Report on available statistical data and available studies on local renewable energy sources. Contents

ABSTRACT...... 3 4. COUNTRY SUMMARY OF DATA AVAILABILITY...... 16 1. INTRODUCTION...... 4 4.1 Statistics and data in Finland...... 16 2. AVAILABLE OPEN DATA...... 4 4.2 Statistics and data in Ireland...... 16 2.1 Wind Energy...... 4 4.3 Statistics and data in Norway...... 16 2.2 Photovoltaic energy...... 7 4.4 Statistics and data in Sweden...... 16 2.3 Historic hourly data from the 5. DISCUSSION...... 17 National Weather services...... 8 5.1 Different calculation roads. 2.4 Biofuel availability, general...... 9 How much information is actually 2.5 Biofuel availability (forest)...... 9 needed?...... 17 2.6 Hydropower...... 9 5.2 EnergyPLAN modelling of wind 2.7 Peat...... 10 turbines. Motivation and need of 2.8 Data from the EnergyPLAN correction factor...... 17 homepage ...... 10 6. CONCLUSIONS...... 18 3. DATA FROM PROJECTS...... 11 3.1 Biofuel availability...... 11 7. REFERENCES...... 19 3.2 BioDRI...... 11 3.3 BeWhere projects...... 11 3.4 The S2Biom project – Delivery of sustainable supply of non-food Biomass to support a ”resource-efficient” Bioeconomy in Europe [17]...... 12 3.5 Arctic Energy project and evaluation of local energy data...... 13 3.5.1 Consumption: Hourly distributions...... 15

2 ABSTRACT

A report has been made on the information on statistical data and available studies on local energy systems in Sweden, Finland, Norway and Ireland that was available from open data. The study shows that almost all data are available on national level but the availability decreases gradually if we go forward to regional, county, municipality and community level. Sweden has good national and energy statistics down to municipality level, Finland at county level. Norway has statistics to municipality level for a part of the country but only to county level for the northernmost areas. Ireland has statistics on some data on local level. Important data sources, availability maps etc. are described in some details. A separate list data of availability for each of the four countries is included. Experience of energy simulation from some previous projects has been described. It can be summarized as follows: There is always some data missing. The structure of those “information holes” was different for each case. However, it was possible to get around this by choosing different evaluation roads. These roads were also different for each case.

A comparison of these experiences with the data found in this report indicates the available data should be sufficient to start modeling pilot communities if: • we know the geographical area and position of the chosen pilot communities • and have a general description of their properties and what they want to do

The main object of the assessment in next step (delivery T.2.4.3) should be to supply those data

Luleå January 6, 2019

Carl-Erik Grip 1 Introduction The aim of this deliverable, according to LECO: s project plan 1. INTRODUCTION is: “Statistical data and available studies on local energy systems (use and suThe aim of this deliverable, according to LECO:pply, local resources) will be s project plan is: “Statistical datacompiled into a report.” The study was and available studies on local made focusing on both yearly meaenergy systems (use and supply, local resources)n data and the distribution of will be compiled into a report.” The hourly values over the year. The later study was made focusing ones are necessary as the intension both yearly mean data and the distributionty of availability and consump of hourly values over the year. The latertion are seldom in phase and an ones are necessary as the intensity of availability and consumption are seldom in phase and an evaluation model must handle that. evaluation model must handle that.

22. AVAILABLEAvailab lOPENe op DATAen data 2.1 Wind Energy 2.1 Wind Energy Figure 1 shows maps on wind speed and expected electricity production from Finnish Wind Atlas [1] Figure 1 shows maps on wind speed and expected electricity production from Finnish Wind Atlas [1]

a) Wind speed at 50 m (Year) b) Calculated production at a height of 50 m (year) Figure 1 Wind speed and calculated production at 50M according to Finnish Wind Atlas [1]

The maps can be set to give a yearly mean value or the mean value for a chosen month at a height of The maps can be set to give a yearly mean value or the mean value for a chosen month at a height of 50, 100 or 50, 100 or 200 m. 200 m. The map in Figure The map in Figure1 a shows the yearly mean 1 a shows the yearly mean of the wind speed for a height of of the wind speed for a height of 50m. 50m. The map in Figure 1 b The map in Figure 1 b shows the calculated yearly production ofshows the calculated yearly production of a WinWord WWD-3, 3 MW wind Turbine at a WinWord WWD‐3, 3 MW wind a height of 50 m. Turbine at a height of 50 m.

4 Figure 2 shows pictures from SEAI: s Irish Wind Mapping System [2]. Figure 2 shows pictures from SEAI: s Irish Wind Mapping System [2].

a) Wind farm Layer b) With wind speed layer Figure 2 SEAI: s Wind Maps [2].

Wind farm data are extracted by pointing at the Wind farm map in Figure 2a. and wind data by pointing Wind farm data are extracted by pointing at the Wind farm map in Figure 2a. and wind data by pointing at the wind speedat the wind speed map in Figure 2 b. One example for each map i map in Figure 2 b. One example for each map is shown of the outputs shown of the output after pointing at after pointing at a chosen location a chosen location Figure 3 shows wind power data from different Swedish areas according to the report Vindkraftsstatistik 2016 (WindFigure 3 shows wind power data f Power Statistics 2016) from the rom different Swedish areas accSwedish Energy agency [3] ording to the report Vindkraftsstatistik 2016 (Wind Power Statistics 2016) from the Swedish Energy agency [3]

5

a) Electric areas (SE1‐SE4) b) County data c) Top 50 municipalities Figure 3 Local and regional Wind power, Sweden [3] a) Electric areas (SE1‐SE4) b) County data c) Top 50 municipalities The maps shows yearly production data for The Electric areas SEFigure 3 Local and regional Wind power, Sweden [3] 1‐SE4 (Figure 3 a), all counties (Figure 3 The maps shows yearly production data for The Electric areas SE1-SE4 (Figure 3 a), all counties (Figure 3 b) and b) and the 50 Municipalities with the highest wind Power volume (Figure 3 c). Hourly values of wind The maps shows yearly production data for The Electric areas SEthe 50 Municipalities with the highest wind Power volume (Figure 3 c). Hourly1‐SE4 (Figure 3 a), all counties (Figure 3 values of wind power production power production and total electricity production can also be found at both Nordpool [4] and the b) and the 50 Municipalities withand total electricity production can also the highest wind Power volume be found at both Nordpool [4] and (Figure 3 c). Hourly values of wind the Swedish National Grid Statistics Swedish National Grid Statistics[5]. The latter one also includes Hourly [5]. The latter one also inclu production from Water power,des Hourly production from Water power, Wind Power, Nuclear power, CHP plants, power production and total electricity production can also be found at both Nordpool [4] and the Wind Power, Nuclear power, CHP plGas turbines + diesel generators, Solar powerants, Gas turbines + diesel ge and unspecified producers.nerators, and unspecified Figure 4 shows examples of t hourly logs Swedish National Grid Statistics [5]. The latter one also includes Hourly production from Water power, producers. Figure 4 shows examplof Wind power and Solar power in regiones of t hourly logs of Wind pow SE1 (Northernmost Sweden, seeer and Solar power in region SE1 Figure 3 a) from the Swedish National Wind Power, Nuclear power, CHP plGrid Statistics. ants, Gas turbines + diesel generators, Solar power and unspecified (Northernmost Sweden, see Figure 3 a) from the Swedish National Grid Statistics. producers. Figure 4 shows examples of t hourly logs of Wind power and Solar power in region SE1 (Northernmost Sweden, see Figure 3 a) from the Swedish NationalWind SE1 Grid Statistics. Solar SE1 0,400 600,0 Wind SE1 0,350 Solar SE1 500,0 0,400 600,0 0,300 400,0 0,2500,350 500,0 0,2000,300 300,0 400,0 0,1500,250 200,0 0,200 300,0 0,100 100,0 0,0500,150 200,0 0,0000,100 0,0 1 1

100,0 0,050 315 629 943 1257 1571 1885 2199 2513 2827 3141 3455 3769 4083 4397 4711 5025 5339 5653 5967 6281 6595 6909 7223 7537 7851 8165 8479 315 629 943 1257 1571 1885 2199 2513 2827 3141 3455 3769 4083 4397 4711 5025 5339 5653 5967 6281 6595 6909 7223 7537 7851 8165 8479 0,000 0,0 1 1 315 629 943 1257 1571 1885 2199 2513 2827 3141 3455 3769 4083 4397 4711 5025 5339 5653 5967 6281 6595 6909 7223 7537 7851 8165 8479 315 629 943 a) Wind power 2016 Area SE1 1257 1571 1885 2199 2513 2827 3141 3455 3769 4083 4397 4711 5025 5339 5653 5967 6281 6595 6909 7223 7537 7851 8165 8479 b) Solar power 2016 Area SE1 Figure 4 Swedish National Grid Statistics [5]: Examples of hourly data for the northern electric area (SE1) a) Wind power 2016 Area SE1 b) Solar power 2016 Area SE1 Figure 4 Swedish National Grid Statistics [5]: Examples of hourly data for the northern electric area (SE1)

The Nordpool statistics on Norway includes hourly vales of electricity production and consumptions for the Elec- tric Areas NO1 – NO4, where NO4 is the northernmost one. However, no data separated on wind power, and solar power were found.

6 The Nordpool statistics on Norway includes hourly vales of electricity production and consumptions for the Electric Areas NO1 – NO4, where NO4 is the northernmost one. However, no data separated on wind power, and solar power were found.

2.22.2 PPhotovoltaichotovoltaic e nenergyergy TheThe maps in Figure 5 show the photovoltaic solar potential in E maps in Figure 5 show the photovoltaic solar potential in Europe andurope and in Finland. They were in Finland. They were published by the European commission’s Joint research Centre. They show the incident radiation and expected PV electricity for a published by the European commission’s Joint research Centre. They show the incident radiation and south oriented optimally inclined solar panel. expected PV electricity for a south oriented optimally inclined solar panel.

a) Map showing Europe’s potential of solar radiation b) Map on Finland’s potential of solar and PV electricity [6] radiation and PV electricity [7], [8] Figure 5 Example of Maps showing potential

The map in Figure 5 a shows the incident radiation and expected PV electricity for a south oriented The map in Figure 5 a shows the incident radiation and expected PV electricity for a south oriented optimally optimally inclined solar panel. The PDF file of that map also includes two side maps that are not shown inclined solar panel. The PDF file of that map also includes two side maps that are not shown here. One of them showshere. One of them shows the opti the optimal inclinations for the mal inclinations for the mappedmapped geographical positions. The geographical positions. The other one other one shows the improvement ifshows the improvement if the pan the panels are moving with a one axisels are moving w tracking system. Theith a one axis map in Figuretracking system. The map in Figure 5b 5b shows the incident radiation and expectedshows the incident radiation and expected PV electricity for Fi PV electricity for Finland. A similar map for Sweden (not shownnland. A similar map for Sweden (not here) can be found in reference [9] (not shownshown here) can be found in reference [9] (not shown here). In here). In addition, a more extensive study on the PV potential canaddition, a more extensive study on the be found in the VTT report No 217: “The role and opportunities for solar and Europe”, reference [10]. PV potential can be found in the VTT report No 217: “The role and opportunities for solar energy in ThereFinland and Europe”, reference [10]. is also the STRÅNG model [11] which covers the Nordic countries. It is run by SMHI and can deliver instan- taneous fields of global radiation, photosynthetically active radiation, UV radiation (CIE weighted) and direct radiationThere is also the STRÅNG model [ at a horizontal resolution of 11] which covers the Nordic couabout 2.5 x 2.5 km and a temporal resolutionntries. It is run by SMHI and can deliver of one hour. It has been tuned andinstantaneous fields of global r validated using SMHI: s data onadiation, photosynthetically ac direct solar radiation and global radiation.tive radiation, UV radiation (CIE weighted) and direct radiation at a horizontal resolution of about 2.5 x 2.5 km and a temporal resolution of one Historicalhour. It has been tuned and validated using SMHI: s data on dir data as time series, fields or charts with a time resolution downect solar radiation and global radiation. to hourly values can be extracted using the link of reference [12]. Historical data as time series, fields or charts with a time resolution down to hourly values can be extracted using the link of reference [12]. 2.3 Historic hourly data from the National Weather services 2.3 Historic hourly data from the National Weather services The Swedish Meteorological Institute (SMHI) has a database with the possibility to get hourly data of measured temperature,The Swedish Meteorological Insti wind speed and solar radiationtute (SMHI) has a database with from all weather stations [13]. the possibility to get hourly data of Figure 6 shows the available data from differentmeasured temperature, wind speed locations and solar radiation from all weather stations [13]. Figure 6 shows the available data from different locations

Figure 6 Open data from SMHI, Sweden. Available hourly measurements of temperature, wind and global radiation [13]

Figure 7 shows the weather stations of the Finnish Meteorological Institute (FMI) [14]. (Only the northern part of FinlandFigure 7 shows the weather stations of the Finnish Meteorologic is shown, as the webpage only showed part of the maps.) Two examplesal Institute (FMI) [14]. (Only the of hourly wind and solar observa- tions are included in the picture (location shown with green circle on the maps). northern part of Finland is shown, as the webpage only showed part of the maps.) Two examples of hourly wind and solar observations are included in the picture (location shown with green circle on the maps).

8 Wind speed (m/s) 12

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0 1 606 1211 1816 2421 3026 3631 4236 4841 5446 6051 6656 7261 7866 8471 9076 9681 10286 10891 11496 12101 12706 13311 13916 14521 15126 15731 16336 16941 Wind 2016‐2917: Hailuoto Keskikylä

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‐200 1253 1879 2505 3131 3757 4383 5009 5635 6261 6887 7513 8139 8765 9391 10017 10643 11269 11895 12521 13147 13773 14399 15025 15651 16277 16903 Weather (Wind, Temp etc.) RadiationMeasurement Solar 2016‐2917: Jyväskylä lentoasema

Figure 7 Open data from Finnish The maps and the authors experienceMeteorological Institute [14]. with the data show that measuredPossibilities to downloa hourly data for tod hourly data on wind get time distributions Temp Global radiation etc. Availaon wind and temperature are availableble stations and example on do almost everywhere in Swedenwnloads for 2 years. and Finland, but similar data on global radia- tion are only available on a few stations in these countries. The maps and the authors experience with the data show that measured hourly data for to get time distributions on wind and temperThe Norwegian Weather service [15]ature are available almost ever has a possibility to find weather dataywhere in Sweden and Finland, but at a certain time and place. There seems similar data on global radiation are only available on a few stto be no easy way to get hourly time series. However, they can giveations in these countries. diagrams for the last 13 months, so probably they have the data (see example for Oslo in reference [16]). The Norwegian Weather service [15] has a possibility to find weather data at a certain time and place. There seems to be no easy way to get hourly time series. HoweveThe Irish weather service has a service for getting hourly data from differentr, they can give diagrams for the last 13 stations by pointing at a map. A test on downloading the whole data series for Newport gave a table of the following parameters since its start 2005: months, so probably they have the data (see example for Oslo in reference [16]). Date and Time (utc), rain mm, Air Temperature (C), Wet Bulb Temperature (C), Dew Point e (C), Vapor Pressure The Irish weather service has a (hPa), Relative Humidity (%), Meanservice for getting hourly data Sea Level Pressure (hPa), Mean from different stations by pointing at a Wind Speed (kt), Predominant Wind Direction (deg). A check for the early years showed several missing data, but data for recent years were very complete. map. A test on downloading the whole data series for Newport gave a table of the following parameters since its start 2005: Date and Time (utc), rain mm, Air Temperature (C), Wet Bulb Temperature (C), Dew Point e (C), Vapor Pres2.4 Biofuel availability,sure (hPa), Relative Humidity (%), Mean general Sea Level Pressure (hPa), Mean Wind Speed (kt), Predominant Wind Direction (deg). A check for the early years showed several missing data, but data for recent years wThe “S2Biom project” has publishedere very complete. an extensive map on biomass availability in EU and some other countries, see 3.4 below and reference [17]. SEAI has published a Bioenergy map system for Ireland [18]

2.4 Biofuel availability, general 2.5 Biofuel availability (forest) The “S2Biom project” has published an extensive map on biomass availability in EU and some other countries, see 3.4 below and refeThuresson [19] made a study on therence [17]. SEAI has published availability four Balance areas in Sweden, a Bioenergy map system for Ireland [18] where Balance area 1 is the north- ernmost one and Balance area 1 is the southernmost one. He made for each of these areas he evaluated the avail- ability of four types of forestry by products: Grot (Branches and tops), Långtopp (logs from long tops), Klengallr 2.(Whole5 Bi treesofue froml av smallailab thinning),ility (fo Övrres Röjnt) , (Whole trees other clearing), Stubbar (stumps). Thuresson [19] made a study on tFinland has a very good forest statisticshe availability four Balance ar “METLA” [20]. Ireland has a goodeas in Sweden, where Balance area 1 is forest statistic. Planted areas are given the northernmost one and Balancealso per county, the other data are for area 1 is the southernmost one Ireland as a whole [21] . He made for each of these areas he evaluated the availability of four types of forestry by products: Grot (Branches and tops), Långtopp (logs 2.6 Hydropower Finland does not have much potential left. Sweden has a lot left, but the Environmental Code prevents continued expansion of large-scale hydropower, small-water power is also not profitable and, in addition - has similar nega- tive effects as a large hydropower plant, according to research. In Norway, small hydropower has been extensively developed after 2000, it is currently, but slower and the discussion about Norway as Europe’s Energy Battery is

9 ongoing. that is, there is a lot of potential left, but there is also a lot of resistance to continued expansion, espe- cially against large-scale hydropower and also the backbone (conflict about “monster master” - “Battle of Hardan- ger”). Ireland has good hydropower potential, even for hydro pump storage [22].

2.7 Peat Peat is sometimes looked upon as a “slowly” renewable resource, however it is usually treated as a fossil energy. Finland and Ireland use a lot of peat. In Finland it is used for district heating was used. In Ireland it is also used for the heating of small houses [22].

2.8 Data from the EnergyPLAN homepage The homepage of the EnergyPLAN project [23] includes a list of country models that have been developed within the project.

For Germany two models are named, the “2015 Model” and the “Models for Solar Thermal in Future Energy Sys- tems”. The same information is given for both of them: a full set of hourly distributions for Germany 2010. They are probably enough for good simulation of German cases.

For Finland there are two models: the “2012 Model” and the “2050 RES”. A full set of distributions specific for Fin- land are included. These could be used for modelling. However, for local simulation, it is recommended that the ones for solar energy from the arctic energy project are used (locations see Figure 10b)

For Ireland two models are described, the “2007 Model” and the “2020 Model”. They contain the same set of distri- butions. The ones for heat demand, export hydro, wind, and wave power are specific for Ireland, but not the ones used for solar energy. However, the information for United Kingdom contains a full set of distributions, which could probably be used for the ones missing for Ireland. The simulation for Ireland is described in some detail by David Connolly in EnergyPLAN: s “Fide Guide” [24].

For Norway two models are mentioned: the “2009 Model” and the “2015 Model”. The information contains a set of Norway specific distributions for most parameters except solar. For wind and solar it is recommended that the ones developed in Arctic Energy are used.

For Sweden there is one model mentioned: “2009 Model”. There are no Swedish specific distributions except some from Nordpool for the whole country.

10 For Sweden there is one model mentioned: “2009 Model”. There are no Swedish specific distributions except some from Nordpool for the whole country.

3 Data from Projects

3.3. 1DATA Bi oFROMfuel a vPROJECTSailability Thuresson [19]made a study on four balance areas Where Balance area 1 is the four northernmost 3.1counties in Sweden and Balance area 4 is the southernmost part Biofuel availability Thuresson [19]made a study on four balance areas Where Balance area 1 is the four northernmost counties in Sweden and Balance area 4 is the southernmost part 3.2 BioDRI 3.2The BioDRI project [25] aimed t BioDRI o study the possibility to produce steel or iron powder using gasified Thebiomass as a raw material for re BioDRI project [25] aimed to studyduction of iron ore. For that p the possibility to produce steelurpose, it was necessary to deliver a or iron powder using gasified biomass as aconsiderable amount of forest b raw material for reduction of iron ore.y products (up to around ½ milli For that purpose, it was necessaryon metric tons) to one location. Two to deliver a considerable amount of forestcases were studied, a direct reduction plant in Northern Sweden by products (up to around ½ million metric tons) to one location. Two and one in Southern Sweden (Halmstad cases were studied, a direct reduction plantor Höganäs). This created both a combined availability and tran in Northern Sweden and one in Southern Sweden (Halmstad or Höganäs).sport problem. For this reason, an This created both a combined availability and transport problem. For this reason, an evaluation of the availability of forest biomass in the nearby evaluation of the availability of forest biomass in the nearby region, separated in smaller areas had to be region, separated in smaller areas had to be made. The main outcome is shown in Figure 8 below made. The main outcome is shown in Figure 8 below

Figure 8 BioDRI Project Evaluation of local availability of forest biproducts as Energy source

The evaluation used the data of Thuresson [19] (see section 2.4) for balance area 1(Luleå case) and The evaluation used the data of Thuresson [19] (see section 2.4) for balance area 1(Luleå case) and balance area 4balance area 4 (Höganäs/Halmstad (Höganäs/Halmstad case). Each area wascase). Each area was divided i divided in 10 sub areas. These areasn 10 sub areas. These areas were were projected on to a transport mapprojected on to a transport map published by Andersson-Frisk (referencepublished by Andersson‐Frisk (r [26]). This map showed forestryeference [26]). This map showed transport as red lines and the line with was proportional to the transported volume. We assumed that the intensity of forestry transports in those areas was approximately proportional to their intensity of forestry. The percent of “redness” in each area was measured and used as an expression of transport and forestry intensity. This intensity and the measured area of each subarea was then used to interpolate the Thuresson´s data into each subarea. The tables of Figure 8 show the resulting biofuel availability, expressed as TWH/year, separated on subarea and type of forestry biomass. The map of the northern case also includes an area in northern Finland. This was suggested by a project partner from the paper industry, as they themselves used raw material from that area.

3.3 BeWhere projects [27] is a techno-economic engineering model for renewable energy systems optimization. It identifies the optimal localization, size and technology of the renewable energy system that should be applied in in a specific region. BeWhere was developed at IIASA and Luleå University of Technology, Sweden, from 2006 onwards. In 2010 it was forestry transport as red lines and the line with was proportional to the transported volume. We assumed that the intensity of forestry transports in those areas was approximately proportional to their intensity of forestry. The percent of “redness” in each area was measured and used as an expression of transport and forestry intensity. This intensity and the measured area of each subarea was then used to interpolate the Thuresson´s data into each subarea. The tables of Figure 8 show the resulting biofuel availability, expressed as TWH/year, separated on subarea and type of forestry biomass. The map of the northern case also includes an area in northern Finland. This was suggested by a project partner from the paper industry, as they themselves used raw material from that area.

3.3 BeWhere projects [27] is a techno‐economic engineering model for renewable energy systems optimization. It identifies the optimal localization, size and technology of the renewable energy system that should be applied in in a specific region. BeWhere was developed at IIASA and Luleå University of Technology, Sweden, from 2006 onwards. In 2010 it was expanded by IIASA from the local and national levels to the EU27 level. expanded by IIASA from the local and national levels to the EU27 level. BeWhere uses a Regional map with a grid of user’s resources etcBeWhere uses a Regional map with a grid of user’s resources etc. Examples of . Examples of those maps and grids for those maps and grids for the re- gional projects of BeWhere Sweden [28] and BeWhere Finland [27] are shown in Figure 9. the regional projects of BeWhere Sweden [28] and BeWhere Finland [27] are shown in Figure 9.

Figure 9 Maps and grids from the regional developments of BeWhere Sweden [28] and BeWhere Finland [27]

BeWhere Sweden and Finland are foBeWhere Sweden and Finland are focused particularlycused particularly on forest b on forest biomass, biofueliomass, biofuel production and design production and design of forest-based value chains, with a high degree of detail regarding the biomass supply and industrially integrated of forest‐based value chains, with a high degree of detail regabiofuel production, where potential plant hosts are largely modelled individually.rding the biomass supply and industrially The model is primarily used to integrated biofuel production, wanalyze how future bio-based value chainshere potential plant hosts are can be implemented cost-effectivelylargely modelled individually. The model from a system perspective, what is primarily used to analyze how future bio‐based value chains role the existing energy infrastructure (industry and energy facilities) can play,can be implemented cost‐effectively from and how different parameters a system perspective, what role affect, for example, the choice of conversionthe existing energy infrastruct technologies, localization, andure (industry and energy facilities) can integration, in a system where the same limited resource (biomass) is also in demand from other sectors. The parameters considered include e.g. play, and how different parameterpolicy instruments, future scenarios for energys affect, for example, the cho market conditions, technologicalice of conversion technologies, development and industrial localization, and integration, iinvestment opportunities [28]. n a system where the same limited resource (biomass) is also in demand from other sectors. The parameters considered include e.g. policy instruments, future scenarios for The initial focus with BeWhere Sweden was to find the optimal location for production of renewable vehicle energy market conditions, technological development and industrfuel. One important tendency that was found during the work was that more industrialial investment opportunities [28]. branches, e.g. the Steel industry have a big interest in using the same biomass resources. This will cause a n intense competition on those resources. A new project BioMetInd (Forest biomass in metal industry-possible possibilities and consequences) [29] was started together with partners from steel and forestry branch to study the impact of that competition on biomass availability.

3.4 The S2Biom project – Delivery of sustainable supply of non-food Biomass to support a ”resource-efficient” Bioeconomy in Europe [17]. The S2Biom project is a European project, granted within the 7th Framework program. It supports the sustainable delivery of non-food Biomass feedstock at local, regional, and pan European level. One product is the “Atlas with regional cost supply biomass potentials for EU28, western Balkan countries, Moldavia, Turkey and Ukraine”. This report contains high class availability spatial data regarding availability and costs of different biomass products from both agriculture and forestry, covering those countries. The Agricultural maps that show the supply of Straw and stubble, pruning supply, Grassland cuttings and Unused grassland cuttings are interesting for our project. On the forestry side a map showing the potential for Primary residues (logging residues and stumps), see is of special interest, as well as maps on Secondary residues. Some data on waste resources (Biowaste and post-consumer wood) can perhaps also be of interest [17]. 3.5 Arctic Energy project and evaluation of local energy data

The arctic Energy project is a three-year Finnish- Swedish- Norwegian INTERREG project. It has been executed dur- ing the years 2016 -2018. The aim was to model and evaluate the possibility to create energy independent commu- nities in the arctic region. Figure 10a shows the communities that were studied in the project. NORUT carried out a series of simulations for the locations shown in Figure 10b

a) Communities studied in Arctic energy b) Data from NORUT: simulation and hourly Full circle=Detailed case studies and simulations distributions, Wind; hourly distributions Figure 10 Arctic energy project. Studied communities and some data

The starting situation was the one that is usual in projects of this type: There was a lack of data for all Thelocations. The data that were available or missing was differen starting situation was the one that is usual in projects of this type: Theret between all of them, and different roads was a lack of data for all locations. The datahad to be used to evaluate the h that were available or missing waseat consumption for the differe different between all of them, andnt communities. Th different roads hadey are summarized to be used to evalu- ate the heat consumption for the different communities. They are summarized below as they could be of interest below as they could be of interest for similar situations in the LECo project. for similar situations in the LECo project. Evaluation of Heat consumption for RESCA community: The RESCA energy community is situated close Evaluation of Heat consumption for RESCA community: The RESCA energy community is situated close to Oulu. Accordingto Oulu. According to the project plan, was chosen as a first t to the project plan, was chosen as a first test and illustrationest and illustration of the use of the of the use of the EnergyPLAN model. A problemEnergyPLAN model. A problem wa was a limited availability of data.s a limited availability of data. No local data were available from No local data were available from Statistics Finland. However, there was aStatistics Finland. However, ther report: “HIUKKAVAARA A northerne was a report: “HIUKKAVAARA A winter city – a human-sized, sustainable northern winter city – a human‐sized, forerunner” [30], that showed the expectedsustainable forerunner” [30], that showed the expected developm development and population over time and the amount of differentent and population over time and the types of buildings. The expected situ- ationamount of different types of bu 2020 was chosen as case and theildings. The expe population and cted situation amount of different2020 was chosen as case and the buildings that year was estimated from the report. The Swedish Energy Agency had published data on specific yearly heat consumption of small houses [31] ,population and amount of different apartment buildings [32] and premises [33]. buildings that year was esti These data were used togethermated from the report. The Swedish with the building data to calculate the totalEnergy Agency had published data o Heat consumption for that communityn specific yearly heat consum 2020 ption of small houses [31] , apartment buildings [32] and premises [33]. These data were used together with the building data to calculate the Evaluationtotal Heat consumption f of Heat consumptionor that community 2020 for Boden: Boden is s a city situated just over 30 km inland from the Gulf of Bothnia. It has small heat and CHP plants producing District heating and Electricity. The municipality also owns someEvaluation of Heat consumption for Boden: of small Hydropower plants. There is also a larger Boden is s a city situated just over 30 km inland from the Hydropower plant owned by the Stockholm based nation- alGulf of Bothnia. It has small he company Vattenfall. The electricityat and CHP plants producing Dis from that plant goes to the nationaltrict heating and Electricity. The net and does not contribute neither to Boden’s Energy Balance nor its Economy. Boden excluding the Vattenfall plant was chosen as case. The home page ofmunicipality also owns some of small Hydropower plants. There i Boden also contains an extensive Area Description of the amount av alls also a larger Hydropower plant owned Building types in both Boden as a whole andby the Stockholm based national company Vattenfall. The electri its suburbs. The home pages and yearly reports of Boden and its subsidiarycity from that plant goes to the national energy business companies also havenet and does not contribute neit an extensive statistic of the Districther to Boden’s Energy Balance n heating and production and the heator its Economy. Boden excluding the plants, CHP and Hydropower plants. ThisVattenfall plant was chosen as case. The home page of Boden als made it possible to calculate the heat consumption along two parallelo contains an extensive Area roads: Description of the amount av all Building types in both Boden as a whole and its suburbs. The home • Road One: Swedish Energy Agency’s data on heat consumption small houses [31] , apartment buildings [32] and pages and yearly reports of Bodepremises [33] where used togethern and its subsidiary energy bus with the building data in the extensiveiness companies also have an extensive Area Description to calculate a yearly heat consumption of the whole community. This is similar to the method used for RESCA. • Road Two: The Energy data from the District heating statistics was extrapolated to the users outside that net- work. These data were added to get the total consumption. • The data from the two roads were then compared to validate the calculation

Evaluation of Heat consumption for Jokkmokk: Jokkmokk is a city with the center just over 110 km inland from the Gulf of Bothnia. Jokkmokk has few inhabitants (c.a. 5100) but a large area: (219477 km2). Around half of that area is National parks. The big rivers in the municipality produce over 10 TWh electricity/year. However, that production and its economy belong to the governmental company Vattenfall in Stockholm. The city center has a district heating network, using heat created from forestry fuel in a local heat plant. LTU and the municipality agreed to study a community consisting of Jockmokk except Vattenfall’s river- and power system. No direct data were available regarding the heat use outside the district heating network. For small houses the DoA statistics [34] showed the number of small houses heated through district heating during 2016. The total number of house- holds in small houses during the same time could be found in statistics Sweden [35]. The difference was assumed to be small houses outside the network. This gave a district heating ratio of 20.46% for small houses. A similar approach was made for apartments. The number of apartment buildings connected to district heating were found in the DoA statistics [34]. The total number of households in Apartment buildings during the same time could be found in statistics Sweden [35]. The number of residential buildings was estimated by assuming 8 households per apartment building. A district heating ratio of 70,97% for these buildings could be calculated from that. The dis- trict heating ratio in nonresidential buildings and others was assumed to be the same as for apartment buildings. The total heat use outside the network was then estimated by proportioning with the district heating ratio.

Distributions for heat consumption in Luleå and Jokkmokk. Logged hourly data from the district heating networks in Luleå and Jokkmokk were used to create distributions for heat consumptions. This distribution was used also for the users outside the network. (It can be assumed that a certain building needs the same amount of heat whether or not it is connected to the district heating net). A map in reference indicate that there are several district heating networks in Sweden, Finland and Germany, and at least some in Ireland. It might be of interest try to get similar data for the other member countries.

Distributions for Photovoltaic energy. The partner from NORUT made simulations on solar energy for a num- ber of locations within the member countries Some examples are shown in Figure 11 (Copied on to the map of in Figure 5 a)

Figure 11 Examples of solar energy distributions calculated in the arctic Energy project.

3.5.1 Consumption: Hourly distributions Hourly data on electricity consumption for Sweden Norway and Finland are available from Nordpool and can be used to create distributions for the EnergyPLAN model. Figure 12 shows examples for 2016 a

Electricity consumption 2016. Nordpool areas in Sweden. Electricity consumtion 2016. Nordpool areas in Norway Electricity consumption 2016. Nordpool data Finland. (SE1 is the northest one) (NO4 is the northest one) 30000MWh 30000MWh 16000MWh 14000 25000 25000 12000 20000 20000 10000 15000 15000 8000 6000 10000 10000 4000 5000 5000 2000 0 0 0 1 1 1 84 84 84 167 250 333 416 499 582 665 748 831 914 997 167 250 333 416 499 582 665 748 831 914 997 167 250 333 416 499 582 665 748 831 914 997 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 SE1 SE2 SE3 SE4 NO1 NO2 NO3 NO4 NO5 Figure 12 Hourly distribution of Electricity consumption In Sweden Norway and Finland 2016

Data are available for four Nordpool regions in Sweden and 5 in Norway. For Finland there is only data for the whole country

For Heat consumption we have not found any open hourly statistics. In the Swedish projects we were able to get logged data from local District heating distributors in Luleå and Jokkmokk. Distributions calculated from them are shown in Figure 13 a and b. We did not have those data for the other partner countries. However, the homepage of Energyplan has a section” Existing Country Models”, where files for Finland, Norway and Ireland could be found [36]. These are shown in Figure 13 c, d and e.

a) Luleå b) Jokkmokk c) Finland [36] d) Norway [36] e) Ireland [36] Figure 13 Hourly distribution of local heat consumption (from data logged by District heating Distributors).

The distribution for Jockmokk has a period of extremely low consumption in summer. There were also missing data in that period. A discussion with the heat plant showed that there were some measurement problems at very low flows, so those data are perhaps not perfect. The country profile for Finland is very similar to the one for Luleå. The one for Norway is quite different. It might be because of coastal climate and less difference between winter and summer. The one fore Ireland is more similar to the Swedish and Finnish ones, except that there is a constant value instead of measurement data in the middle

Figure 11 Examples of solar energy distributions calculated in the arctic Energy project. Figure 11 Examples of solar energy distributions calculated in the arctic Energy project. 3.5.1 Consumption: Hourly distributions 3.5.1 Consumption: Hourly distributions 3Hourly.5.1 dataCo onns electricityumption consumption: Hourly di forstr Swedenibution Norways and Finland are available from Nordpool and can be Hourly data on electricity consumption for Sweden Norway and Fiused to create distributions for the EnergyPLAN model. Figure 12 shows examplesnland are available from Nordpool and for 2016 a can be used to create distributioHourly data on electricity consumption for Sweden Norway and Fins for the EnergyPLAN model. Figure 12 shows examples for 2016 a nland are available from Nordpool and can be used to create distributioElectricity consumption 2016. Nordpool areas in Sweden. ns for the EnergyPLAN model. FElectricity consumtion 2016. Nordpool areas in Norwayigure 12 shows examples for 2016 a Electricity consumption 2016. Nordpool data Finland. (SE1 is the northest one) (NO4 is the northest one) 30000MWh 30000MWh 16000MWh Electricity consumption 2016. Nordpool areas in Sweden. 14000 Electricity consumption 2016. Nordpool data Finland. 25000 25000 Electricity consumtion 2016. Nordpool areas in Norway (SE1 is the northest one) (NO4 is the northest one) 12000 30000MWh 30000MWh 16000MWh 20000 20000 10000 14000 1500025000 1500025000 8000 12000 6000 1000020000 1000020000 10000 4000 15000 15000 8000 5000 5000 2000 6000 100000 100000 0 1 1 1 4000 84 84 84 167 250 333 416 499 582 665 748 831 914 997 167 250 333 416 499 582 665 748 831 914 997 5000 167 250 333 416 499 582 665 748 831 914 997 5000 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993

1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 2000 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 0 SE1 SE2 SE3 SE4 0 NO1 NO2 NO3 NO4 NO5 0 1 1 1 84 84 84 167 250 333 416 499 582 665 748 831 914 997 167 250 333 416 499 582 665 748 831 914 997 167 250 333 416 499 582 665 748 831 914 997 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 1080 1163 1246 1329 1412 1495 1578 1661 1744 1827 1910 1993 SE1 SE2 SE3 SE4 NO1 NO2 NO3 NO4 NO5 Figure 12 Hourly distribution of Electricity consumption In Sweden Norway and Finland 2016 Figure 12 Hourly distribution of Electricity consumption In Sweden Norway and Finland 2016 Data are available for four Nordpool regions in Sweden and 5 in Norway. For Finland there is only data Data are available for four Nordpool regions in Sweden and 5 in Norway. For Finland there is only data for the Data are available for four Nordpool regions in Sweden and 5 infor the whole country whole country Norway. For Finland there is only data for the whole country For Heat consumption we have not found any open hourly statistics. In the Swedish projects we were For Heat consumption we have not found any open hourly statistics. In the Swedish projects we were able to get able to get logged data from local District heating distributorFor Heat consumption we have notlogged data from local District heating distributors found any open hourly statisti in Luleå and Jokkmokk.s in Luleå and Jokkmokk. Distributions cs. In the Swedish projects we were Distributions calculated from them are able to get logged data from local District heating distributorcalculated from them are shown in Figure 13 a and b. We did notshown in Figure 13 a and b. We did not have those data for the other s in Luleå and Jokkmokk. Distributions partner have those data for the other partner countries. However, the homepage calculated from them are shown in Figure 13 a and b. We did notcountries. However, the homepage of Energyplan has a section” Existing Countryof Energyplan has a section” E Models”, where files for Finland, have those data for the other partner xisting Country Models”, where files Norway and Ireland could be found countries. However, the homepage for Finland, Norway and Ireland [36]. These are shown in Figure 13 c, could be found [36]. These are d andof Energyplan has a section” E e. shown in Figure 13 c, d and e. xisting Country Models”, where files for Finland, Norway and Ireland could be found [36]. These are shown in Figure 13 c, d and e.

a) Luleå b) Jokkmokk c) Finland [36] d) Norway [36] e) Ireland [36] Figure 13 Hourly distribution of local heat consumption (from da) Luleå b) Jokkmokk c) Finland [36] ata logged by District h d) Norway [36] eating Distributors). e) Ireland [36] Figure 13 Hourly distribution of local heat consumption (from data logged by District heating Distributors). The distribution for Jockmokk has a period of extremely low consumption in summer. There were also missing data in that period. A discussion with the heat plant sThe distribution for Jockmokk has a period of extremely low conhowed that there were some measurement sumption in summer. There were also missing data in that period. A discussion with the heat plant sproblems at very low flows, so those data are perhaps not perfeThe distribution for Jockmokk has a period of extremely low consumptionhowed that there were some measurement ct. The country profile for Finland is very in summer. There were also missing problems at very low flows, so those data are perhaps not perfesimilar to the one for Luleå. Thdata in that period. A discussion withe one for Norway is quite diffe the heat plant showed that thererent. It might be because of coastal climate werect. The country profile for Finland is very some measurement problems at similar to the one for Luleå. Thand less difference between winter and summer. very low flows, so those data are perhapse one for Norway is quite diffe not perfect. The The one fore Ircountry profilerent. It might be because of coastal climate eland is more similar to the Swedish for Finland is very similar to the one for Luleå. The one for Norway is quite different. It might be because of coastal climate and less difference between and less difference between winter and summer. and Finnish ones, except that thewinter and summer. The one fore Irelandre is a constant value instead is more similar to The one fore Ir the Swedish of measurement data in the middle andeland is more similar to the Swedish Finnish ones, except that there is a and Finnish ones, except that theconstant value instead of measurementre is a constant value instead data in the middle summer period. of measurement data in the middle Maybe this indicates measurement problems like in Jokkmokk. Maybe a similar solution could be good also for the Jokkmokk curve.

The distribution for Finland (Figure 13c) is very similar to the Swedish one. Figure 13Figure 13Figure 13

These distributions from Luleå and Jokkmokk show a very high difference between summer and winter. This is because of the arctic climate in northern Scandinavia. The distribution for 4. COUNTRY SUMMARY OF DATA AVAILABILITY

A rough summary on the availability of local data in the memory countries is included below.

4.1 Statistics and data in Finland • National statistics (StatFin) • Energy statistics: whole Finland • Housing statistics: Buildings and gross floor area by Year, Area, intended use of building, heating fuel/ source of heat and Information: Regional data, includes district heating could be used with other statistics to calculate regional heating demand • Income and consumption Households consumption: Data for major regions on cost for energy data on cost consumption of electricity, gas and other fuels (23 subgroups) + car fuel. (Expressed in cost so it would need some recalculation to get e.g. GWh) • Forestry statistics (METLA): Regions. Includes forest fuel • Solar potential: VTT report 2015 and FinSolar aurinkoenergiatietoa: Maps on potential • Wind Energy • Statistics: Finnish Wind Power Association 2017& VTT2014 • Wind and icing atlas: map on wind rate and possible production

4.2 Statistics and data in Ireland • CSO - Central Statistics Office. • Energy statistics only whole Ireland • Building stock housing stock per county per city and county • Census 2016 Small Area Population Statistics: Number of households heated by Oil, Natural Gas, Electrici- ty, Coal, Peat, LPG or Wood, as well as size of housings for chosen county • Forest statistics: Forest areas (ha) per county otherwise for Ireland as a whole • Energy atlases: • SEAI WIKI map of local data on Wind farms, windspeed and bioenergy • SOLARGIS: map of local values of radiation and PV potential

4.3 Statistics and data in Norway • SSB Statisics sentralbyrå (statistics Norway) together with Norwegian Water Resources and Energy Directorate • Energy national data • Electricity county and municipality, Finnmark only County • Energy use some municipalities, Finnmark only County level: Coal, coke, Wood, Gas, Petrol, Kerosene, Diesel, Gasoline and light oils, Heavy fuel oil, waste oil, Waste • Data on water power • Data collected in Arctic energy, e.g. on solar and wind

4.4 Statistics and data in Sweden • Statistics Sweden (Statistikmyndigheten SCB) [35] • Good general and energy statistics is available down to Municipality level • Swedish Energy agency (Energimyndigheten) • Energy statistics for buildings (small houses, multi-dwelling buildings and non-residential premises): Heat use/unit or m2, total or divided on energy source [31], [32], [33] • Wind power statistics (country county and municipality level) • Solar cells number and effect county level • Swedish Meteorological and Hydrological Institute (SMHI) Statistics for weather station data on measured temperature wind and radiation available online (down to Hourly level) • Data on sustainable energy from cooperative projects, e.g. BioDRI, Arctic Energy, Energyplan different levels 5. DISCUSSION

5.1 Different calculation roads. How much information is actually needed?

The description of recent projects in section 3 shows examples of how available data have been used to create Energy models of different communities. The experiences can be summarized as follows: There is always some data missing. The structure of those “information holes” was different in the different cases. However, thereThere is always some data missing. was always a possibility to getThe structure of those “information around this by choosing different holes” was different in the different cases. However, there was always a possibility to get around this by choosing different evaluation roads. These were also evaluation roads. These were also different between the cases. different between the cases. A comparison of these experiences with the data found in this report indicates the available data should A comparison of these experiences with the data found in this report indicates the available data should be suffi- be sufficient to start modeling if: cient to start modeling if: • if we have the location of the pilot communities  • and getif we have the location of the pilot communities a general description of their properties and what they want to do  and get a general description of their properties and what they want to do The main task for the next step (delivery T2.4.3) should be to describe those data for the chosen pilots The main task for the next step (delivery T2.4.3) should be to describe those data for the chosen pilots

55.2.2 EnergyPLANEnergyPLAN modelling modelling of o fwind wind turbines. turbines .Motivation Motivatio nand an d need of correction need fofac correctiontor factor Figure 14 a shows a typical power curve of a wind turbine. FiguFigure 14 a shows a typical power curve of a wind turbine. Figure 14 b illustratesre 14 b illustrates and how it will influence and how it will influence model- modelling using data on flling using data on fluctuationsuctuations of Wind speed of Wind speed

Model thinks this is Nameplate capacity

a) Theoretical power curve [37] b) Wind speed distribution curve (Example) Figure 14 Effect of power curve if wind power is evaluated from a wind speed distribution

The power curve becomes flat above a certain wind rate. I.e., the power output for the windspeeds of The power curve becomes flat above a certain wind rate. I.e., the power output for the windspeeds of would not would not look that diagram, inslook that diagram, instead all peaks wouldtead all peaks would be cut off be cut off when the wind speed when the wind speed corresponding to corresponding to the nameplate ca- the nameplate capacity is reachepacity is reached. The EnergyPLAN modeld. The EnergyPLAN model uses a uses a version, normalized betweenversion, normalized between 0 and 1, 0 and 1, of the distribution. It then calculatesof the distribution. It then cal the instantaneous effectculates the instantaneous effec by multiplying the instantaneoust by multiplying the instantaneous value in value in the distribution with the nameplate capacity.the distribution with the nameplate capacity. This will create This will create an output effect that is below the real one. an output effect that is below the real one. EnergyPLAN has a correction factor that decreasesEnergyPLAN has a correction fact the peak effect to correct foror that decreases the peak effe that. The FIDE Guide [24], publishedct to correct for that. The FIDE Guide by the EnergyPLAN group, illustrates how the correct factor can be calculated using production data from actual windmills. It would be good if the local partners[24], published by the EnergyPLAN group, illustrates how the co could get such data from local producers. rrect factor can be calculated using production data from actual windmills. It would be good if the local partners could get such data from Anotherlocal producers. conclusion is that if we have or can get a distribution of the power output instead of the wind speed it would be better as the flattening effect of Figure 14 a would then be included in the distribution curve. better to useAnother conclusion is that if we that one instead of using wind speed have or can get a distribution + correction factor. For Sweden of the power output instead of the wind the data from National Grid Statistics couldspeed it would be better as the flattening effect of Figure 14 probably be used, see Figure 4a). The best would be if it would a would then be included in the distribution be possible to use local contacts and try to getcurve. logged better to use that one in data from nearby windmill.stead of using wind speed + co If neither of that works we wouldrrection factor. For Sweden the data have to live with Wind Speed distribution + Correction factor. from National Grid Statistics could probably be used, see Figure 4a). The best would be if it would be possible to use local contacts and try to get logged data from nearby windmill. If neither of that works we would have to live with Wind Speed distribution + Correction factor. 6. CONCLUSIONS

• A rough survey on available data sources and experiences from previous projects have been made. General data on national level are OK. Especially for Energy balances the availability of data on a ”lower” level (county, municipality and community) are less sufficient. That availability differs between countries. • However, a comparison of the documented data availability and the experiences from recent projects indicates that the available energy data should make it possible to model the example communities if we know: • o the geographical area and position of the chosen pilot communities • o and have a general description of their properties and what they want to do • The main object of the assessment in next step (delivery T.2.4.3 should be to supply those data

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[50] “Project 10700. BioDRI: Forest meets steel. Final Report (Project report in Swedish),” [Online]. Available: http://www.energimyndigheten.se/forskning-och-innovation/projektdatabas/sokresultat/?projectid=19110. Project Partners Centria University of Applied Sciences (Finland), Western Development Commission (Ireland), Luleå University of Technology (Sweden), Renewable Energies Agency (Germany)*, Jokkmokk municipality (Sweden), The Gaeltacht Authority (Ireland), Lohtaja Energy Cooperative (Finland), UiT – the Arctic University of Norway (Norway) *Outside the NPA Programme area