Michiel Fremouw 2012 GIS based EPM
residual heat potential Delft University of Technology &City of Rotterdam
MUSIC: GIS based EPM and residual heat potential
November 2012
Author: M.A. (Michiel) Fremouw, BSc ([email protected])
supervisors Delft University of Technology: prof. dr. ir. A.A.J.F. (Andy) van den Dobbelsteen ([email protected]) prof. dr. ir. A. (Arjan) van Timmeren ([email protected])
supervisors Municipality of Rotterdam:
ir. N.J.M.D. (Nico) Tillie ([email protected]) drs. R.J.M.M. (Roland) van der Heijden ([email protected])
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3 Contents
PART I ...... 8
1. RESEARCH FRAMEWORK ...... 10 1.1. Background ...... 10 1.1.1. EPM ...... 11 1.1.2. REAP ...... 11 1.1.3. The MUSIC Project ...... 13 1.1.4. iGUESS ...... 14 1.2. Problem statement ...... 14 1.3. Objectives...... 14 1.4. Limitations...... 15 1.5. Research questions ...... 15
PART II ...... 16
2. ENERGY POTENTIALS IN IGUESS ...... 18 2.1. Solar potential ...... 18 2.1.1. Input ...... 18 2.1.2. Conversion ...... 19 2.1.3. Output ...... 20 2.1.4. Future development ...... 21 2.2. Wind potential ...... 22 2.2.1. Input: wind ...... 23 2.2.2. Input: area restrictions ...... 25 2.2.3. Conversion: individual turbine yield ...... 26 2.2.4. Output: collective yield ...... 30 2.3. Biomass potential ...... 32 2.3.1. Input ...... 33 2.3.2. Conversion ...... 33 2.3.3. Output ...... 34 2.4. Geothermal potential ...... 35 2.4.1. Input ...... 35 2.4.2. Conversion ...... 36 2.4.3. Output ...... 38 2.4.4. Future development ...... 38
3. RESIDUAL HEAT POTENTIAL ...... 38 3.1. Introduction to residual heat ...... 39 3.2. District heating components ...... 40 3.3. Framework for the residual heat module ...... 42 3.3.1. Focus on energy potentials ...... 43
4 3.3.2. System limits ...... 43 3.3.3. Sources ...... 43 3.3.4. Heating vs. Cooling ...... 43 3.3.5. Scale ...... 44 3.3.6. Feasibility ...... 44 3.4. Existing models ...... 44 3.4.1. Vesta (NL) ...... 44 3.4.2. OEI (NL) ...... 46 3.4.3. DESDOP (CH) ...... 47 3.5. Residual heat sources ...... 47 3.6. District heating: demand side ...... 48 3.7. Modeling residual heat ...... 49 3.7.1. Source ...... 49 3.7.2. Transport ...... 49 3.7.3. Distribution ...... 50
PART III ...... 51
4. EPM INTEGRATION ...... 53 4.1. Energy categories ...... 53 4.1.1. Fuels ...... 53 4.1.2. Electricity ...... 54 4.1.3. Thermal ...... 54 4.2. iGUESS module output ...... 57 4.3. iGUESS module exclusions ...... 57 4.4. Output aggregation: energy profiles ...... 58 4.5. Stepped calculations ...... 59 4.6. Visualisation ...... 60
PART IV ...... 64
5. CASE STUDY: ROTTERDAM CITY, SOUTHERN AREA ...... 66 5.1. Demand ...... 66 5.2. Geothermal potential ...... 67 5.3. Solar PV potential...... 70 5.4. Biomass potential ...... 71 5.5. Wind potential ...... 73 5.6. Residual heat potential ...... 75 5.7. Combined potentials ...... 79
PART V ...... 82
6. CONCLUSIONS AND RECOMMENDATIONS ...... 84
5 7. FUTURE DEVELOPMENT ...... 84 7.1. Industry ...... 84 7.2. Transport ...... 84 7.3. Energy storage ...... 84 7.4. Time ...... 84
BIBLIOGRAPHY ...... 86
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7
PART I PART I
research framework
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9 1. Research framework This section describes the existing methodologies and projects that form the basis for this project.
1.1. Background We do live in interesting times. Social, political and financial turmoil, peak oil and nuclear accidents all contribute to the realisation that perhaps things need to change. Although for some functions the world’s estimated 85% current reliance on fossil fuels (Cullen & Allwood, 2010) will be harder to replace, for many others present day renewable energy technologies can provide a viable alternative.
Figure 1 Global map of energy conversion efficiency (Cullen & Allwood, 2010, p. 2065)
Next to the more obvious climate mitigative effects of replacing the combustion of these fossil fuels with renewable alternatives, energy security (just 90 days of strategic oil reserve in the EU(2006/67/EC)) and the possible consequences of geopolitical strife for these dwindling supplies should be sufficient incentive to make this transition sooner rather than later. And as Cullen and Allwood also show, exergetic efficiency is quite poor, about 90% of (mostly fossil) primary energy being lost during transport and conversion for the intended final use. In other words, perhaps even today we do not have an energy problem, but an exergy problem.
Providing the right type of energy at the right place at the right time is of course the answer. As the potential yields of renewable energy sources tend to depend on local physical and climatological circumstances though, providing spatial information on these is crucial. iGUESS, one of the products of the MUSIC project thus aims to unlock these potentials in great detail, by using GIS information from the participating cities and calculation modules for various technologies (solar, wind,
10 geothermal and so on) as well as investigating demand reduction potentials, energy networks and more.
This study builds upon the EPM and REAP methodologies, in order to further develop and connect the various modules and allow the GIS component of the MUSIC project, known as iGUESS, to provide integrated energy potential information for a specified target area.
1.1.1. EPM In order to build a better, more sustainable built environment that swallows less (fossil) energy, it is important to take local available recourses into account when designing and making plans. The method of Energy Potential Mapping (EPM) has especially been designed to do this. The aim of EPM is to chart and quantify all different local potentials – on various scales, depending on the area of study – as clearly as possible. In this way the use of available potentials can even play a directive role in the design for urban patterns. Already in an early stage of the planning and designing process, the maps can contribute and be a helpful tool in locating different functions at the right positions on the urban and regional level and in elaborating their sustainable energy supplies. (Dobbelsteen, Broersma, & Stremke, 2011)
basic information topography climate underground land use energy system
Nature & Buildings Infra- energy sources sun wind water soil agriculture & industry structure
energy potentials
fuel
electricity and electricity storage
heat, cold and heat/cold storage
CO2 capture
interventions energy-based plan
Figure 2 EPM method
1.1.2. REAP The Rotterdam Energy Approach and Planning (Tillie, Dobbelsteen, Doepel, Joubert, Jager, & Mayenburg, 2009) introduces a spatial component to the New Stepped Strategy of reducing, reusing
11 and producing sustainably within the built environment, by considering its effects on various levels from the individual buildings to city level, and back.
CURRENT REDUCE UTILISE GENERATE PROVIDE situation the demand waste flows sustainably clean & efficiently
avoid energy re-use generate generate energy demand by waste flows renewable clean and efficiently with building archi-tectural on on the energy on the fossil resources inventory measures building scale building level on the building scale
Figure 3 New Stepped Strategy
Reducing the urban heat island effect for example will reduce summer cooling requirements for individual buildings, and conversely the excess heat captured by an individual building’s solar thermal collectors could be stored collectively and used elsewhere later. Furthermore, waste flows from industrial and other functions, at present often exhausted into the environment, may be well suited for reuse in an exergetically lower function. Exchanging, storing and cascading allow access to currently untapped potentials on many levels, and may thus greatly facilitate implementation of a fully renewable energy system.
12 REDUCE UTILISE GENERATE PROVIDE the demand waste flows sustainably clean & efficiently
avoid energy connect to generate generate demand by communal renewable energy clean city urban energy grid energy and efficiently with fossil measures centrally resources centrally
avoid energy exchange and generate demand by balance or renewable district urban cascade energy energy on the on the district measures district level scale
avoid energy exchange or generate cascade energy renewable neighbourhood/ demand by environ- on the energy on the cluster mental neighbour- neighbour- measures hood scale hood level
avoid energy re-use generate generate energy demand by waste flows renewable clean and building efficiently with archi-tectural on on the energy on the fossil resources measures building scale building level on the building scale
Figure 4 REAP method
1.1.3. The MUSIC Project
More than 70% of the European population live in metropolitan areas and over half of all CO2 emissions take place in urban zones. Mitigation in Urban Areas: Solutions for Innovative Cities brings together five European cities and two research institutes who cooperate and exchange knowledge on how to implement an energy transition in their own city, in order to reach the 20% greenhouse gas emission reduction and 20% renewable energy usage objectives that the European Union has set for 2020. Although the long term goal is using the practices and open source tools developed within the MUSIC project to enable international comparison of the achievements of cities and regions in Europe (MUSIC, 2010), the short term result of the current work packages will be to allow individual cities to determine their spatial distribution of energy demand and supply, and if possible connect these.
The Dutch government expects that the CO2 reduction target set for 2020 will be exceeded at the current pace, attributed to the built environment and horticulture (Verdonk & Wetzels, 2012, p. 7). Increased visibility of energy and emission reduction potentials towards stakeholders, who may or may not have a technical background, is thus quite necessary.
13 1.1.4. iGUESS The partner cities of the MUSIC project are in urgent need of more comprehensive geospatial data and additional IT tools to develop and implement their carbon-reductive planning and policies and to monitor the progress they make. This will be facilitated by the development Integrated Geospatial Urban Energy information and Support System (iGUESS), which will allow cities to: (Leopold et al, 2011)
- Compose urban energy maps, with information on energy cost, consumption and emissions, - Support urban energy planning by automated identification of potential locations, e.g. solar power generation, usage of geothermal energy, wind energy and heat exchange or public lighting efficiency, - Develop scenarios for innovative geospatial energy planning Optimise their energy infrastructure (e.g. district heating, gas- and electricity grid, lighting), - Perform `energy book keeping' for buildings and couple them to smart metering systems, visualise their CO2 emissions and reductions measures and identify opportunities for energy exchange, - Monitor the effects of the MUSIC project.
Priorities of external stakeholders
Common meetings/ framing Prioirities municipality as a questions participation/PSU (in Goals and ambitions and then use Targeting and prioritising the stakeholder (policy and Listing opportunities and threats, expertise investments and projects in a regulation) forming coalitions a certain area) certain area (Area Programmes)
Data/Information/Knowledge; Sustainability Sustainability paragraph Profile, MUSIC and GIS (WHAT, WHERE and WHY THERE)
Figure 5 Role of iGUESS within MUSIC
In short, iGUESS aims to facilitate the transition to the MUSIC project’s low carbon future by providing a GIS infrastructure, calculation methods and output in a web interface for specialists and policy makers alike, both to facilitate the planning stage and to monitor results.
1.2. Problem statement Performing an EPM study on a specified area can be quite time consuming, and incorporates residual heat sources only at a basic level, even though there is a significant spatial component to their effectiveness due to heat transport loss. The iGUESS web system brings the promise of greatly facilitating renewable energy potential calculations, however in its current state both combined energy potential assessment and residual heat potentials are not implemented.
1.3. Objectives - Apply the EPM methodology of combining energy potentials to the iGUESS system.
14 - Develop a residual heat potential method to be used in iGUESS as well as future EPM studies.
1.4. Limitations Even though MUSIC aims to influence the urban CO2 balance and include financial consequences of using potentials, the focus of this report will be limited to the energetic component in order to fit within the temporal constraints of a graduation project, as well as on the technical level (cfr. Figure 6).
in use
economical €
technical
physical
Figure 6 Energy potential levels. Physical potential is the theoretical maximum, technical potential is the maximum achievable at current technology levels, and economical potential is financially feasible. This study focuses on the technical potential level.
Furthermore, even though the initial intent was to include a much higher temporal resolution, both due to the variable nature of renewable energy sources (if possible even down to hourly calculations over a year) and their consequences for required storage capacity (at present not even included in iGUESS modules), the focus will also be on yearly total yields for this same reason.
1.5. Research questions - Most energy potential related iGUESS modules are currently still in the concept stage, others in early stages of development. Assuming current methods used within EPM and the availability of much more detailed geospatial data (GIS), how do these modules relate to one another (for example concerning spatial exclusions), and which output will they produce? - As heat loss plays a significant part in both technical and financial viability of using residual heat from various sources, how should this be modeled? - How can these energy potentials be combined to provide meaningful output at various scales, bearing in mind the REAP methodology?
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PART II PART II renewable energy in iGUESS
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17 2. Energy potentials in iGUESS Determining energy potentials generally follows the basic path of spatial input – conversion technology – energy potential, although the exact composition of each step tends to differ. The iGUESS system still being under active development, some modules are currently further along their development path than others. This chapter will describe both their functionality and output based on past EPM calculation methods, with the intent of both providing the ground work for EPM integration and facilitating development and refinement of the future iGUESS modules.
2.1. Solar potential The solar module is currently the only module in the running prototype stage, and calculates roof photovoltaic potential based on a 3D LiDAR model.
3D orientation conversion electric or thermal solar radiation + shading technology yield
Figure 7 Solar potential
2.1.1. Input The primary source for this module is of course the sun. Solar radiation power arriving at Earth fluctuates between 1321 W/m2 and 1412 W/m2, due to its slightly elliptical orbit. This extraterrestrial solar spectrum (ETS) is then reduced within the atmosphere by scattering (reflection back into space) absorption (spectral gaps), cloud reflection (also back into space) and occasionally increased by underside cloud reflection as the total of solar radiation arriving on one surface point is the sum of direct, reflected and scattered radiation (Paulescu & Badescu, 2012).
Module input consists of the amount of direct sunlight and global radiation at the location combined with a 3D LIDAR model of the area. The surface angle at which a PV panel is mounted can positively or negatively influence its conversion efficiency. Even though existing surface angles can be changed by means of an angular mounting frame or active tracking, a 3D model of an urban area as used in the current solar module can help calculate a more accurate PV baseline potential for an area. Shading from surrounding structures may also negatively influence PV yield for a surface and is included in the current solar model. By applying solar tracking the amount of radiation striking each roof surface can thus be determined, providing a highly detailed theoretical potential.
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Figure 8 Global horizontal irradiation for Europe (SolarGIS, 2012)
2.1.2. Conversion This calculated theoretical potential can then be converted into a technical potential by applying the efficiency rate of a chosen photovoltaic technology. Figure 9 pictures the best available technologies. Commercially available conversion rates (and accompanying price) can vary greatly and are significantly lower. The average conversion efficiencies of newly sold (and exported) modules by for example US manufacturers are 11% for Thin-film PV and 16% for Crystalline Silicon PV (EIA, 2012).
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Figure 9 Best photovoltaic research-cell efficiencies (NREL, 2012)
2.1.3. Output
The result will be an electrical potential in kWh or GJe. The existing prototype is furthermore able to assess ownership of each roof by using cadastral data, making it possible to identify for example housing corporations and collective ownership, which can facilitate application and speed up energy transition.
Figure 10 iGUESS Solar demo: combining photovoltaic yield with ownership
20 2.1.4. Future development Using the current 3D roof model, it is possible to include solar thermal potential with relatively little effort. These collectors have a thermal yield roughly three times that of photovoltaic ones, and are thus of interest in the temperate climate of North West Europe (but note that the small subset of concentrating collectors specifically requires direct sunlight as input as opposed to both that and global radiation).
Figure 11 Types of solar thermal collectors: unglazed, flat plate, evacuated tube and parabolic trough
Next to roofs, roads can be equipped with embedded solar thermal collectors, for which a rough potential can be determined using road surface area and the 3D model for shading, although roads are frequently paired with rows of trees which affects solar intake. A possible route to determine potential more accurately might be using a time series of detailed thermal scans on few (representative and extreme) days per season in order to determine the effect of shading on sections of road throughout the year. The same applies for land unsuitable for agriculture or other purposes, these may be used for large scale solar farms.
Figure 12 Vertical solar thermal collector in Ostrava-Marianske, Czech Republic (TiSUN, 2012)
Furthermore, a significant amount of vertical surface area may also be suitable within an urban environment (cfr. Figure 12), although being able to determine its potential is dependent on the ability to determine open and closed surfaces in the model, as well as shading and aesthetic wishes (as facades are much more visible than roofs).
21 Although the current 3D model should be capable of doing basic calculations on vertical potential, its top down origin (airborne scanner) limits the detail level, and will not take facade composition into account.
Google’s Street View project however deploys vehicles (cars, cycling trikes, hand trolleys) that combine regular photography with horizontal LIDAR scans. As Google dedicates itself to renewable energy (Google Green, 2012) and the MUSIC member cities have been covered, Google’s models might be available for use within iGUESS. The photographic data can then be used to determine facade suitability (for example by identifying homogenous surfaces). In order to include the higher levels of high rise buildings as well as rear facades, horizontal scans may additionally need to be performed by aerial vehicles though (either full sized helicopters or smaller and much cheaper UAVs).
Figure 13 Google Street View Car (Google, 2012)
Finally, although the Solar District Heating project(SDHtake-off, 2012) specifically focuses on solar thermal energy, their suggestions(Sørensen, 2012) to consider both suitably oriented sloped surfaces (hillsides, train track beds, dikes) and as roof tiles above car parks are valid for photovoltaic panels as well, the latter of course being particularly interesting when considering electric vehicles.
2.2. Wind potential
fp Prated + + + etc + = base map wind speed model existing turbines etc turbine specifications safety restrictions available area potential yield
Figure 14 Wind potential
Wind power has significant potential for the electricity supply: Lu et al. (2009) estimate that a globe spanning network of 2.5MW turbines could provide more than 40 times of the current worldwide consumption of electricity and more than 5 times of the total global use of energy in all forms by (Lu, McElroy, & Kiviluoma, 2009). These however are theoretical values, not taking into account limitations of for instance a physical, spatial, legal and social nature so the actual global potential will be significantly lower.
22 Although wind power provided may seem somewhat erratic on a short time scale, a study on long term wind patterns in the UK shows there is a weak but positive correlation to UK electricity demand patterns (Sinden, 2007).
Average Monthly Wind Power Capacity Factor 40% Long term average capacity factor of 30% 35%
30%
25%
20%
15%
Capacity Factor - Percent 10%
5%
0% Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Month
Figure 15 Monthly wind power availability in the UK (averaged over 34 years of wind data)(Sinden, 2007, p. 116)
Due to its coastal location Rotterdam is a promising area for wind energy, and as the wind module within iGUESS is at present not yet developed, this potential has been investigated in more depth, in order to arrive at a more accurate yield using GIS data.
2.2.1. Input: wind The basis for wind potentials is the annual wind speed average, for which regional and national meteorological institutes tend to have data available (for the Netherlands: Wieringa & Rijkoort (1983)).
This may however produce a very conservative estimation of available wind power, as an important property of wind is that its potential yield increases cubed in relation to speed:
= 1 3 푤 Equation푃 1 Relation2 휌퐴 between푣 wind power and wind speed (Masters, 2004, p. 312)
Where:
wind power [W]
푃푤 air density [kg/m3]
A휌 cross-sectional area through which the wind passes [m2]
23 wind speed [m/s]
As푣 average wind speed increases at increasing heights, this means that turbines mounted higher from ground level will yield significantly more.
Masters (2004, p. 314) thus warns against using year round average wind speed for yield calculations, and instead suggests using wind speed probability distributions (i.e. a few different wind speeds and the number of hours per year they occur), since that third power in both equations would mean using an average that may significantly underestimate available wind power. Figure 16 for example plots the wind speed regime and the mathematical average (of 4.5 m/s) for Rotterdam in 2011 against an indication of the amount of potential energy (simply represented by ,as and are assumed constant here). 3 푣 ∗ 푡 휌 퐴 ( = 4.5 m/s)
푣̅ t (h) 1600 3 푣 ∗ 푡 1400
1200
1000
800
600
400
200
0 v (m/s) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 16 Wind speed regime for Rotterdam over 2011 (at a station height of 4.5m), relative distribution superimposed (dimensionless on the vertical scale, shown for speed vs yield distribution comparison only) (KNMI, 2011) 푷풘 Higher speeds with far fewer hours can thus provide much more energy than the more ubiquitous low wind speeds (although turbines tend to only be rated for a specific range of wind speeds, for example 3.5 to 14 m/s; more on this in the output section). The actual potential yield in this example would be a quite significant additional 120% more than when using the average wind speed, and most of that at wind speeds manageable for most wind turbines (the only real limiting factor here being the aforementioned power rating, particularly with smaller turbines).
The influence of air density is also notable. The difference in mass of the same volume of air at 0°C and 25°C is 10%; in other words, a 0°C wind at the same speed provides 10% more power to a wind turbine than the same wind at 25°C (Masters, 2004, p. 316). As this would mean doing 8760
24 different calculation steps per city ( and changes do not necessarily correspond) as opposed to just 10-20 steps (wind speed range of 0-20 m/s, further explained in the electricity output section 휌 푣 below), it is probably more practical to consider this a later refinement step.
The data required is thus:
- Wind speed model (hours per year per wind speed per height) – an estimation can be made based on the average wind speed map, but this will likely be far less than the actual technical potential - Wind rose (to determine the shape of the minimum distance zone per turbine)
2.2.2. Input: area restrictions Various functions and objects may require minimum distances to wind turbines either due to risk of destructive failure, risk for wildlife, interference with radio waves (microwave relay paths, radioastronomy and meteorological, shipborne and airborne radar systems), civilian and military low level flight paths and more. Although specific regulations will likely differ internationally, a list of Dutch rules and guidelines was compiled by KEMA (Braam, Mulekom, & Smit, 2005). Furthermore, there may be acoustic and cast shadow discomfort limiting proximity to residential areas (Provincie Zuid-Holland, 2010, p. 24).
There may also be existing turbines in an area. More on the required minimum distance for these can be found in the output section.
Within GIS this effectively comes down to drawing minimum distances around specified objects and areas, and depending on whether these boundaries are legally required or relative guidelines, combining them to form an exclusion mask. The remaining area can potentially be used for wind turbines and should thus count towards the city’s wind energy potential.
In hilly and mountainous areas the terrain features will for obvious reasons be much more important, although a detailed wind speed model may be available that, like the one available for the Netherlands, will easily show the actual effect on wind speed behaviour. Although, with the exception of the effect of high rise in the centre of Rotterdam, the relatively flat landscape in the Netherlands is not a major influence on potential, turbines do tend to be placed in rows on dikes and along roads, presumably to minimise any adverse effects and maximise individual yield. However, as the total possible yield for an area as a whole might be much larger, at the expense of a lower yield per turbine, for EPM calculations which focus on total potential, this type of grouping can most likely be left out.
The data required is thus:
- Location of existing turbines, highrise, terrain features and other obstacles (may even be covered by a sufficiently detailed wind speed model or map as visible low wind speed ‘blots’) - Restriction mask based on safety related required distances (these may be different per country; note that the distances involved are dependent on the chosen turbine height and diameter)
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2.2.3. Conversion: individual turbine yield Wind turbines generate electricity by slightly slowing down the wind blowing through their swept areas, converting this into rotational movement and through a dynamo into an alternating electric current. Where larger wind turbines are concerned, the only commonly used type is the upwind horizontal axis turbine.
The power output of a single wind turbine can be calculated with:
= × × × × 1 2 3 푝 Equation푃 2 Power2 output휌 휋푟 from a 푓single푣 wind turbine (Lu, McElroy, & Kiviluoma, 2009, p. 10934)
Where:
intercepted kinetic energy [kW]
푃 air density [kg/m3]
휌 area swept by the rotor blades [m2] 2 휋푟 efficiency power factor
푝 푓 wind speed [m/s]
Wind푣 turbines do have a maximum rated power, beyond which they will adjust their pitch in order to reduce structural load, which means that for smaller turbines, strong winds may not fully count towards electric generation, and beyond its maximum wind speed the turbine will even fully furl. In other words:
Equation푃 3≤ Maximum푃푟푎푡푒푑 power output for a single wind turbine
If is not specified by the manufacturer, it can be determined by solving Equation 2 for vrated.
푝 In 푓the example of Figure 17, the wind turbine is rated at 600kW, has a minimum wind speed of 5 m/s and a maximum allowed wind speed of 20 m/s. Larger, higher rated turbines also tend to have correspondingly higher maximum allowed wind speeds, and can thus harvest more of the upper wind speed range.
26 Hours per y ear 1300 Output (kW) 1200 750 700 1100 650 1000 600 900 550 800 500 450 700 400 600 350 500 300 400 250
300 200 150 200 100 100 50 0 0 12 3 456 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 W ind s peed (m/s) Wind speed (m/s)
Figure 17 Annual energy calculation of a wind turbine (Burton, Sharpe, Jenkins, & Bossanyi, 2002, p. 513)
For the city level used within MUSIC, and assuming air density changes are not taken into account (cfr. the input section), this will only mean that the same area calculation has to be performed several times with the specified hour duration per wind speed, though. The equation for yearly energy output of a single turbine will thus be:
= 푣=푛 Equation푈 푡푢푟푏4 Annual ∑energy푣=1 푈 yield푣 of a single wind turbine
With:
= 8760 × × × × × × 1 2 3 푣 푝 Equation푈 5 Annual energy푡 yield2 from휌 a single휋푟 wind푓 turbine푣 at a given wind speed occurrence
And:
Equation푛 6≤ Turbine푛푚푎푥 maximum rated wind speed Where:
total annual yield for a single rotor [kWh]
푈 푡푢푟푏 wind speed [m/s]
푣 maximum measured wind speed [m/s]
푛 maximum rated wind speed for the wind turbine [m/s]
푛푚푎푥 annual yield at given wind speed [kWh]
푈 푣 number of hours at given wind speed [h]
The푡 high end of rated turbine power has reached the multi megawatt range now, going as far as 7MW for a recently introduced giant offshore model with 82m blades on a ~100m mast (Vestas, 2012). Lower mast heights will typically generate tens to many hundreds of kilowatts due to the decreased rotor area.
27 For this study, the calculations above have been aggregated into a single tool (Figure 18), which, based on rotor specifications and the local wind regime at turbine height, provides both the individual yield per turbine (useful when the potential turbine locations are known in ArcGIS), and an estimate yield per ha based on turbine cross spacing (more on this in the next section). Although the latter will be less accurate, it does allow for a quick estimation of potential wind power in an area.
Light green cells are input fields, part of the calculations are performed on a secondary tab. On the left half the wind profile is shown, on the right the turbine power and yield profiles are offset against the wind speed in m/s, and the areas below the input fields provide turbine and area yields.
It should be noted that the source for wind speed data as used in the calculation tool has unfortunately been taken offline since, although the required data can still be provided commercially.
28 location: Maasvlakte WGS84 latitude: 51.954634°NB WGS84 longitude: 4.026632°OL axis height from ground level: 90m
Wind speed Day % Evening % Night % class (7-19h) (19-23h) (23-7h) 1 1.8 1.8 1.3 2 3.6 3.7 2.5 3 5.7 5.1 4.5 WIND ENERGY PROFILE t (h) 4 7 6.6 5.7 1000 5 8.2 8.1 7.4 6 10 8.5 9.4 900 7 10.4 10.5 11.1 800 8 9.5 9.6 11.6 700 9 9.4 8.8 10.3 v^3*t 600 10 7.8 8.8 8.6 11 6.1 7 6.5 500 12 5.2 5.7 5.3 400 13 4 4.7 4.7 300 14 3.2 3.5 3.3 v^3*t 15 2.3 2.3 2.3 200 16 1.8 1.8 1.6 100 17 1.4 1.6 1.5 0 18 0.8 0.9 1.2 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 v (m/s) 19 0.8 0.5 0.6 20 0.4 0.4 0.3 21 0.3 0.2 0.2 22 0.1 0 0.1 23 0.1 0.1 0.1 24 0.1 0 0 25 0.1 0.1 0 26 27 28 29 30
Figure 18 Wind turbine calculation tool
29 2.2.4. Output: collective yield In order to convert the yield from individual turbines into an area potential, the turbines need to be separated by a minimum mutual distance in order to prevent the wake of one turbine (cfr. Figure 19) from significantly affecting air flow and thus decreasing the yield of another turbine.
Figure 19 Wind turbine wake simulation (NTUA)
A first order estimation of power output can be calculated by multiplying individual yield by the estimated number of turbines an area can occupy, taking into account that minimum distance.
× = 푥 푈푡푢푟푏 퐴 6 Equation푈 7 Energy10 yield per m2
Where:
total annual yield per m2 [kWh/m2]
푈 퐴 number of turbines per km2 [kWh]
푥Turbine density depends on the size of the turbines. The larger the turbine, the more distance is needed in order to optimise total wind park yield. Jacobson and Masters for example assumed a density of six 1.5MW turbines per km2 when calculating the theoretical case of 100% wind coverage of US energy needs (Jacobson & Masters, 2001, p. 1438).
A more refined estimation of wind potential however can be achieved by considering actual turbine placement. Restricting overall power loss to <20% requires a downstream spacing of >7 rotor diameters with cross-wind spacing of >4 diameters (Lu, McElroy, & Kiviluoma, 2009, p. 10934) (citing (Masters, 2004) and (Jacobson & Masters, 2001)).
This spatial envelope can easily be implemented within GIS using wind rose data (Figure 21). Considering the wind is prevailingly coming from the South West, for Rotterdam this would translate to a perimeter elongated in this prevailing wind direction (Figure 21), where its size is dependent on rotor diameter. If there is no clear prevailing wind direction however, it may be necessary to maintain a more circular perimeter.
30 N
330 030
300 060
25% 20 15 10 5 W 0 E
240 120
210 150 1-2 bft 2-4 bft S 5-6 bft 7 bft
Figure 20 Wind rose for Vlissingen, NL (Heijboer & Nellestijn, 2002)
Depending on the contours of local restrictions imposed, and bearing in mind that the (invisible) turbulence envelope can most likely extend far beyond these perimeters, this method will likely provide a much more accurate yield than a rough calculation based on an average number of turbines per km2 in an urban area with a possibly quite complicated set of rules.
Figure 21 Example of minimum distance between wind turbines of various sizes, given a prevailing wind direction
31
Figure 22 Calculating yield within a restriction envelope using actual optimised placement (left), as opposed to using averages
Calculating potential yield will just be a matter of drawing the cumulative restriction layer, choosing a type of turbine (for example 2MW) and determining how many dots fit in the resulting mesh, respecting a minimum distance from one another, and where in case of differing turbine sizes, the largest minimum distance is used.
2.3. Biomass potential
Figure 23 Basic biomass conversion principle
Biomass energy potentials revolve around harvesting plant matter and to a more limited extent animal matter (manure), and either using them directly as fuel or converting to energy carriers, in order to produce heat, electricity and motion. The resulting biofuels cover all major phases: solid, liquid and gaseous, their possible uses tied to both their energy content and phase.
32 Biomass Industry Agriculture Forestry Waste resources Food, fibre&wood Energy&short Forest harvesting & Landfill gas. Other process residues rotation crops. Crop supply chain. Forest & biogas. MSW incineration residues. Animal wastes agroforest residues & other thermal processes
Matching biomass supply&demands Traditional biomass: fuelwood charcoal&animal dung from for bioenergy, biofuels and materials agricultural production
Centralised electricity Liquid&gaseous biofuels Heating/electricity & Bio-refining, biomaterials, Bioenergy &/or heat generation for transport cooking fuels used on site biochemicals, charcoal utilization Energy supply Transport Building/industry Industry
Figure 24 From biomass resource to service (IEA, 2007, p. 11)
2.3.1. Input There are many possible sources for biomass, both rural and urban. Primary production can for example be from:
- Forests (for example willow plantations) - Algaculture (potentially high yield, but still experimental) - Other energy crops (corn, rapeseed, jatropha etc)
Although traditionally biomass potential was not associated with urban environments, the (primarily residual) sources in cities are ever often harvested actively . Typical secondary biomass forms are:
- Biodegradable household and industrial waste - Trimmings from public parks - Sludge from wastewater treatment facilities - Landfill gas - Manure (although more common in rural areas and generally not separately collected in cities)
2.3.2. Conversion The route from feedstock to end use can be quite diverse as well (cfr. Figure 25), the most suitable technologies depending on factors such as available feedstock, desired end use, local energy infrastructure (for example local gasification and a pipeline vs. transporting feedstock to a centralised plant), the necessity of energy storage (in a specific form) and requiring a uniform carrier (for example scrubbing biogas and changing its caloric content to match natural gas).
33 Heat Electricity Transport Cogeneration
Steam Gas Micro Fuel Spark Compression turbines turbines turbines cells ignition ignition engines engines
Synthetic Synthetic Methanol Ethanol Biodiesel gasoline diesel Bioenergy carriers Process Synthesis steam gas Refining
Steam boiler Bio-oil Esters
Direct Fermentation Flash Inter- Gasification combustion /distillation pyrolysis esterification
Pentose Conversion Lignin Glucose sugar processes Methane Acid or enzme hydrolysis (cellulase)
Landfill Lignin/cellulose complex Biogas gas Pre-hydrolysis acid or enzyme (hemicellulase) Animal Vegetable fats oils Anaerobic digestion Steam explosion Starch Sugar Meat Oil crops crops crops processing (oil palm, Ligno-cellulose (cereals, (cane, oilseed rape, potatoes) beet) sunflower, jatropha) Biomass Sewage Animal Wet Green feedstock sludge manures process crops wastes
Municipal Forest Wood Crop residues Vegetative Short solid waste arisings process (bagasse, Crops rotation (organic residues straw, (miscanthus, forest fraction) (sawdust rice husks, canary grass) (salix, shavings coconut populus, off-cuts) shells, eucalyptus) palm fibre)
Figure 25 Biomass conversion routes (IEA, 2007)
2.3.3. Output As this is likely the most diverse and complicated category of energy sources where spatial effects are concerned, their potential within previous EPM studies has generally been considered on the feedstock level, applying conversion and reduction factors in order to represent end use.
34 Fuel type Caloric value (MJ/kg) Domestic waste 9 - 11 Tree bark, resin free (dry) 17 - 23 Kindling (dry) 10 - 17.5 Pine needles 20 Grass, hay (dry) 15 Wood, birch (30% moist) 13 Wood, conifer (30% moist) 13 Wood, resin free (dry) 18 - 20 Wood, resin (dry) 22 - 23 Wood, deciduous (50% moist) 9.5 Wood, air dried (20% moist) 15.5 - 20 Wood, bark (dry) 20 Wood, trunks (dry) 19 Wood, branches (dry) 20 Wood, recently chipped (70% moist) 6 Wood chips (30% moist) 13
Table 1 caloric values of typical secondary biomass feedstock(Gemeentewerken Rotterdam, 2008, p. 7)
Furthermore, the main focus within iGUESS should be on residues. Energy crops may be included, but mostly for comparison as available arable land tends to be quite limited within urban areas.
2.4. Geothermal potential
extraction
sustainable aquifer + + well distribution heat-in-place thermal yield
recuperation
Figure 26 deep geothermal potential
Geothermal potential within iGUESS primarily focuses on vertical ground source heat pumps (GSHP) and so-called deep geothermal potential. As vertical GSHP implementation in MUSIC was described in great detail by Pierce (2012), this section will briefly investigate deep geothermal potential.
2.4.1. Input Geothermal module input consists of maps with suitable aquifers at various depths and their physical potential. Table 2 is an example of suitable aquifers that are present in the Netherlands. The thermal energy available at these depths results from a combination of radioactive decay in the earth’s crust (~80%) and heat flow from the planetary core (~20%) (MacKay, 2009, p. 97). Depending on the location and the geological composition of the crust, temperature roughly increases at a rate of 30°C per km.
35 stratigraphic super stratigraphic group ThermoGIS™ stratigraphic stratigraphic member ThermoGIS™ group group formation member code Carboniferous Limburg DC Tubbergen - DCDT Permian Rotliegend Upper Rotliegend RO Slochteren (Upper-)Slochteren ROSL_ROSLU RO Lower-Slochteren ROSLL RO (Upper-)Slochteren and Lower RO-STACKED Slochteren stacked Lower Germanic trias TR Volpriehausen Lower Volpriehausen Sst. RBMVL TR Upper Volpriehausen Sst. RBMVU TR Detfurth Lower Detfurth Sst. RBMDL TR Upper Detfurth Sst. RBMDU Upper Germanic Trias TR Röt Formation Röt Fringe Sst. RNROF TR Upper & Lower – Volpriehausen TR-STACKED Sst., Upper & Lower Detfurth and Röt Fringe Sst. Stacked Lower Cretaceous Rijnland KN Vlieland Sst. Rijswijk, Berkel, Ijsselmonde, de Lier KNWNB stacked* KN Friesland KNNSF KN Bentheim KNNSP KN Gildehaus KNNSG
Table 2 Aquifers represented in ThermoGIS (TNO, 2010, p. 19)
2.4.2. Conversion Deep geothermal heat extraction technology consists of drilling into a suitable aquifer at two separate points, with a heat exchanger transferring the heat to a secondary circuit, after which the cooled down brine is returned through the second well. This results in a cold front at the injection well which slowly radiates out. After a number of years this cold front has reached the extraction well, and when its output temperature has dropped to a certain point, the well is considered depleted and the aquifer will need to recuperate for a period of time, absorbing geothermal flux and thus warming back up. 1.000 - 5.000m - 1.000
70
60
infiltration well
50 60
70 production well 1.000 - 1.500m
Figure 27 Deep geothermal heat extraction
Technical potential thus depends on two factors: recuperation time and well distribution.
36
+5
production period recuperation period
0 ΔT ] K -5 e change [ r tu a
e r -10 p em T
-15
-20 0 10 20 30 40 50 60 Time [years]
Figure 28 GSHP production vs recuperation (Rybach, 2007, p. 3)
The recuperation principle is demonstrated for a GSHP system by Figure 28. After a well has cooled down a certain amount after 30 years production is stopped, after which geothermal flux increases well temperature, fast first, slowing down more and more later until about 95% of the original heat- in-place is reached. At this point the well area can be used again, as the required time to get to 100% reaches infinite due to the ever decreasing temperature difference. Table 3 provides a comparison of individual well lifespan, recuperation time being a multitude of this life span. An infinite life span means extraction does not exceed geothermal flux, a limited life span means heat –in-place is being extracted at a greater rate.
CO2 reduction resource energy depth power scale (houses) CAPEX (compared to gas) / COP life span shallow GSHP heat / cold 2-150 m 25 GJ/yr 1 € 10k 20% / 4-6 infinite
shallow aquifer heat / cold 30-150 m 0.1 MWth 100 € 100k 20% / 4-6 infinite
deep aquifer heat 1.5-3.5 km 7.5 MWth 3000 € 6M 70-95% / 15 30-75 yr
deep aquifer faults heat / electricity >4 km 20 MWth / 3 MWe 8000 / 4500 € 30M 100% 30 yr
Table 3 Comparison of geothermal resources in the Netherlands (TNO, 2010, p. 5)
Previous EPM studies have applied a recuperation multiplier for deep geothermal potential of five, in other words, in order to be able to continuously provide heat at 90°C with wells that would last 30 years each, five wells will be required in the same area. Well distribution finally is harder to pinpoint as local crust composition, failed drillings and other factors may reduce the number of wells that may be drilled in an area. This will require a more detailed geological investigation though, assessing data from existing drillings as well as new ones if they are too far and few apart.
To attain a rough determination of geothermal potential, the method developed by TNO for their ThermoGIS application (TNO, 2010) will provide sufficient information for now (and has in fact frequently formed the basis for geothermal potential in past EPM studies). ThermoGIS both provides aggregate potential and that of individual layers at various depths. As the temperature gradient is
37 30°C per km and these suitable layers may be quite far apart, each layer should be considered individually so the available energy potential can be combined with a temperature range.
2015 ? MRH m a t ched t o demand
RH: R e c o v e r able e c onomically r e c o v e r able He a t t od a y
PRH: Po t e n tial R e c o v e r able He a t t echnically r e c o v e r able
he a t in subsu r fa c e HI P : He a t I n P la c e 2007
Figure 29 ThermoGIS methodology (TNO, 2010, p. 35)
There may be several suitable aquifers at different depths, thus providing their heat-in-place at different temperatures. Extracting the equivalent of geothermal flux (i.e. fully renewable heat) is possible, but will, due to the costs involved, most likely not be considered economical within a reasonable time frame. Kreith & Yogi Goswami (2007, pp. 26_5-26_14) discuss the economics and renewability of geothermal energy in more detail.
2.4.3. Output Both GSHP and deep geothermal deliver thermal energy, which means the well extraction temperature needs to be known in order to assess its impact on demand.
Furthermore, in case of deep geothermal, more than one aquifer may be suitable at the same area, and if these are sufficiently far apart depth wise, their output should calculated separately because of significantly different temperatures (for example 1 GJ at 60°C from a 2.000m deep aquifer vs 1GJ at 90°C from a 3.000m deep layer at the same location).
2.4.4. Future development Deep geothermal heat may have a high enough temperature to additionally be capable of electricity and even cold generation (adsorption), providing additional possibilities for energy supply.
3. Residual heat potential Due to the presence of a vast potential of currently underutilised residual heat within both the Rotterdam harbour and industrial areas in other urban regions, and the difficulties involved in transporting heat from source to sink, this section provides the base work for a proposed residual heat module.
38
Figure 30 Rozenburg waste incineration plant, currently providing residual heat (Warmtebedrijf Rotterdam)
3.1. Introduction to residual heat The purpose of this module is to visualise the geospatial potential of waste heat sources using existing and future district heating networks. In other words, show their potential ‘striking distance’, uncover opportunities to connect them to demand areas and due to their generally high temperatures, present cascading opportunities.
Due to the physical phenomenon of heat transport occurring over a temperature gradient (Fourier’s law), any heat carrier will dissipate its thermal energy into the environment until equilibrium is reached. The effective distance over which heat can be transported is limited because thermal insulation can only slow down the process towards equilibrium . Transport loss tends to be noticeable compared to the heat capacity of the most common carrier, water, and quite significant even at large distances. Heat distribution therefore tends to be an urban phenomenon, regional at most.
Residual heat potential may be considerable in urban areas with industrial activity, even though it also tends to be vastly underutilised. A CE Delft study on waste heat in the Dutch Rijnmond area (which includes the municipality of Rotterdam) estimates a total potential available for export of 1274 MW for hot water, and a further 653 MW of even higher quality heat (as high-pressure steam).
39 Although the study identifies various bottlenecks regarding utilisation of this potential, they consider local and national government to be highly important actors in overcoming these bottlenecks, partly by “localising and taking stock of heat demand and organizationally bringing together demand and supply. Furthermore, government is also the director of the planning processes necessary for large scale heat supply and an intermediary between the various parties involved”. (CE Delft, 2002, pp. 9,43)
3.2. District heating components In its most basic form, a heat network consists of a series of insulated dual pipes (Figure 31) using a heat carrier (usually water or steam) in order to transport heat energy from a heat source to a sink. The depleted heat carrier is then transported back to the heat source through the second pipe.
100cm
160cm
Figure 31 Typical district heating pipe pair as used in Rotterdam (Warmtebedrijf Rotterdam)
The source may be purpose-built for heat production (for example a geothermal well or a cogeneration plant) or provide ‘waste’ heat as a by-product (conventional power plants, waste incineration, industrial processes).
40
Figure 32 Heat buffers at the Amsterdam Waste Energy Works (Afval Energie Bedrijf, 2010)
A heat buffer (Figure 32) may be present at the source in order to ensure a continuous supply of heat from otherwise intermittent sources (example: the Waste Energy Works in Amsterdam).
Figure 33 District heating project currently under development in Rotterdam (Warmtebedrijf Rotterdam)
In order to maintain pressure in longer networks and/or divide heat streams over multiple pipes, transfer stations further down the network include pumps and possibly heat exchangers. Furthermore, they may be equipped with auxiliary boilers that provide a temperature upgrade for particularly long networks (the so called booster stations in Figure 33).
Finally, users tend to be hydraulically separated from the transport network with a distribution station (heat exchanger), which transfers the heat to a secondary distribution network and into the buildings themselves.
Heat networks can make use of multiple sources, and industrial waste heat could be applied as a transitional source for a future geothermal powered heat network, in order to spread expenses, as both laying heat network pipes and drilling geothermal wells comes with considerable expense.
41 Where residual heat potential is concerned however, the distance travelled between source and sink is more important than including all elements. As the path of least resistance will be following the route of the district heating network, this will rarely be a straight line from source to sink. Both the distances of the connections between two nodes, their individual insulative properties and the local temperature difference will have to be considered in order to calculate the delivery potential for a paired heat source and sink.
Figure 34 Difference between the geographical route that has to be followed to connect building k to building k’ (dashed line k-k’’-k’), and the shortest distance geometrically (solid line k-k’) (Weber & Shah, 2010, p. 1295)
3.3. Framework for the residual heat module The main goal of the underlying research regarding the residual heat module within iGUESS is to provide a methodology to determine the geospatial potential of (both existing untapped and new) concentrated heat sources using GIS, which will be usable regardless of the temperatures, capacities or general network layout involved, and preferably be capable of working with a wide range of underlying data resolutions, depending on what’s available. This potential will have to be supplied in such a way that it can be used in combination with the other EPM layers in order to determine total potential.
Several boundaries will apply, in order to ensure both visible, incremental progress (cfr. the development phase chapter) and allow for a useful end product within the allotted time.
42 3.3.1. Focus on energy potentials As with the other modules, the focus of this module will initially be on energy, as opposed to cost or
CO2 reduction. As the generated potential maps will contain various elements and energy streams that both cost and CO2 generation can be attached to, it should be possible to provide insight in these areas as well.
The emphasis is also on visualising potentials, rather than provide a complete simulation tool. Although it should be possible to calculate estimates as to what percentage of heat demand can be supplied by district heating networks and their sources, a fully renewable energy system consists of far more than district heating (both demand and supply side, for example co- or even polygeneration). For full system simulations there are many far more capable expert calculation tools available, and the purpose of this module is rather to make the potentials of (waste) heat sources and district heating visible for a broader audience than is the case at the moment.
3.3.2. System limits The border between what is part of the distribution network and what is part of the delivery system may be unclear in some cases. The aim is to stop at the doorstep and (at least for this module) disregard anything that could be considered demand side. The demand will be considered in a separate module.
A second important system limit is the spatial boundary used. Although Rotterdam’s city limits could be considered convenient organisationally, the CE Delft study (2002, p. 12) shows that the vast waste heat potential in Rotterdam’s industrial area may find its biggest users in the greenhouse areas to the north, south and most importantly east of Rotterdam, even though these would be outside of the city limits proper. The GIS methods developed here are universally applicable though, and the investments required will likely mean that regional proximity of demand and supply concentrations should be leading.
3.3.3. Sources Many different types of heat (and cold) sources are possible. The focus for the development phase of this module however will be on high temperature waste heat sources, keeping in mind that a large percentage of current housing is not well (or even not at all) insulated and using conventional high temperature radiators, and the large number of high temperature sources (power plants, waste incineration plants and other industry) currently untapped.
3.3.4. Heating vs. Cooling Part of the thermal demand of the built environment, especially for offices, healthcare, retail and industrial locations, may be for cooling, and a changing climate may result in this demand increasing. There are quite a few similarities between transport of heat and cold, but there are also major differences (for example the carrier used, and impact on potentials associated with preventing freezing issues).
As the MUSIC project initially covers North West Europe with its cool temperate climate, the cooling demand, at least within the residential and commercial sectors, is quite small compared to its heating counterpart (IEA, 2008, p. 530), the focus here will be on heating, having a far larger impact on total heat demand.
43 3.3.5. Scale For the same reason (high temperature industrial sources with a large waste heat capacity), initially only larger district heating networks will be included. As the underlying physical principles can simply be scaled however, it should be possible to investigate smaller networks as well in the future.
3.3.6. Feasibility Although it will be possible to study the energetic feasibility of a potential new district heating network, there may be practical issues that this module does not take into account, as the intent of this module is to visualize potentials rather than flesh out detailed projects. CE Delft (2002, p. 38) for example mentions two issues with delivering waste heat from Rotterdam’s industrial area to the Westland greenhouses: not just the number of greenhouse square meters decreasing in coming years, but also because the prevalence of narrow roads and numerous ditches makes it difficult (or expensive) to lay the actual pipes.
3.4. Existing models Although the norm for modeling of energy systems seems to be to use aggregate numbers and (in some cases) focus on introducing a detailed temporal component (which is advantageous and quite necessary to study the required peak supply as well as any storage components of a heat network), only few include a spatial component in their calculations (but not necessarily in their output) which may be of use for the district heating module. It should be noted that the more detailed models seem to be geared towards a full blown simulation rather than visualizing potentials though, requiring them to spatially aggregate into a giant single area, in order to reduce calculation time. As the focus of the GIS based iGUESS system is very much on spatial potential, these are not further explored.
3.4.1. Vesta (NL) Vesta (Wijngaart, Folkert, & Elzenga, 2012) (Folkert & Wijngaart, 2012) (Leguijt & Schepers, 2011) was developed by CE Delft for Dutch research agency Agentschap NL, in order to assess a sustainable heat supply for the built environment in the Netherlands as a whole, with a strong focus on cost. “The Vesta model is a decision making support model for policy makers. It is not an optimalisation model that ‘automatically’ calculates the most cost effective route towards a low carbon built environment. It also isn’t a simulation model capable of determining the most likely future.” (Wijngaart, Folkert, & Elzenga, 2012, p. 28) As the model is quite extensive and both describes and factors in a vast number of actors involved in heat generation, supply and use (included elsewhere in this report), the focus in this section is mainly on their modeling approach for heat transport.
44 PC4 Distance d1 area1 Heat Distance d2 source Distance d3
PC4 area 2
Figure 35 Schematic representation of the connecting of heat supply areas (Leguijt & Schepers, 2011, p. 28)
Vesta condenses both supply areas and transport nodes into the centres of gravity of the areas used. PC4 in Figure 35 refers to a slightly coarser spatial resolution of the (six digit) Dutch postal code system, a means of dividing areas which is also frequently used in statistics (and thus a practical basis for including data from other sources).
Figure 36 Schematic representation of components in the heat distribution network (Leguijt & Schepers, 2011, p. 30)
Although the availability of the actual layout of heat networks in GIS, and the expected relative ease with which (globally traced) new pipes can be included, will result in a more accurate representation of spatial potential for a given source within or near the city, the centre of gravity method employed here will likely be advantageous for the demand side model, as a representation of the size of the distribution network and thus its accompanying loss, cfr. also Figure 36. Which components are to be included here depends on the chosen resolution, which, depending on the available sources, may be as fine grained as single housing blocks.
45 3.4.2. OEI (NL) The OEI or Optimal Energy Infrastructure model (Hoiting & Nuiten, 2011) was developed by W/E Adviseurs, also for Agentschap NL, and aims to investigate energy infrastructure related investment choices for building sites of 250 or more dwellings. As with Vesta, it is intended to calculate energy effects as well as investment cost and additionally CO2 savings, and is modeled from the demand side rather than the supply side (thus less suitable to visualize supply potential).
OEI contains a heat transport calculation module (pp. 30, 33, 41-43) which seems to limit itself to a single supply side temperature of 90°C, and thus focuses on the cost for heat demand from the specified zone (in GJ/yr), either from sources within the zone or imported externally. It does include energetic expenses for transporting the required heat though, and is capable of considering cogeneration, i.e. the simultaneous production of heat and electricity by a single source. The following equations are used:
= , , , , , 퐸푤 푒푧 , , 푊푉푤푡 푒푧 푊푉푤푡 ∗ �퐸푤 푒푧 + 퐸푤 푒푧 표푣푒푟푖푔+ 퐸푤 푙표푐 표푣푒푟푖푔� Equation 8 heat transport loss for an energy zone within the OEI model
Where:
, = heat transport loss for the energy zone [GJ]
푤푡 푒푧 푊푉 = total heat loss heat for the transport pipe [GJ]
푤푡 푊푉, = heat energy demand of the energy zone [GJ]
푤 푒푧 퐸 , , = heat energy demand of the other energy zones in this location [GJ]
푤 푒푧 표푣푒푟푖푔 퐸 , , = heat energy demand of the other locations [GJ]
푤 푙표푐 표푣푒푟푖푔 퐸
And:
=
푤푡 푤푡 푤푡 Equation푊푉 9 heat transport푤푣 loss∗ 퐿for the pipe network within the OEI model Where:
= total heat loss of the heat transport pipe [GJ]
푊푉푤푡 = heat energy demand of the other locations [GJ/km]
푤푣푤푡 = length heat transport pipe [km]
The퐿푤푡 single temperature and focus on the demand side make the supply side temperature that’s fed into the district heating network the variable, which is the opposite of calculating potentials, making the heat distribution part of the OEI model less useful for the district heating module.
46 3.4.3. DESDOP (CH) The purpose of the District Energy System Design and Optimisation (DESDOP) tool is to define the mix of technologies that will best meet the energy service requirements of a small city, thus combining both the consideration of available energy services with a perspective situated at the district level (Weber & Shah, 2010, pp. 1293-1294). The smaller target scale also makes it possible for DESDOP to both have a spatial and a temporal component. Similar to the Vesta model, DESDOP represents some of the more difficult to model demand (particularly in the retail and services sector) by aggregated nodes, although transport connections and residential buildings are modeled geographically more accurately, the latter being represented by their node connecting to the network.
The model also includes cost calculations, and the temporal component makes it possible to assess whether supply and storage are able to continuously provide for demand even in peak load situations.
Green space (no consumption attached) Nodes representing buildings Aggregated nodes Intersection node Plant node
Figure 37 Heat distribution network for the optimised district energy system of a case study location in the UK (Weber & Shah, 2010, p. 1302)
3.5. Residual heat sources Heat can come from many different sources at varying temperatures. Some will be purpose built to provide this heat; others produce heat as a non competitive by-product, and will currently most likely be emitting it into the surrounding environment. Typical sources include CHP plants (which may run on many different fuels and can be freezer to factory sized, and includes conversion of regular power plants (Bartnik & Buryn, 2011)), waste incineration plants (examples: AVR Rozenburg in Rotterdam, Afval Energie Centrale in Amsterdam), deep geothermal wells, oil refineries
47 ((Samuelsson, 2010)) and many more. Although utilisation of small scale, neighbourhood level sources might be feasible, the initial focus here will be on large scale and high temperature sources.
(AS FROM 2013) MAASVLAKTE 2 MAASVLAKTE EUROPOORT EUROPOORT BOTLEK PERNIS ROTTERDAM CITY
CHEMICALS, BIOFUELS AND EDIBLE OILS
OIL AND OIL PRODUCTS
GAS AND POWER, COAL AND BIOMASS
UTILITIES
Rotterdam
Dordrecht
Moerdijk 0 1 2 3 4 5 km Leased out
Figure 38 Port of Rotterdam’s industrial locations (Port of Rotterdam Authority, 2010)
3.6. District heating: demand side Temperatures used in heat networks vary, and both depend on what’s available at the source as well as what is needed on the demand side. For the latter, the following are mentioned:
Country Tsupply Treturn Thotwater Denmark 70 40 <60 Finland 70 40 55 Korea 70 50 55 Romania 95 75 Russia 95 75 50 United Kingdom 82 70 65 Poland 85 71 55 Germany 80 60 55 Netherlands* 90 70 60 70 50/40 50 40/30
Table 4 examples of temperatures for the design of central heating systems (Skagestad & Mildenstein, 2002, p. 53) and *(Roos & Manussen, 2011, p. 7) / (Dobbelsteen, Wisse, Doepel, Dorst, Hobma, & Daamen, 2011)
Supply side temperatures of 120°C are common in Rotterdam (CE Delft, 2002) and international literature mentions temperatures as high as 160°C (Poredoš & Kitanovski, 2001, p. 2167). On the demand side, well insulated homes using radiant space heating may even require supply temperatures as low as 40°C (Dobbelsteen, Wisse, Doepel, Dorst, Hobma, & Daamen, 2011). However, this will require an alternative means to increase temperature for tap water heating, as the tap water circuit regularly needs to heat up above 60°C throughout in order to prevent legionella bacteria growth (NEN, 2011, p. 15). An alternative would be to use a heat pump in order to locally uprate the temperature delivered by the district heating network for tap water use.
48 The temperature used will therefore most likely be a trade-off between what a source can supply over which distance, and what can (or should) be generated locally. Using a higher temperature for example may allow more uses at the target location, but it will also increase heat loss per delivered joule, whereas using a lower temperature will require some local uprating or generation, but also reduce the percentage of heat loss in the transport network and thus be exergetically more efficient.
The temperature used within the transport network will therefore be dependent both on what the source can deliver, the length of the network as well as the desired end uses and technologies deployed, and within the GIS map should be specified at the heat source.
3.7. Modeling residual heat Although an assessment of economical potential will require including a more detailed district heating network including buffers, booster stations and other components, technical potential can be assessed with just three components: the source, the transport network and the distribution network (cfr. Figure 39).
SOURCE TRANSPORT DISTRIBUTION
loss fa ctor Uinput Uoutput
Figure 39 Transport model
3.7.1. Source Residual heat potential for a given source largely depends on the (production) process involved and the type of installation, and the amount of recoverable heat will likely be only part of the total thermal exhaust. If a local assessment study is not available, a first order estimation may be possible by comparing similar installations and scaling their known thermal output to match production capacity of the local source. The same goes for the available temperature, although industrial sources tend to provide residual heat temperatures high enough for all types of domestic demand.
3.7.2. Transport The route followed determines network length and thus needs to be known. Transport pipes are generally laid where they can easily be accessed for maintenance purposes, for example at the sides of main roads, taking into account the presence of existing underground infrastructure and other local obstacles. A map of potential transport network routes can thus be generated, which can be used to draw extensions on a map of the existing transport network in order to unlock new areas. The available capacity of the existing network is not relevant at this stage (which only assesses if source and sink can be matched), but in order to assess economical potential this does need to be known as it may require laying extra capacity along an existing route.
49
Figure 40 Detail of an urban suitability map for transport pipes, an application like ArcGIS Network Analyst may be used to plot the shortest route between source and sink
Transport heat loss is represented as a percentage of roughly 0.25% per km (both T and U, where T is offset against the year round average temperature). Although applying a linear loss may be inaccurate at long distances, including more detailed, non linear transport capacity calculations to increase accuracy would also needlessly complicate GIS implementation. The method described here will for now suffice for this module, whose goal after all is to simply represent large scale residual heat delivery potential in a city level area.
3.7.3. Distribution The final component is distribution. Even though the same transport pipes used for bridging large distances may be used to get near the user, the smaller diameter distribution pipes that branch off from the main network and into the buildings will result in a higher loss within the distribution area. Warmtebedrijf Rotterdam uses a fixed loss percentage of 20% (also both T and U) for the distribution part of the network in the planning stage (Warmtebedrijf Rotterdam, 2012). Although a particularly low building density will negatively affect this loss percentage, current practice is to limit heat distribution to denser urban areas in order to justify construction cost, so the main factors will be total heat demand per area and the temperatures needed (for example “10 GJ at 90°C and 5GJ at 50°C”).
50
PART III PART III
EPM integration
51
52 4. EPM integration Combining the various energy potentials into a concise overview that allows assessing local and city wide impact requires defining the types of energy output they have.
4.1. Energy categories In order to be able to combine the output of the modules described in a meaningful way, the type of energy produced has to be specified. Furthermore, if source and sink are geographically separated, there will be a transport cost and/or energy loss involved. Energy demand can be separated into three major categories: fuels, electricity and thermal.
electricity e
fuels S L G phase / eeq
thermal temp ranges
Figure 41 energy categories
4.1.1. Fuels The first category, and the most versatile (exergetically, i.e. usefulness), is fuels. Combustible materials come in many different shapes and forms (from crude oil to wood to cow dung) but can generally be reduced to three categories: solid, liquid and gaseous. Fuels can be converted in electricity and heat and depending on the type, may even provide both transport and heavy industry.
Solid fuels will most likely be transported by road, train and ship (and in very rare cases by conveyor belt), so homogeneity and thus a uniform energy value per content is not a major concern.
Liquid and gaseous fuels however may in some cases be transported by pipeline, and in the case of gas even form the primary carrier (within the fine-meshed Dutch natural gas network). As a homogenous energy carrier will be expected on the demand side and transport loss here depends on energetic content (pumping around a kg of something will have a fixed cost, so a higher energy content in that kg will mean a lower energetic transport cost per MJ), it may be beneficial to mix and convert carriers at the source to match. The average caloric value for natural gas in the Netherlands is 35.17 GJ/m3 (GasTerra, 2012), and any biogas mixed in will be concentrated or reduced to that value1.
1 About 14 million m3 of landfill gas is converted and injected this way, the total Dutch potential is estimated to be 5.000 million m3 (Harmsen & Harmelink, 2007, pp. 78,79).
53 Transport of biomass over longer distances may require a significant amount of energy in itself, certainly where low-caloric sources are concerned (for example fresh grass clippings with >50% water content (Spijker, Elbersen, Jong, Berg, & Niemeijer, 2007, p. 45)). Energy consumption for wood chipping and transport of ligneous biomass to a CHP plant however won’t be more than a few percent of its energy content if the distance is less than 20 km, which covers most urban areas (Koop, Luning, & Pouwels, 2010, p. 29).
4.1.2. Electricity The energy category of electricity is fairly straightforward, as devices using it expect 230 V/50 Hz and the supply chain is aimed at providing this. Although there will likely be future issues with network capacity (for example individual house connections due to local production and electric vehicles), these are outside of the scope of this report.
Assuming the presence of a modern and well maintained network, transmission and distribution loss for electricity generally is quite limited, below 10% nationally (World Bank, 2012). Losses in high- voltage AC overhead transmission lines amount to 15% per 1000 km at 380 kV and 8% per 1000 km at 750 kV. High-voltage direct current (HVDC) can reduce this further to about 3% per 1000 km (IEA, 2008, p. 405).
Although exact figures were not found within the time frame for this thesis, losses at the meso (city) level will be somewhat higher as the lower voltages used here increases resistance, but still considerably lower than heat transport and distribution related losses.
4.1.3. Thermal The last (and, exergetically speaking, lowest) category is thermal energy. Heat and cold account for a large amount of current primary energy consumption, for the Netherlands about 40% (1220 PJ heat and 50 PJ cold) (Schepers & Lieshout, 2011, pp. 19,20) (Harmsen & Harmelink, 2007, p. 15). Most of this heat demand requires relatively low temperatures (<100°C) and could thus be provided by means other than locally combusting natural gas at ~1400°C.
700
600 >1.000 °C 500 750-1.000 °C 400 500-750 °C 300 250-500 °C 200 100-250 °C 100 <100 °C 0 Industry Households Agriculture Commerce
Figure 42 heat demand in the Netherlands in 2006 (Schepers & Lieshout, 2011, p. 16) (citing (CBS, 2009))
54 1.6 liquid hydrogen tank 1.4 -160 °C 1.2 1.0 built 0.8 environment
or E/Q [ - ] kiln (brick) t 0.6 c chemical reactor 1200 °C 0.4 600 °C gyfa oven (kitchen) e r 0.2 200 °C E x 0.0 0 1 2 3 4 5 T/T0 [ - ]
steam (humidification) steam 100 °C (disinfection) 120 T galileistr hot water 120 °C (cooking / cleaning) HVAC (heating) 100 °C 20 °C hot tap water Lighting 0.3 equipment HVAC 70 °C (cooling) freezer 15 °C -20 °C 0.2 environ- ment 90 T rad,old or E/Q [ - ]
t 10 °C c 0.1 fridge
gyfa 5 °C e r E x 0.0 0.9 1.0 1.1 1.2 1.3 1.4 70 T rad,new
T/T0 [ - ]
Figure 43 emperature ranges (and exergy factors) in the built environment (Vaan, 2007, p. 20)
Although the initial intention was to create a predetermined set of temperature ranges 40 T LTV that would be suitable for the built environment, it appears that both the widely diverse
range of temperatures that various sources provide (or even that a single source may 26 T room,s provide, cfr solar thermal), the diverse range on the demand side (radiators at 90°C and 23 Th,summer 20 T room,w 70°C, low temperature radiant heating at 50 to 30°C and the ability to use heat pumps at even lower supply temperatures) as well as the, for large scale residual heat 10 T utilization, quite significant transport loss factor, will make this quite difficult.
0 Tl,winter Figure 44 (on the right) example of temperature ranges 6 Tfridge For lower temperature heat and cold there effectively is a sliding scale where the categories are dependent on local circumstances, with a few key temperatures, for
example the average minimum in winter ( ), the average maximum in summer -24 Tfreezer ( ) and the year round mean temperature ( ). As buildings have to maintain 푇�푤푖푛푡푒푟 their room temperature against these temperature, they’re an indication of the usability 푇�푠푢푚푚푒푟 푇� of ambient thermal sources especially when compared to different seasons (thus showing thermal storage potential).
55
power generation conventional power plant cogeneration plant pulp and paper mill breweries chemical extraction wool washing cloth drying industry oil recovery concrete curing sludge digestion heap leaching copper processing fish farming soil warming food processing vegetable and fruit dehydr agriculture greenhouses stable and breading group cereals and fodder heat pumps domestic hot water air conditioning district and radiant panels space heating radiators snow melting swimming pools spa treatment balneotherapy
0 50 100 150 200 250 300 temperature °C
Figure 45 Lindal diagram: Heat generation and applications by temperature range (Hurter & Schellschmidt, 2003, p. 782)
For higher temperatures where transport will likely play a role, temperature will drop when heat is transported from source to sink. As cascading possibilities may result in much more efficient use but will also complicate calculations (compared to a fairly straightforward 120°C source -> 90°C sink -> 70°C return -> 50°C arrival at source), it will therefore be better to specify both supply and demand values as x Joules at y °C, also taking into account the gradual replacement of high temperature radiators with low-temperature radiant heating systems. Furthermore, specifying return temperatures (and energy amount) after use on the demand side will make cascading opportunities visible.
required:
- 100x 31 GJ at 90°C (HT space heating) - 100x 24 GJ at 50°C (LT space heating) available: - 200x 9 GJ at 65°C (tap water) 300TJ at 120°C
deliverable at available: specified range: - 100x 20 GJ at 70°C (HT return) 230TJ at 90°C - 100x 10 GJ at 30°C (LT return) - 200x 3 GJ at 40°C (tap return)
56
4.2. iGUESS module output
OUTPUT cold heat fuel electricity H L L M H G L S MODULE ~230V 50Hz SOLAR PV x thermal x x WIND 30m x 100m x GEO GSHP x aquifer st x deep x x x BIO trimmings x x organic waste x x x x energy crops x x x DISTR heat x x
Table 5 Types of energy output for the MUSIC modules
4.3. iGUESS module exclusions A significant feature of EPM is the ability to assess mutual exclusivity, in other words how an area may be used in multiple ways that make it unavailable to other technologies. The modules featured within the MUSIC project mostly don’t exclude one another, with the exception of biomass and solar when considering vacant land and dedicated energy crop production. Trimmings and organic waste for example are by-products and as such do not claim square meters, as they are rather the result of existing processes and space uses.
57 WIND SOLAR BIO DISTR GEO PV 30m heat deep GSHP 100m thermal trimmings energy crops
MODULE organic waste WIND 30m 100m x SOLAR PV thermal x BIO trimmings organic waste energy crops x x DISTR heat GEO GSHP deep
Table 6 Area use exclusions between modules
4.4. Output aggregation: energy profiles Energy potentials can be aggregated into building and area profiles, an example of which is Figure 46. The profile will have two sections: demand and supply/return.
DEMAND e (MJ) e (kWh) e (aeq) T (°C) function 32,356.4 8,987.9 920.0 - electr, heat pump etc S 0.0 0.0 0.0 - L 0.0 0.0 0.0 - G 2,813.6 781.6 80.0 - cooking 21,453.7 5,959.4 610.0 +70 space heating 12,309.5 3,419.3 350.0 +60 tap water 540.0 150.0 15.4 -24 refrigeration TOTAL 69,473.2 19,148.1 1,960.0
SUPPL/RET e (MJ) e (kWh) e (aeq) T (°C) function 13,320.0 3,700.0 378.7 - roof solar panels S 0.0 0.0 0.0 - L 0.0 0.0 0.0 - G 0.0 0.0 0.0 - 15,324.1 4,256.7 435.7 +50 space heating return 6,154.8 1,709.7 175.0 +30 tap water sewage TOTAL 34,798.8 9,666.3 989.4
Figure 46 Energy profile for a single building
The demand section is fairly straightforward, and depending on use and the local energy mix can be split up even further. The energy demand for cooking for example, currently mostly filled by natural gas in the Netherlands, could also be switched over to electric depending on what will be available.
58 The supply / return section has two functions, both for producing entities (cogeneration plants, energy crop fields) and in order to facilitate cascading, as waste flows will likely have use for other functions. This is most apparent for district heating, where the return flow of an old fashioned 90°C radiator is still at 70°C, a temperature that has numerous uses within the built environment.
In order to keep the dataset to a manageable size (as well as catering for possibly limited information on building properties and energy use), demand and supply profiles can be specified, for example “1960s terraced house” or “1970s apartment building”. The number of these buildings per city block, postal code area or neighbourhood can in turn be aggregated into a demand in MJ per temperature.
Aggregating results in building or area profiles will also account for local generation, for example the use of roof solar panels or in case of dedicated energy production, the amount of biofuel a field can produce and the amount of residual heat at a specified temperature that an industrial source can provide. These profiles provide the basis with which city wide energy potentials can be determined, and opportunities visualised.
4.5. Stepped calculations Determining the energy potentials on a city level consists of three steps. First those energy potentials that are generated locally, and / or have noticeable associated transport losses, as well as local demand, are calculated per cell (for example building blocks, neighbourhoods and districts). Residual heat sources are included as well as significant transport loss means local application allows more energy to be used from a particular source.
The second step considers city level energy potentials, in this case (large) wind and heat transport networks, either determining city wide potential or projecting the energy networks over the individual cells in order to allow matching demand and supply at the final step.
As mentioned in the wind module section, the relatively small physical footprint of large wind turbines combined with placement vs. yield optimisation, area restrictions (that may exclude entire neighbourhoods) and very limited transport losses for electricity, makes it more beneficial to consider this to be a city wide resource. If, despite their limited yield, small (building level) wind turbines are considered, they should of course be included in the first step as local resources.
The third, final step compares available energy potentials with the (reduced) neighbourhood demand profiles to determine whether a potential energy export is possible or import is required, and can be expanded upon to provide an energy based plan.
For other scales it may be beneficial to arrange calculations differently. On an individual building or small neighbourhood level for example demand and potentials may be calculated simultaneously and then combined to see net energy levels, and on a larger, regional scale it may be more beneficial to consider fuel production to be a local phenomenon more focused on processing or cogeneration plant placement. For the energy potentials currently considered however (PV, wind, woody biomass, GSHP and deep geothermal), transport losses on the city level are only significant where heat is concerned.
59 4.6. Visualisation The demand and supply profiles of an area can be combined into a visual diagram which will show both deficiencies and export options. As with the Heat Maps project (Broersma, Fremouw, Dobbelsteen, & Rovers, 2010), sinks are visualised as contours and sources partially or completely fill them. The thermal scale is an interpolated graph on its side, where width equals energetic content and height the accompanying temperature.
Figure 47 demonstrates this. The horizontal axis represents the amount of energy, the vertical axis represents the type, and in case of thermal energy, the temperature. Adapted from the visualisation in the heat maps project is the representation of demand as hollow bars, and supply either filling these partly or exceeding them with the coloured bars (thus representing export potential).
S L G 125
75 25
-25
Figure 47 demand and supply profile diagram
Individual potential maps can be combined into a map stack which provides a quick overview of available potentials. Furthermore, demand and supply for each energy type can be offset against one another to create a relief map, showing geographical concentrations of demand and supply and uncovering possible opportunities for transfer.
60
61
Figure 48 Energy potential stacks (Broersma, Fremouw, Dobbelsteen, & Rovers, 2010) and (Broersma, Dobbelsteen, Grinten, & Stremke, 2009)
The ability to generate maps on the fly however will make it much easier to create tailored maps in order to visualise hidden patterns. The aforementioned energy poverty map for example combines energy use with household income, and can provide insight in how to direct initiatives and funds that help those who need it the most. The same applies to the thermal section of the energy profiles: if an area has a demand profile that is evenly spread over the temperature range, cascading might provide significant energy reduction there.
62
Figure 49 example of net energy 3D relief map
63
PART IV PART IV
case study
64
65 5. Case study: Rotterdam city, southern area In order to test EPM aggregation, demand of dwellings in the southern part of urban Rotterdam has been investigated, comparing estimated demand with energy potentials derived from solar (PV), biomass (clippings from public parks and green areas) and geothermal (deep). As restrictions for large wind turbines may prevent placement in this area and electricity transport losses are quite limited, wind potential is investigated for Rotterdam as a whole.
Figure 50 Case study area within the municipality of Rotterdam (enlarged version in the annex)
The case study area for local potentials covers the southern part of the city proper (south of the Meuse, west of the Waalhaven), and contains 32% of Rotterdam’s households.
5.1. Demand The BAG (Basisadministratie aan Adressen en Gebouwen, basic administration for addresses and buildings) contains the age of each building. Using this data, an assumption was made for buildings of 1980 and before to require heating at 90°C, those built between 1980 and 2000 to require heating at 70°C (i.e. improved insulation) and those built after 2000 to require heat at 50°C (i.e. radiant heating). Although this may not fully correspond to the actual situation (for which data was not at hand), this will provide a rough spatial distribution of dwellings with varying demands, for the purpose of demonstrating the new EPM heat profile.
66 2267
4227 3418 2133
2380 3225 4391 7122 6417 1061 5898 6769 1166 5060 7235
7594 15980
630 553
8080
6457 7636
city limit 306 neighbourhoods roads <1980 water 1980-2000 terrain >2000 1:40.000 km 0 0.5 1 2
Figure 51 rough estimate of heat demand, based on building age
heat demand e-demand
TJw/yr TJw/yr TJw/yr TJe/yr (at 90 °C) (at 70 °C) (at 50 °C)
Kop van Zuid 0117 3 3 27 21 Kop van Zuid-Entrepot 1079 56 5 6 40 Vreewijk 1080 78 291 19 71 Bloemhof 1081 62 263 13 63 Hillesluis 1082 48 195 16 51 Katendrecht 1085 29 39 28 21 10 Feijenoord Afrikaanderwijk 1086 62 106 17 39 Feijenoord 1087 74 79 4 30 Noordereiland 1088 13 73 10 18 Oud-IJsselmonde 1283 60 50 33 37 Lombardijen 1284 34 345 19 73 Groot-IJsselmonde 1289 74 69 81 40
Beverwaard 1290 12 IJsselmonde 250 17 0 59 Charlois Zuidrand 1570 4 7 2 0 Tarwewijk 1571 16 230 14 53 Carnisse 1572 24 281 4 52 Zuidwijk 1573 39 272 38 63 Oud-Charlois 1574 63 270 23 63 Wielewaal 1575 0 29 0 5 15 Charlois Zuidplein 1576 24 31 0 9 Pendrecht 1577 15 247 44 58 Zuiderpark 1578 26 10 12 8 Heijplaat 1593 10 43 7 10 1,063 2,956 417 883
Table 7 Demand (dwellings)
5.2. Geothermal potential Geothermal potential is derived from the potentially recoverable heat for spatial heating in ThermoGIS.
67 stratigraphic stratigraphic group ThermoGIS™ stratigraphic stratigraphic member ThermoGIS™ suitability in super group group formation member code target area Carboniferous Limburg DC Tubbergen - DCDT - Permian Rotliegend Upper Rotliegend RO Slochteren (Upper-)Slochteren ROSL_ROSLU - RO Lower-Slochteren ROSLL - RO (Upper-)Slochteren and Lower RO-STACKED - Slochteren stacked Lower Germanic trias TR Volpriehausen Lower Volpriehausen Sst. RBMVL v TR Upper Volpriehausen Sst. RBMVU ~ TR Detfurth Lower Detfurth Sst. RBMDL ~ TR Upper Detfurth Sst. RBMDU ~ Upper Germanic Trias TR Röt Formation Röt Fringe Sst. RNROF v TR Upper & Lower – Volpriehausen TR-STACKED - Sst., Upper & Lower Detfurth and Röt Fringe Sst. Stacked Lower Cretaceous Rijnland KN Vlieland Sst. Rijswijk, Berkel, Ijsselmonde, de Lier KNWNB v stacked* KN Friesland KNNSF - KN Bentheim KNNSP - KN Gildehaus KNNSG -
Table 8 Aquifers represented in ThermoGIS and their suitability in the target area
The three layers of interest are the Lower Volpriehausen Sandstone (RMBVL, ~3.1-3.7 km), Röt Fringe Sandstone (RNROF, ~3-3.5 km) and the Vlieland Sandstone (KNWNB, ~0.7-1.6 km) members. Their potential is shown in Figure 52.
GJ/ha/yr 149 - 198 198 - 248 248 - 297 297 - 347 347 - 396 396 - 446 446 - 495 495 - 545 545 - 594
Figure 52 Aggregated geothermal potential
68 T (°C) at 3000m 88.0 - 89.5 89.5 - 91.0 91.0 - 92.5 92.5 - 94.0 94.0 - 95.5
Figure 53 Temperatures at 2000m depth
renewable avg pt. recoverable heat total temp range (°C) avg temp (°C) ha GJ/ha/yr TJ/yr 3000m (est) 3000m 0117 Kop van Zuid 26 525 13.7 89.5 - 91.0 92 1079 Kop van Zuid-Entrepot 73 250 18.3 89.5 - 91.0 92 1080 Vreewijk 201 475 95.5 91.0 - 92.5 93 1081 Bloemhof 80 483 38.7 89.5 - 92.5 94 1082 Hillesluis 89 333 29.7 89.5 - 92.5 94 1085 Katendrecht 56 500 28.0 89.5 - 91.0 92 10 Feijenoord 1086 Afrikaanderwijk 48 300 14.4 89.5 - 91.0 92 1087 Feijenoord 63 250 15.8 88.0 - 91.0 93 1088 Noordereiland 24 350 8.4 88.0 - 91.0 93 1283 Oud-IJsselmonde 195 242 47.1 89.5 - 92.5 94 1284 Lombardijen 260 433 112.7 91.0 - 95.5 98 1289 Groot-IJsselmonde 578 233 134.9 91.0 - 95.5 98
12 IJsselmonde 1290 Beverwaard 149 200 29.8 89.5 - 94.0 96 1570 Charlois Zuidrand 0 483 0.0 92.5 - 94.0 95 1571 Tarwewijk 83 500 41.5 89.5 - 92.5 94 1572 Carnisse 62 500 31.0 91.0 - 92.5 93 1573 Zuidwijk 153 417 63.8 92.5 - 94.0 95 1574 Oud-Charlois 135 433 58.5 91.0 - 92.5 93 1575 Wielewaal 21 317 6.7 91.0 - 94.0 96 15 Charlois 1576 Zuidplein 26 525 13.7 91.0 - 92.5 93 1577 Pendrecht 123 483 59.5 92.5 - 94.0 95 1578 Zuiderpark 495 500 247.5 91.0 - 94.0 96 1593 Heijplaat 41 500 20.5 91.0 - 92.5 93
Table 9 Geothermal potential
Although these values are aggregate values for any and all suitable aquifers, the vast majority of potential is concentrated in the first two layers at roughly 3km. Based on the temperatures at 2000m depth as reported by ThermoGIS, 30°C was added in order to approach extraction temperatures and account for the difference in depth, resulting in an average well temperature of 94°C (cfr. Figure 53 and Table 9).
69 Geothermal potential appears to quite modest, but it may fulfil about a quarter of the area’s current heat requirements, and a much larger share if the buildings in the area are better insulated. As the extracted temperature is lower than 90 °C, heat pumps may be required for dwellings with conventional radiators in order to be used for spatial heating.
5.3. Solar PV potential As the 3D PV module within iGUESS does not yet cover all of Rotterdam, solar potential has been calculated based on roof area present, modified to represent potential for flat and sloped roofs. Based on existing data, the area consists of 39% flat roofs and 61% sloped roofs. A PV efficiency rate of 16% (cfr. solar module section) was used. Furthermore, the PV yield for flat roofs has been reduced to 90% to take into account obstacles (skylights, chimneys, rims etc) and that of sloped roofs to 45%, taking into account the reduced yield of unfavourably oriented roof sections.
Figure 54 Roof area
When comparing PV yield to electric demand there are discrepancies on the neighbourhood level, but for the area as a whole PV yield potential appears to be roughly equal to electricity demand.
Considering the significant heat demand in the area, it may however be more useful to (at least partially) deploy solar thermal collectors. Assuming yield will triple using vacuum tube solar thermal collectors (a figure used in previous EPM studies), and output temperature is on average above 70 °C, using thermal collectors will nearly cover the 70°C range of heat demand. Aquifer thermal storage potential, as well as solar thermal yield and heat demand in winter, will need to be investigated further in order to determine viability over a year, however.
70 roof area flat roofs sloped roofs PV roof (ha) (TJ/yr) (TJ/yr) potential (TJ/yr)
0117 Kop van Zuid 27.8 20.7 28.2 48.9 1079 Kop van Zuid-Entrepot 19.2 14.3 19.4 33.7 1080 Vreewijk 6.8 5.1 6.9 12.0 1081 Bloemhof 4.4 3.3 4.5 7.8 1082 Hillesluis 18.0 13.4 18.2 31.7 1085 Katendrecht 8.4 6.2 8.5 14.7 10 Feijenoord 1086 Afrikaanderwijk 34.8 25.9 35.3 61.2 1087 Feijenoord 27.3 20.3 27.6 47.9 1088 Noordereiland 12.4 9.2 12.5 21.8 1283 Oud-IJsselmonde 22.4 16.7 22.7 39.4 1284 Lombardijen 43.7 32.6 44.3 76.8 1289 Groot-IJsselmonde 27.1 20.2 27.4 47.6
12 IJsselmonde 1290 Beverwaard 3.4 2.5 3.4 6.0 1570 Charlois Zuidrand 73.2 54.5 74.1 128.7 1571 Tarwewijk 22.5 16.8 22.8 39.6 1572 Carnisse 16.3 12.1 16.5 28.6 1573 Zuidwijk 6.5 4.8 6.6 11.4 1574 Oud-Charlois 14.3 10.7 14.5 25.1 1575 Wielewaal 13.7 10.2 13.9 24.2 15 Charlois 1576 Zuidplein 31.1 23.2 31.5 54.7 1577 Pendrecht 20.6 15.3 20.8 36.2 1578 Zuiderpark 26.9 20.1 27.3 47.3 1593 Heijplaat 35.8 26.6 36.2 62.9 516.9 384.9 523.2 908.1 TJ/yr
Figure 55 PV potential
5.4. Biomass potential Although there may be many potential primary, secondary and tertiary sources possible within urban and industrial areas, the biomass potential of clippings from public parks and green areas was chosen here for its geographical spread and limited present day use.
In order to estimate local yield (mass), the amounts reported by Gemeentewerken Rotterdam (2008) were adapted for use with the municipal land use categories used within GIS. These categories were sorted either as ligneous (35.188 ton city wide) or garden waste (4.395 ton city wide), and yields per category per neighbourhood subsequently derived from either of these amounts. Grassy areas were considered to provide no yield, as the clippings of these public areas tend to be left in place as fertiliser.
71
Figure 56 Biomass potential: public parks and green areas
Energy potential was then calculated with the estimated caloric value and yield (mass) per category per area. In order to consider usefulness of this potential within the confines of buildings (requiring electricity and heat), a conversion ratio to 20% electricity and 60% useful heat was applied based on small scale cogeneration. As biomass inherently provides a means of energy storage, this may help cope with fluctuations in electricity and/or heat supply.
As can be expected considering the source, potential from clippings from public parks and other green areas, this form of biomass has a limited impact on demand. The ability to either feed specific areas with local CHP plants however, or tie these into a larger (ring) district heating network, combined with the inherent storage potential of biomass, may this worth considering though, more so when other local sources of biomass are considered (for example household biodegradable refuse).
72 20% 60%
yield (TJ/yr) CHP (TJe/yr) CHP (TJw/yr)
0117 Kop van Zuid 0.0 0.0 0.0 1079 Kop van Zuid-Entrepot 1.1 0.2 0.7 1080 Vreewijk 2.1 0.4 1.3 1081 Bloemhof 0.3 0.1 0.2 1082 Hillesluis 0.6 0.1 0.4 1085 Katendrecht 0.3 0.1 0.2 1086 Afrikaanderwijk 0.4 0.1 0.2 1087 Feijenoord 18.6 3.7 11.1 1088 Noordereiland 6.0 1.2 3.6 1283 Oud-IJsselmonde 0.0 0.0 0.0 1284 Lombardijen 6.9 1.4 4.1 1289 Groot-IJsselmonde 0.0 0.0 0.0 1290 Beverwaard 4.9 1.0 3.0 1570 Charlois Zuidrand 6.1 1.2 3.7 1571 Tarwewijk 0.5 0.1 0.3 1572 Carnisse 0.3 0.1 0.2 1573 Zuidwijk 1.5 0.3 0.9 1574 Oud-Charlois 1.9 0.4 1.1 1575 Wielewaal 0.1 0.0 0.1 1576 Zuidplein 0.2 0.0 0.1 1577 Pendrecht 2.8 0.6 1.7 1578 Zuiderpark 21.5 4.3 12.9 1593 Heijplaat 1.6 0.3 1.0 78.0 15.6 46.8
Figure 57 Biomass potential (clippings from public parks and green areas)
5.5. Wind potential Small wind turbines may be possible in dense urban areas (albeit having a potentially very low output), large wind turbines however are not possible for the most part. As mentioned in the wind module section, depending on the location, restrictions related to safety may be in place. Furthermore, the effect of Rotterdam’s highrise on wind speed at 100m is quite significant (cfr. Figure 58), making it, especially in the context of Rotterdam’s coastal areas, less suitable even when strictly yield is concerned. The distance between the windy production area in the west of Rotterdam and the chosen demand location in the east however is not an issue as electricity can be transported over this distance with minimal losses. This is why large wind turbines are considered more regional potentials, able to cover discrepancies in local generation and consumption of electricity.
73
Figure 58 Average wind speed at 100m in the Rotterdam region (SenterNovem, 2005, pp. 24,25)
For this case study 6MW turbines with a rotor diameter of 126m are considered, resulting in a minimum distance of 463m to any buildings in the area. Due to calculation times involved this is a rough representation, as the minimum distance is different between for example dwellings and offices, and infrastructure like pipes with dangerous substances, existing wind turbines or restrictions related to naval radar interference are not included. The resulting available area is displayed in Figure 59, combined with the average wind speed at 100 m (in order to establish wind speed regions for calculation purposes).
avg wind speed at 100m exclusion areas
6.0 - 6.5 6.6 - 7.0 roads 7.1 - 7.5 7.6 - 8.0 water 8.1 - 8.5 8.6 - 9.0 km 1:170000 0 1 2 4
Figure 59 Wind speed regions and restrictions related to buildings
For each wind speed area the average yield per ha is calculated, this number divided by 2 in order to take into account any further restrictions and arrive at a conservative potential. For a more precise yield, individual placement of turbines should be considered instead of an aggregate number per hectare, as described in the wind module section.
74 U/turb/yr area yield total yield location area (ha) avg wind 100m (GWh/yr) (TJ/ha/yr) (TJ/yr) 1 254.1 6.5 tot 7.0 10.7 6.2 1,569.9 2 363.2 7.0 tot 7.5 11.5 6.6 2,410.4 3 546.3 7.5 tot 8.0 13.2 7.6 4,167.9 4 679.7 8.0 tot 8.5 14.1 8.1 5,521.3 5 777.7 8.5 tot 9.0 14.7 8.5 6,604.9 6 629.5 9.0 tot 9.5 15.6 9.0 5,638.4 3,250.5 ha 25,912.8 TJ/yr correction factor* 50% TOTAL: 12,956.4 TJ/yr
Table 10 Wind turbine yield (*applied to take into account any further restrictions)
Even with the presently applied reductions, electrical potential is quite significant. The combination of a coastal location, low number of interfering obstacles (high rise) and sparse number of buildings result in a high yield. It should be noted that for proper grid integration, measures need to be taken in order to balance out differences in demand and supply at each hour of the day, either by directly storing the yield from these wind turbines (offshore hydroelectric basin) or by controlling output of other power sources (for example a biogas turbine). Combined with tailoring hourly industrial electricity demand to wind availability as well as additional offshore wind farms, the Rotterdam wind energy potential may be able to provide significant electricity export opportunities to the national grid.
5.6. Residual heat potential In order to determine potential first the potential sources need to be identified. Data on these turned out to be difficult to find, however a legal obligation for industry to register harmful emissions (the so-called “milieujaarverslag”, environmental yearly report) allows for some basic insight into the spatial distribution of promising facilities by considering CO2-emissions as a substitute, for which a detailed database was obtained (Dijkshoorn, 2012). It should be noted that conversion of CO2-exhaust into waste heat potential for a given source is more difficult as the relations between the amount of residual heat and CO2 exhaust strongly depend on the underlying industrial process.
Although at a late date a report was obtained with a first order estimation on waste heat potential within the Rotterdam area(Buck, Afman, & Jordan, 2011), its confidential nature made it hard to use for the geospatial analysis below, so the CO2 analog method continues to be used there. The report does confirm the highly dependent nature of both thermal demand and residual potentials to their parent industrial processes, so for residual heat potentials a process engineering analysis is strongly recommended.
75 Waste Other 2% disposal Chemical 7% industry 10%
Energy sector 47% Refine-ries 34%
Figure 60 CO2 emissions in Rotterdam by industry type (2010 figures)
The Rotterdam area amounts to 144 industrial sources of CO2, 86 of which had a registered and/or active exhaust in 2010 (the most recent year for which data is available). Most CO2 exhaust originates from the energy sector and refineries, with minor contributions from the chemical industry, waste disposal and others. Of the major exhaust sources only two are within the city proper, both currently being used to feed district heating networks on the north side.
Figure 61 industrial facilities in Rotterdam by CO2 emissions, based on (Dijkshoorn, 2012)
The Botlek area in the middle, which is relatively close to the city itself, is the source of 57% of
industrial CO2 emissions (minus the 7% from AVR Rijnmond already in use for district heating) and may be a promising source for district heating.
Furthermore, 25% of CO2 emissions originate from a single E.on (coal fired) power plant on the Maasvlakte. Although it is quite far removed from the city itself, its heat may be applied towards industrial use, the town of Hoek van Holland (4310 households) and towns to the south west (like Oostvoorne, Brielle, Rockanje and Hellevoetsluis). The older section of this plant has in the past provided heat for a shrimp farm, and might thus be available for new residual heat applications.
76
Figure 62 current and planned district heating sources and networks
At present, the district heating network consists of the older northern section and a southern section that’s currently under development. Three sources are used: the Galilëistraat power plant in the centre, the RoCa plant in the North West and AVR Rijnmond (a waste processing facility) for the new network in the south west.
Figure 63 District heating potential of an oil refinery
Using CO2 emissions as a residual heat analog, there appear to be no large residual heat sources in the area, so for the purpose of this case study, a single residual heat source is considered: the giant Shell Pernis refinery at Vondelingenplaat. Residual heat potential is quite dependent on the industrial process involved, but an estimation was made based on its production capacity of 21Mton and a residual heat project involving two Swedish refineries at 17Mton combined, delivering 543.8
77 GWh/yr (Samuelsson, 2010). The thermal potential for Pernis is likely to be significantly more(Buck, Afman, & Jordan, 2011), but Samuelsson doesn’t state which percentage of their technical potential is used. For this case study, the estimated (partial) residual heat potential of Pernis would thus be
671.8 GWh/yr, or 2.418 TJw/jr.
Assuming a linear energy loss of 0.24% per km (Heirbaut, 2012) and an average route length of
12km, this would result in a capacity of 2350 TJw/jr available at the case study area, if fully transported here. Note that the proximity of the neighbourhoods allowed the transport route to be simplified to a single pipe, representing the average transport loss to the area as a whole.
As distribution loss is significantly higher than within the main transport network, and household density is thus important, only areas with a high number of households are assumed to be participating. The threshold has been set to 50 households per ha of land area, resulting in coverage of 8 neighbourhoods and 34.8% of households in the area. Distribution heat loss per neighbourhood has been estimated by using the reverse of the density, which also allowed to estimate the average delivery temperature. Total heat loss will be 25%, thus requiring 2.218 TJw/yr delivered by the transport network.
Heat demand for the neighbourhoods with sufficient density within the case study area, thus appears to be covered by this single source. If more sources are included, demand reduction measures are taken and lower density neighbourhoods are included as well, district heating coverage may of course be higher.
land house- demand estimated aggreg. required district surface holds density loss demand energy heating CBS code name (ha) n hh/ha participation % TJw/yr TJw/yr district heating coverage 0117 Kop van Zuid 26 710 27.3 - - - - 50.0 density threshold 1079 Kop van Zuid-Entrepot 73 3,790 51.9 v 40% 66.9 111.6 35,500 households covered 1080 Vreewijk 201 7,030 35.0 - - - - 37.7% of total households 1081 Bloemhof 80 6,260 78.3 v 18% 338.1 413.0 8 neighbourhoods covered 1082 Hillesluis 89 4,720 53.0 v 39% 259.3 426.0 34.8% of total neighbourhoods 1085 Katendrecht 56 1,760 31.4 - - - - 522 ha of land area covered 1086 Afrikaanderwijk 48 4,040 84.2 v 13% 185.3 213.5 17.5% of total land area 1087 Feijenoord 63 3,300 52.4 v 40% 156.3 259.1 68.0 avg household density 1088 Noordereiland 24 1,840 76.7 v 19% 95.7 118.8 51.9 min 1283 Oud-IJsselmonde 195 2,760 14.2 - - - - 94.0 max 1284 Lombardijen 260 6,680 25.7 - - - - 1289 Groot-IJsselmonde 578 13,470 23.3 - - - - 1290 Beverwaard 149 5,100 34.2 - - - - 1570 Charlois Zuidrand 0 0 - - - - district heating loss 1571 Tarwewijk 83 5,720 68.9 v 26% 259.5 350.3 1.2 density vs loss factor 1572 Carnisse 62 5,830 94.0 v 5% 309.5 325.7 25% total loss 1573 Zuidwijk 153 6,370 41.6 - - - - 1574 Oud-Charlois 135 6,440 47.7 - - - - 1575 Wielewaal 21 530 25.2 - - - - 1576 Zuidplein 26 630 24.2 - - - - 1577 Pendrecht 123 5,820 47.3 - - - - 1578 Zuiderpark 495 620 1.3 - - - - 1593 Heijplaat 41 750 18.3 - - - - 23 2,981 94,170 31.6 8 1,671 2,218
Figure 64 Estimating distribution heat loss
Whether or not the potentials described are viable depends on more factors however. An intermittent production process, or one constantly varying in capacity should not be an obstacle, as short term fluctuations in heat production can be compensated for with a heat buffer (cfr. Afval Energie Centrale). If longer periods are expected or large fluctuations however (which may very well
78 be the case with facilities that only generate heat as a by product), it may be necessary to add an installation to the network whose primary role is to generate heat, able to switch on and off relatively quickly. In short: crossing the boundaries from assessing potential (with some degree of uncertainty) to a full blown network design may get complicated quickly. Finally, as the required investments related to heat transport piping are quite significant, a major consideration will be the financial one.
5.7. Combined potentials The calculated local and regional potentials can now be combined into an overview (Figure 64), in order to assess whether on average, demand is covered for the area as a whole. If the result is positive, discrepancies within the neighbourhoods themselves may be solved by exchanging within the area.
Rotterdam Zuid DEMAND (dwellings) SUPPLY NET SUM heat demand e-demand GEOTH (deep) residual heat (Pernis) BIOMASS (clippings/CHP) SOLAR (PV) WIND (large) heat electricity
TJw/yr TJw/yr TJw/yr TJe/yr TJw/yr Tavg at 3000m TJw/yr TJw/yr TJ/yr TJe/yr TJw/yr TJe/yr TJe/yr TJw/yr TJw/yr TJw/yr TJe/yr (at 90 °C) (at 70 °C) (at 50 °C) (renewable) (°C) 90+ °C 70-90 °C (total) (20%) (60% at 90°C) (at 90 °C) (at 70 °C) (at 50 °C)
0117 Kop van Zuid 3 3 27 21 13.7 92 0 0 0.0 0.0 0.0 49 - 11 -3 -27 28 1079 Kop van Zuid-Entrepot 56 5 6 40 18.3 92 0 71 1.1 0.2 0.7 34 - -37 66 -6 -6 1080 Vreewijk 78 291 19 71 95.5 93 0 0 2.1 0.4 1.3 12 - 19 -291 -19 -59 1081 Bloemhof 62 263 13 63 38.7 94 358 0 0.3 0.1 0.2 8 - 335 -263 -13 -55 1082 Hillesluis 48 195 16 51 29.7 94 0 275 0.6 0.1 0.4 32 - -18 80 -16 -19 1085 Katendrecht 29 39 28 21 28.0 92 0 0 0.3 0.1 0.2 15 - -1 -39 -28 -6 10 Feijenoord 1086 Afrikaanderwijk 62 106 17 39 14.4 92 196 0 0.4 0.1 0.2 61 - 149 -106 -17 23 1087 Feijenoord 74 79 4 30 15.8 93 0 166 18.6 3.7 11.1 48 - -47 87 -4 22 1088 Noordereiland 13 73 10 18 8.4 93 101 0 6.0 1.2 3.6 22 - 101 -73 -10 5 1283 Oud-IJsselmonde 60 50 33 37 47.1 94 0 0 0.0 0.0 0.0 39 - -12 -50 -33 3 1284 Lombardijen 34 345 19 73 112.7 98 0 0 6.9 1.4 4.1 77 - 82 -345 -19 5 1289 Groot-IJsselmonde 74 69 81 40 134.9 98 0 0 0.0 0.0 0.0 48 - 61 -69 -81 7
12 IJsselmonde 1290 Beverwaard 250 17 0 59 29.8 96 0 0 4.9 1.0 3.0 6 - -217 -17 0 -52 1570 Charlois Zuidrand 4 7 2 0 0.0 95 0 0 6.1 1.2 3.7 129 - -1 -7 -2 130 1571 Tarwewijk 16 230 14 53 41.5 94 275 0 0.5 0.1 0.3 40 - 301 -230 -14 -13 1572 Carnisse 24 281 4 52 31.0 93 328 0 0.3 0.1 0.2 29 - 335 -281 -4 -24 1573 Zuidwijk 39 272 38 63 63.8 95 0 0 1.5 0.3 0.9 11 - 26 -272 -38 -51 1574 Oud-Charlois 63 270 23 63 58.5 93 0 0 1.9 0.4 1.1 25 - -3 -270 -23 -37 1575 Wielewaal 0 29 0 5 6.7 96 0 0 0.1 0.0 0.1 24 - 7 -29 0 19 15 Charlois 1576 Zuidplein 24 31 0 9 13.7 93 0 0 0.2 0.0 0.1 55 - -10 -31 0 46 1577 Pendrecht 15 247 44 58 59.5 95 0 0 2.8 0.6 1.7 36 - 46 -247 -44 -21 1578 Zuiderpark 26 10 12 8 247.5 96 0 0 21.5 4.3 12.9 47 - 235 -10 -12 44 1593 Heijplaat 10 43 7 10 20.5 93 0 0 1.6 0.3 1.0 63 - 12 -43 -7 53 1,063 2,956 417 883 1,129 94 1,259 170 78.0 15.6 46.8 908 12,957 1,372 -2,445 -417 12,998
Table 11 Combined potentials
This overview is divided into three segments: demand on the left (the pink columns displaying thermal demand at different temperatures, the yellow column displaying electricity demand), supply in the middle (geothermal in brown with both temperature and potential in TJ, then residual heat potential with the same columns, then biomass in green with raw potential on the left and CHP thermal and electric output on the right, then solar in orange and eventually wind) and the net sum (supply minus demand) on the right, mirroring the demand columns.
Individual neighbourhoods can now be assessed with their localised energy profile (Figure 65).
79 1086 Afrikaanderwijk 39 TJ dwellings 61 TJ roof PV S 0.4 TJ clippings L G 160
140
120
196TJ residual 106°C 100 14TJ geoth 3000m 92°C 62TJ dwelling 90°C °C 80
106TJ dwelling 70°C 60
17 TJ dwelling 50°C 40
20 demand supply 0
Figure 65 Neighbourhood EPM profile for the Afrikaanderwijk (1086)
In this case the Afrikaanderwijk would on average receive sufficient electricity from PV, but requires additional heat energy. The single residual heat source considered however can cover that demand fully, and this neighbourhood thus has a net positive potential.
Figure 66 (right) spatial distribution of supply potentials for solar, biomass, residual heat and geothermal.
Although the focus was on demonstrating the EPM method, and thus only a limited number of renewable potentials was taken into account, the immense amount of wind power clearly stands out (although considering population distribution, strictly speaking only a third would be available to the case study area, less when industries consume part of it as well). In case currently uninvestigated renewable energy potentials prove to be unable to cover heat demand and electricity can’t be favourably exchanged for heat from surrounding areas, heat pumps combined with ground source, water and air exchangers might be considered to provide the difference using the excess wind power.
Even though geothermal potential in the area itself may not be sufficient (although reduction measures may still bring demand and supply closer), the case study area borders on sparsely inhabited areas to the east and south, where geothermal potential may be tapped and transported. This applies to the area to the east as well, covered by either water or industrial areas which might only make limited use of geothermal heat. Finally, the towns of Maassluis and Hoek van Holland appear to be on top of the highest yielding geothermal area in the Netherlands (roughly ten times the yield per m2 of the case study area). If this remains unused by these towns or the neighbouring Westland area, this might be transported to the Rotterdam urban area as well.
80 In conclusion: based on this small set of renewable potentials and only considering households, even though energetically speaking the area is a net exporter, there are imbalances for individual energy categories that need addressing, particularly heat. Further study of additional potentials is expected to at least partially close the gap however, and exchange potentials with neighbouring areas appear to be present as well. Finally, even though only one source of residual heat was considered, total potential is likely to be quite significant.
81
PART V PART V conclusions and recommendations
82
83 6. Conclusions and recommendations Although most of the iGUESS modules were not yet developed, both the roof solar module demonstrator, research within this project regarding the other (future) modules, and experiences with the many layers of GIS data available within Rotterdam have made it abundantly clear that the iGUESS energy modules can not only be combined to provide EPM based energy profiles, but additionally that the level of detail provided by this GIS data brings unprecedented detail to the EPM methodology and thus warrants further development.
Furthermore, even though development of an improved residual heat potential method that can be used within a GIS environment proved challenging at first, the result has been simplified sufficiently for use both in iGUESS and future EPM studies. In its current state the model does require manually connecting sources and sinks, but further automation using network analysis applications should be possible.
7. Future development Next to the suggestions made throughout the report, there are several more general observations that can be made regarding further development of iGUESS and EPM. The number of energy potentials currently included for example should be expanded in order to provide a broader picture of available energy.
7.1. Industry Even though traditionally detailed data on industrial energy demand and supply tends to be difficult to obtain, its share of total urban energy demand and supply is quite significant. CO2 reduction measures will thus be much more effective if industry is included in more profound ways than just as possible sources of residual heat.
7.2. Transport The same goes for transport, which tends to be good for a significant part of fuel (and soon electricity) demand. As biomass may be relatively scarce within an urban area, including transport demand will show which part of it is actually available for CHP and other building and industry related purposes.
7.3. Energy storage Renewable sources like photovoltaic and wind tend to have largely independently fluctuating outputs. In order to ensure a continuous match between supply and demand, an urban energy network will require sufficient storage facilities, possibly complemented by steering non essential demand. Being able to assess the required diurnal and seasonal storage capacity and demand effects does require a temporal component though.
7.4. Time As mentioned in the previous paragraph, assessing viability of an energy based plan requires including a temporal component more detailed than the current steady state calculations using a
84 lump sum over a year. An example of this is the solar module, which even includes calculating shading from neighbouring buildings, although in its current state it only does this once in advance, providing the results of those dynamic calculations as a stationary map layer. Ideally demand and supply are matched per hour over a year, but larger steps (days, weeks, months or seasons) can initially be used to reduce complexity and gradually build up the model. Even though smaller scale temporal energy models already exist, integrating a spatial component will bring an unprecedented level of detail to the feasibility of calculated energy potentials.
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