agency DEA, 2012 Equipment Danish Technology data

PETA HRE, 2017 100x100m EU14 Finest European thermal atlas

STRATEGO STRATEGO, 2016

Equipment EU28 Contribute to PETA (HRE) and H&C market analysis

EuroHeat&Power EHP 2013 Equipment

EU28 District heating and cooling systems

ISWA ISWA 2012 Equipment

EU28 Waste-to-Energy plants in Europe

CEWEP CEWEP 2014

Equipment

EU28 Waste-to-Energy plants in Europe

ENTRANZE EC, IEE programme

Building EU27 Total unit consumption per m2 in buildings

Mapping and Analyses of Current and Future (2020-2030) Heating/Cooling Fuel Deployment (fossil/renewables)

DG-ENER EU28 Final Energy Primary energy Useful energy Data related to H&C technologies

Photovoltaic geographical information system

JRC Europe and Africa Wind speed, Air temperature, Solar irradiation, PV production

Renewables ninja Stefan Pfenninger and Iain Staffell

World PV production and Wind turbine production

GeoDH Europe GeoDH Europe Geothermal potential


project, IEE programme

PETA 4 Heat Roadmap Europe 4 (HRE 4)

HRE 4 countries:AT, BE; CZ, FI, FR, DE, HU, IT, NL, PL, RO, ES, SE

Excess heat and heat demand

PETA STRATEGO project

EU28 Heat demand

Solar energy potential

Wood and straw resources

Geothermal potential

Urban TABULA TABULA 2014

Building EU28 Building typologies by country

BPIE BPIE 2014 Building EU28 Statistics on European Building stock

EU building Observatory EC Building EU28 U-value for building typologies

Average EU building heat load for HVAC equipment. Ventilation values

EC Building High U-value scope: DK, FI, NL, SE

Ventilation values (infiltration + active ventilation)

Middle U-value scope: AT, BE, FR, GE, IE, LU, UK

Low U-value scope: BU, CY, CZ, EE, GR, HU, IT, LT, LV, MT, PL, PT, RO, SK, SL, ES

Figure 30: List of publically available databases, useful for the PLANHEAT tool


3.3 Additional sources of information on national level Except presented available databases, national authorities could have some needed information. Examples of existing energy maps of energy use, demand and potentials Several websites exist that provide maps of cities or regions showing information on energy demand, energy use, energy networks, renewable and waste heat potentials. PLANHEAT’s mapping module’s goal is to provide certain maps for any given city. Some examples of existing maps will exemplify this. Map of gas use in Amsterdam Scrolling and zooming gives information on local gas use for different locations and scales in the municipality of Amsterdam.

Figure 31: Natural gas demand in Amsterdam (www.maps.amsterdam.nl)

Figure 32: Deep geothermal potentials in the south of the Netherlands (www.thermogis.nl)

http://www.maps.amsterdam.nl/


Map of deep geothermal heat potentials in the Netherlands On the website of www.thermogis.nl information on the sustainable heat potentials of deep underground (2-5 km’s depth) can be found indicating the suitability of geothermal plants (for heat only). The region around of Rotterdam and The Hague, in the Netherlands, have relative good potentials from deep geothermal wells. Some of only a few geothermal plants in the Netherlands are situated in this area, providing mainly heat for greenhouses. Many plans for constructing new plants exist, to provide sustainable heat to local existing heat grids. Map of aquifer thermal energy storage (ATES heat and cold storage) potentials in the Netherlands. On the website of www.warmteatlas.nl information on potentials of the heat storage capacity in the deeper underground (200-400 m depth) can be found.

Figure 33: Potential of ATES in the Netherlands (www.warmteatlas.nl) Map of residual or waste heat potentials in the Netherlands (www.warmteatlas.nl) On the website of www.warmteatlas.nl information on potential supply by waste heat of can be found. Sources of waste heat are often from industrial processes, but also waste heat from cooling processes are indicated, e.g. from cooling in supermarkets. Rotterdam has a large harbour with many industries present. Their processes provide large amounts of waste heat. Already some of the companies provide their waste heat to the district heating network of Rotterdam but most of the waste heat is being discharged in the river Maas.


Figure 34: Sources of waste heat in Rotterdam area in the Netherlands (www.warmteatlas.nl)

Map of existing district heat network in the Amsterdam region Next to maps show existing district heat and cold network in Amsterdam (www.maps.amsterdam.nl). When zooming in, the trajectories of the network are visible into detail.

Figure 35: Map of existing DHN in Amsterdam ( www.maps.amsterdam.nl )

http://www.warmteatlas.nl/



Figure 36: Zoom in of map of existing DHN and connections in Amsterdam (www.maps.amsterdam.nl)

Figure 37: Potential of the use of surface water in the Netherlands as a source for heating and

cooling (Deltares / IF)



4 References

Documents

Guidebook Training Module 1 Input data for mappingplanheat.eu/wp-content/uploads/2017/12/Guidebook... · 2017-12-12 · 1 Guidebook Training Module 1 Input data for mapping Version