m28 solution d133 a gis ems tool 2

8/10/2019 M28 Solution D133 a GIS EMS Tool 2

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TREN/06/FP7EN/239285/SOLUTION

SOLUTION

Sustainable Oriented and Long-lasting Unique Team for

energy self suffIcient cOmmuNities

Deliverable D1.3.3-a, WP 1.3

GISBASED ENERGY MANAGEMENT SYSTEM:MODEL DESCRIPTION

Energy Supply and demand management

Due date of deliverable: 29-02-12Actual submission date: 29-02-12

Start date of project: 1 November 2009 Duration: 60 months

Organisation name of lead contractor for this deliverable: VTTRevision submission date: v1, original

CONCERTO is co-funded by the European Commission

Project co-funded by the European Commission within the Seventh Framework Programme (2007-2013)

Dissemination Level

PU Public

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

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

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


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GIS based Energy Management System: Model description

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TABLE OF CONTENTS:

1 SUMMARY OF THE CONTENTS..........................................................................................42 OBJECTIVE OF THE WORK PACKAGE.................................................................................43 OBJECTIVES OF BEST AND CDS SHEETS .............................................................................5

4 APPROACH TO ACHIEVE THE DELIVERABLE.........................................................................54.1 Principle of GIS based Energy management tool ........................................ 54.2 General Specification .............................................................................. 74.3 Source material and numeric estimation ................................................... 7

4.3.1 GIS data sources ................................................................................... 84.3.2 Heat consumption of buildings .............................................................. 104.3.3 Areal fuel consumption and emissions .................................................... 10

4.4 Implementation in GIS-EMS software ......................................................124.4.1 Concerto area energy balances and total emission analysis ....................... 124.4.2 Planning of decentralised energy production and DH expansion withEMS 134.4.3 Geographical representation of local energy production ............................ 16

4.5 Case studies.........................................................................................174.6 Preliminary results ................................................................................21

5 CONCLUSION............................................................................................................ 22


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1 Summary of the contents

The report in hand describes the current state of the GIS based energy systemmanagement model and the plans for the final implementation, aiming to provide a tool

to illustrate numerical data in geographical format in a meaningful way to supportdecision making on a city level.

A number of case studies were carried out utilising the data from City of Lapua, energycompany of Lapua (Lapuan Energia) and Population Register Centre to flesh out thecapabilities of the GIS assisted study in the field of energy system management. Thedetailed raw data was refined and aggregated to produce representative illustrationsconcerning the Concerto area in Lapua.

The report contains a general specification of the concept, a detailed description on thedata sources utilised and the methods used to refine the raw data, a listing of the mainparameters used in the calculation of preliminary results, energy balances and emissionsanalysis of the Concerto area, the methodology for planning decentralised production andexpansion of district heating system using EMS, and preliminary results and a collectionof illustrations in 250 m grid format showcasing the current state of the GIS tool.

2 Objective of the Work Package

The objective is to provide a common framework suitable for assessing the success of thedifferent demonstration projects and the emission mitigation potential of a community-wide application of these EE and RES technologies. This work package aims at:

Providing common reporting formats

Evaluating all relevant factors of actual and planning energy data formapping demand and supply.

Conducting quantitative and qualitative analysis of the performance of thedemonstration site installations.

Monitoring and validating the outcomes of individual and overallapplications

The report in hand aims to describe the Energy Management System using LapuaConcerto zone in visualisation of the EMS tool. The next step in developing andimplementation of EMS is a report presenting the final results of the energy supply anddemand management in the city of Lapua, i.e. application of the EMS tool for LapuaConcerto zone.

In the EMS tool development and implementation the GIS (geographical informationsystem) has an important role. GIS gives possibilities for collection and presentation

input data, for treatment of data and for output of information of the energy systemmanagement. Connection of GIS and energy modelling leads to the new title of the tool:GIS-based EMS. Energy-maps and optimised energy configuration are the main results ofthe GIS-based EMS tool.

In the WP in question, GIS-based EMS meets the several requirements in the differenttasks (Task 1.3.1-3). Some subtasks are listed below:

A common reporting format for collating relevant demand and supply data

Data mapping and resulting tool for Energy supply and demand management.

Implementation principle of the actual and planning data into the EnergyManagement System.

The database within the EMS leads to mapping of the demand and supply sidesand serves as references for further simulation and optimisations.


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For Lapua built EMS tool connects together of all relevant elements of the EMS(GIS and optimisation models)

Visualisation of the results of the EMS simulation showing the optimum use of theenergy in the Lapua Concerto zone

3 Objectives of best and cds sheetsThe work package belongs to horizontal RTD cooperation in the Solution project.However, the WP Energy supply and demand management is closely connected to Energyefficiency and RES demonstrations in the Concerto cities. Configuration of the GIS basedEMS-tool includes demo-cases, the sub-models are built and verified by the data andinformation on demo-cases. Respectively, replication possibilities of the demo-cases willbe studied by the GIS-EMS tool.

4 Approach to achieve the deliverable

4.1 PRINCIPLE OF

GISBASED

ENERGY MANAGEMENT TOOL

The content of the term Energy supply and demand management differs in domains ofthe user. In the large cities provided with district heating system and severalcogeneration energy production plants, the main aim of the energy sector is to findeconomically and environmentally best possible production structure and operational co-generation between the plants and at the plants to cover energy consumption anddemand. An individual house owner thinks of a little bit different issues, even if theeconomy and maybe also environmental impacts are the top issue.

The Concerto-cities in the Solution project could be defined as semi-urban communities,while they are countryside or provincial towns. Typical features of these semi-urbancommunities from energy management viewpoint are as follows:

There is a CHP-DH system or it is a potential alternative in the community There are small scale and separated areal DH-systems or they are potential

alternative in the community

Substantial number of building and facilities classified to individual energy system.Also some special energy and industry integrations can have a significant role inthe energy balance of the community

Single production units can have numerically and potentially major status in theenergy balances (Wind energy, hydropower, ground heat, etc.)

Individual decision makers (households, entrepreneurs, industry, services) havethe major role in energy management of the community

Open electricity market and electricity market legislation (related for example toelectricity distribution) have an important role in the local energy management

Relatively large changes (quantity and quality) in the energy system can beexpected in the near future.

These special features of the community energy system give an own special content forthe communal energy management system.

The whole area (Concerto zone) should be considered (centralised and distributedand decentralised energy areas)

Yearly based energy management for the whole area

Operational/short term optimisation and management is needed for some energy

production/consumption areas (cogeneration, polygeneration, etc).


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Geographical Information system (GIS) is needed in the EMS of the wholecommunity

The unit geographical area is a plot in the city plan also in energy management

Special development is needed for presentation of energy management

input/output Energy networks are included to the EMS

The main results from the SU-EMS are: yearly based energy supply and demandbalances, CO2-emissions, network operation

Expert reasoning should be including into the operation with GIS-EMS-tool

The Figure 1 shows the structure of the GIS based energy management tool developedfor semi-urban communities to manage energy and environmental situation anddevelopment in the whole city area. The tool consists of three functional levels and theirinteraction.

GIS level provides data and information management and completes choice functions,

where detailed optimisation is not needed.Modified optimisation level includes criteria creation phase, where decision principles aretreated for the energy solutions.

Master optimisation level is used to create the frames for the energy supply and demandin cases where co- or polygeneration takes place. Another goal is to finalise theoptimisation of the plant after revised data from GIS-module.

In the presentation of the energy situation, GIS has an essential role. The results of thevarious alternatives of energy arrangements can be collected to different layers of energymaps and finally an entire energy solution for the area can be presented quantitativelyand qualitatively.

The forth element in the scheme is reasoning. This means that the experts have alsopossibility to manage and make decisions during the planning process. This is necessaryin practical approaches of new energy structure creation.

Application x

GIS

Modified

optimisation

model

Master

optimisation

model

Total

Optimisation

Performance

Application 2

E-map layer ofstudied

application:

Capacity

Energy balances

CO2-balance

Operational

and

Total benefits

Geografical information

Energy consumption

Networks

Direct supply-demand choices

Optimised supply-demand cases

Optimesed

Operational

Performance

Reasoning

Fixing system

Reasoning

Fixing system

Communal

E-map

Capacity

Energy balances

CO2-balance

Monetary

consequences

Figure 1. Scheme of GIS-based Energy management tool.


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4.2 GENERAL SPECIFICATION

The Figure 2 below illustrates the basic concept the GIS-EMS application is based on. Theidea is to utilise the results and experiences gained from the optimisation and simulationmodelling results, use the GIS software to compile all the available data into a moreuseful format and finally present the collected and calculated data concerning the

Concerto area in the City of Lapua while providing tools to effortlessly make additionalstudies based on the material. Significant effort is laid out to make updating thedatabase as simple as possible.

Basically, GIS software is a tool used to illustrate numeric data in geographical context.For example, energy (district heat, electricity, fuels) consumed for space heating andconsequent CO2emissions produced in the Concerto area can be analysed in terms ofspatial distribution. Therefore, numeric data used in GIS analysis, whether beingcollected from statistics or calculated by using simulation or optimisation, has to belinked with actual geographical entities, mostly buildings, located in the Concerto area.

In the case of Lapua (or Finland in general), the most relevant value to be analysedgeographically in terms of energy is heat consumption (space heating and hot water) of

residential and commercial buildings, since spatial distribution of district heating (DH)network strongly defines geographical properties of heating energy. Therefore, in theexemplar case studies of this deliverable, merely heating of buildings is analysed, butsimilar analysis could carried out in terms of electricity consumption in the case ofanother Concerto area, provided that necessary numeric and geographical data isavailable.

Figure 2. Basic structure and data flows of the GIS application.

4.3 SOURCE MATERIAL AND NUMERIC ESTIMATION

In this chapter validation of general properties and requirements of source material usedin GIS analysis for the Lapua case is represented as a generalised example, but howevermethods for processing and validation of numeric data described here can be applied toany Concerto area. A general rule of the more, the better can be stated in terms ofnumeric data, however provided that all the data can be consistently linked to exactbuildings geographically.

Concerto area:

City of Lapua

GIS- Used as a tool

- Maps

- Databases

Building data- Type, size

- Energy source and

technology

- Specific

consumption

Industrial processes

Optimisation

models

Simulation

models

CHP / DH

Oil & electricity

Biogas

Heat storages

Solar energy

Geothermal heat

Bioboilers

Gasification

Concerto area:

City of Lapua

GIS results- Energy balances

- CO2balances

- Scenarios

- Planning

- Management

- Reporting

- Updatingdata


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4.3.1 GIS data sources

General geographical data obtained from the city of Lapua is illustrated in Figure 3. Thisgeographical information represents locations of e.g. waterways, road networks andbuildings. The most important aspect of this data illustrated in Figure 3 is the definitionof the Concerto area, since analysis is delimited to this area only. It is highly important to

validate the map projection of the provided geographical data in order to consistentlycombine data from different sources. It must be noted that building data of Figure 3 onlyincludes type of building (commercial, residential, public services etc.) and area of thegeographical object located on the map. This area does not provide adequate informationfor heating demand calculation purposes, since floor space must be used for spaceheating estimation.

Figure 3. Concerto area (blue line) of Lapua.

Another important source of geographical data concerning district heating network isobtained from local energy company Lapuan Energia. This data includes detailedgeographical information of the district heat pipelines and heat consumers. Numericconsumption data (annual heat energy consumption) of each consumer is also separatelyavailable and can be combined with spatial consumer data for data validation purposes.Location of the district heating network can be seen in Figure 4. District heating networkis highly important from the GIS analysis point of view, since this network connectsbuildings in the Concerto area to the most important potential renewable energy utilizer


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of the area, the CHP plant using biomass for heat and electricity production. GIS analysiscan be highly efficient tool for the district heat producing energy company in search ofpotential expansion of the DH network.

Figure 4. Grid used (250m) for analysis of geographical distribution in Concerto area of Lapua andspatial data of buildings (blue dots) and district heating network (red line).

In order to effectively analyse heating sector in terms of geographical distribution, theConcerto area must be divided into smaller fields. In the case of Lapua, a geographicalsquare grid of size 250m is used for aggregation of numeric data. This grid subject toConcerto area is illustrated in Figure 4, and it appears that the size of the grid is suitablefor this area. Obviously, grid size can be easily modified for other Concerto areas. Themain purpose of the grid is to calculate numeric data for each grid square, e.g. totalemissions from heating or distribution of heating methods (DH, electricity, oil etc.).

Since building data of Figure 3 does not provide adequate numeric information for heatconsumption calculation purposes, another source must be used in the case of Lapua.

Population Register Centre provides accurate building data based on building permits.This data includes several useful details for heating sector purposes:


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Building location in coordinates

Building type (residential, commercial, public services)

Building year

Built floor area

Floor area divided by use (residential use, other)

Heating method (DH, electricity, light fuel oil, wood, ground heat pump, other)

Geographical location of these buildings can be seen in Figure 4. It is evident thatmajority of buildings is concentrated near city centre and also in the vicinity of thedistrict heating network.

4.3.2 Heat consumption of buildings

The calculation of heat consumption of the buildings in the Concerto area is based on rawdata on floor area and type of building in question, and a specific consumption per floorarea as a function of the year of construction.

The values for specific consumption are based on REMA model by VTT Technical ResearchCentre of Finland and present the development of heating needs in Finnish buildings. TheFigure 5 below illustrates this development.

Figure 5. Specific consumption as a function construction year.

As for the raw data from the Population Register Centre, some processing is needed to

refine the numbers into a more useful format. For each building, the total floor area isfirst divided into residential and commercial/public areas by a ratio defined by therecorded number of e.g. individual apartments to offices. This is naturally an over-simplification, but since there is no other systematic way to process data on the scalethat is needed here, it is unavoidable. The floor areas are then multiplied by the specificconsumption calculated according to the year of construction. If a year of majorrenovation is stated in the raw data, this is too taken into account in the calculation witha weighting of 25 % in the calculation of the specific consumption.

4.3.3 Areal fuel consumption and emissions

After each building has been estimated in terms of heat consumption, one importantaspect from Concerto point of view is to calculate fuel consumption and specific CO2emissions for each building. This calculation is straight-forward in the case of light fuel

0

50

100

150

200

250

300

350

1950 1960 1970 1980 1990 2000 2010 2020

Specificconsum

ption(kWh(m2)

Year of construction

Residential Commercial or public


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oil, wood or other combustible fuels. These boiler efficiencies and CO2 emission factorsare used in the case of Lapua:

Light fuel oil: 80% (efficiency), 0.266 kg/kWh (emission)

Wood: 80% (efficiency), 0 kg/kWh (emission)

Heavy fuel oil: 80% (efficiency), 0.284 kg/kWh (emission)

Natural gas: 80% (efficiency), 0.199 kg/kWh (emission)

However, electricity (in heating) and district heating must be treated in a different matterin terms of CO2emission calculation. Direct electricity heating has efficiency of 100%,but in case of heat pumps electricity efficiency of 300% can be used in order to calculateelectricity consumption. Since electricity used in heating is in Finland supplied by thenational grid, linked to the Nordic electricity market, an estimate for the emission factorof the grid electricity must be used. In the case of Finland emission factor of 0.245kg/kWh can be used1.

Consumption of district heating in a building is naturally calculated by using efficiency of100%, however, estimation of emission factor for DH is more complex issue due to thenature of heat produced in CHP plant. Since end-use of DH in the buildings is availableand pipeline losses can be calculated (by using robust estimation or by using simulationtool), total production of DH in the local energy company is therefore obtained. If insome case DH is produced by a heat plant, emission factor is calculated simply by usingplant efficiency and fuel mix data.

However, in the case of Lapua, DH is produced by a local energy company which utilisesCHP plant combined with fuel oil boiler (during peak demand) and bio-boiler (during CHPshutouts). Therefore, straight-forward calculation by using annual values is not possibleas in the heat plant case, and an hourly optimisation model, as illustrated in Figure 2,must be used in order to calculate fuel consumption and emission factor of DHproduction. Utilisation of this optimisation model is represented in Deliverable 1.3.2.

Also, in the case CHP plant fuels (and thereby emissions) must be divided betweenproduced electricity and DH. Energy based allocation or benefit allocation can be used.

In the case of Lapua, emission factor of DH depends highly on the fuel mix (peat fuel andwood chips). Currently Lapuan Energia uses 50% peat fuel in fuel mix for CHP and bio-boiler. However, in order to turn in a renewable direction, target for using 15% share ofpeat fuel has been set. In these cases, optimisation model provides (by using energybased allocation for CHP fuels) following emission factors:

50% peat fuel: 0.251 kg/kWh

15% peat fuel: 0.069 kg/kWh

If there are different scenarios to be analysed in the course of GIS utilisation, e.g. in thecase of DH network extension to be considered, these scenarios can be run through theDH production model or by making sensitivity analysis via several optimisation cases andconstructing a function for fuel use and emissions to be applied to building data. Overall,in order to sustain robustness and ease-of-use from the GIS software point of view thebasis of the GIS analysis should be in the modularity of the building data, not insimulation or optimisation methods.

1

A value for Finland with emissions allocated for power production. Source: W. Graus, E. Worrell. (2010).Methods for calculating CO2 intensity of power generation and consumption: A global perspective. EnergyPolicy.


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4.4 IMPLEMENTATION IN GIS-EMSSOFTWARE

4.4.1 Concerto area energy balances and total emission analysis

Provided that adequate data of Concerto area buildings is available, the process of GISanalysis can be relatively straight-forward, of course, depending on the complexity of

required results. As long as building data is transformed into reasonably aggregated formin the GIS software (e.g. aggregation according grid squares), several graphical resultforms can be created.

Geographical details illustrated in Figure 3 such as waterways, road networks etc. are notused in GIS analysis per se, but they can be utilised as visual aids for clarifying certainlocations. Overall, only the building data (spatial and numerical data for each building),definition of Concerto area and geographical grid are necessarily required for the analysisdescribed in this deliverable.

Figure 6. Suggestive process chart for phases of implementation of GIS analysis.

In Figure 6 the principles of GIS analysis process are illustrated. There are five majorphases in the GIS process, during which building data in Excel file is transformed intovisualisations of Concerto area energy situation:

1. Formatting and validation of building data:In this phase raw building data isexamined and transformed into more compact and well-defined form. Locationcoordinates are examined and validated in terms of given map projection.Irrelevant objects are removed and possible missing data is examined andreplaced with estimations if necessary.

2. Modeling and calculation of energy data: In this phase building data fromphase 1 is used to calculate heating energy for each building. Calculation of heat


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energy consumption is described in Chapter 4.3.2. Estimated efficiencies ofheating technologies are used to calculate fuel consumption for each building(electricity and DH are considered fuels at this stage). These fuel consumptionvalues and emission factors of each fuel (including electricity and DH) are used forcalculating CO2 emissions of each building. Optimisation model is used for

estimation of DH emission factor if necessary. This phase produces Excel files withfuel consumption, fuel type and emissions for each building.

3. Geocoding of building data: In this phase building data is imported into GISsoftware which creates tables for numeric data and defines geographical locationfor each building according to coordinates provided in the Excel file.

4. Aggregation of building data: In this phase building data from phase 3 isaggregated into grid squares by summing fuel consumption for each fuel andsumming emissions over all the buildings locating in corresponding grid square.This phase produces numeric data including total energy consumption per fueltype and total emissions for each grid square.

5. Visualisation of the aggregated data: In this phase graphic tools of GIS

software are used in order to visualize e.g. energy balances or emissions fromheating geographically. Numerous options for visualisation are available, e.g.,thematic maps or prism maps. Resulted visualised maps can be saved as picturesor used otherwise.

It must be noted that this GIS analysis process applies in terms of details to heatingsector. However, process described in Figure 6 could also be applied to analysis of otherissues with minor tuning.

4.4.2 Planning of decentralised energy production and DH expansion with EMS

Building data and GIS analysis described in Subchapter 4.4.1 can be used in EnergyManagement System approach of the GIS software. In this subchapter a suggestive

scheme for energy production planning is represented. It must be noted that processdescribed in Subchapter 4.4.1 is represented in detailed level, since it is alreadyimplemented in the case of Lapua, however, planning tool described here is at the levelof methodical scheme and, therefore, GIS process cannot yet be described in greatdetail.

In Figure 7 the principles of GIS process for decentralised power plant planning areillustrated. This chart is merely an illustrative suggestion for GIS analysis tool and it mustbe refined in terms of technical details when implemented with GIS software. There arethree major distinctive phases in the GIS process:

1. Search for suitable location:In this phase results from GIS analysis describedin Subchapter 4.4.1 are used for search of a suitable location for decentralisedpower plant. For example, thematic map of total heating fuel consumption can beused in analysis of high areal energy consumption density. GIS analysis of energybalances are used here only as aid and location can be selected regardless ofenergy density value of the location.

2. Selection of location and energy data management:In this phase a numberof grid squares (one or more) inside the Concerto area are selected as a tentativelocations for further study. In the GIS user interface selection of the squaresbrings out information about energy consumption of buildings in area selected.Also, the user interface enables settings for parameters, such as type andcapacity of a possible power plant, share of consumption to be covered. Theseparameters are used with cost and energy based functions constructed fromseveral results of optimisation and simulation runs. User can review these

functions as graphs and effect of chosen parameters on function value.


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3. Detailed presentation of location:In this phase selected location is reviewed incloser manner with detailed data in terms of costs (operational and investmentcosts), energy and emissions. If after considering the presented data user labelsthe selected location as feasible, a new scenario file for building data with updatedinformation in terms of new heating method can be created for other GIS analysis

purposes. If the location is infeasible, it is discarded and the GIS process can startfrom scratch.

Figure 7. Schematic process chart for spatial planning of decentralised energy production plant.

In the phase two of the process, on top of a range of different energy producingtechnologies, district heating network is also studied in detail either as an extension tothe main system in Lapua or as a separate areal network. For this purpose, the scale andefficiency of an imaginary network needs to be evaluated. This is accomplished by

utilising the network simulations done during the earlier phases of the project, and withknowledge on the Finnish systems in general. The key indicators here are two definitionsof energy density, MWh,a/m and sometimes kWh,a/m2, (yearly energy consumption perdistrict heating pipe meter, and energy consumption per land area) and the relative heatlosses (ratio of losses to needed production) in the system. The seemingly loose but inprincipal sound relation exists between the energy density and heat losses; heat lossesstart to increase rapidly when energy density becomes lower, see Figure 8.

By applying these indicators to Ala-Nurmo, a residential area part of the Concerto areawith an estimated heat demand of 1 100 MWh, utilising a preliminary network design forthe area we can calculate the energy density to be 0.74 MWh/m. According to the trendline in Figure 8 we get a relative heat loss of 18 %. This is a reasonable estimate, but as

can be seen in the illustration, the specific value of the heat losses can vary a lot from asystem to another. The current trend line is set up using data on all Finnish districtheating systems and will be revised to better deal with the operational environment in


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Lapua by making a few representative simulations with different types and sizes of arealnetworks.

Figure 8. Heat losses and energy densities of Finnish district heating systems.

Another example of useful relations in comparing different options are profit curves as afunction of plant capacity. The specific example illustrated below in Figure 9 describesjust this; the annual operational profit corresponding to a certain capacity. The exampleis from Hrsil SME-area, where a gasifier CHP plant is supplying energy for thesurrounding area. The curve is of course case specific one for Hrsil, but it illustratesboth the problem and a solution to issues always present in designing, sizing the plantand analysing profitability.

Figure 9. Annual profit curve for a gasifier and CHP engine in Hrsil SME-area.

The profit curve illustrated in Figure 9 only applies to Hrsil case in which heat demandis based on specific consumption estimates for buildings linked to the CHP plant.Therefore, this curve applies to fixed heat load only (and synthesis gas consumption).

In the general case examined by GIS planning tool, heat load will vary according to areaselected for analysis. Therefore, annual profit estimate should be a function of heat loadin addition to plant capacity. In Figure 10 an example of this kind of function is

illustrated. This function is based on several optimisation runs performed on gasifier andCHP plant producing heat (with supporting oil boiler) for local demand (hourly

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 1 2 3 4 5 6 7 8

Heatlosses(-

)

Heat density (MWh/m)

40 000

42 000

44 000

46 000

48 000

50 000

52 000

200 250 300 350 400 450 500 550

Annualprofit()

Gasifier capacity (kW)


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consumption data based on outdoor temperature) and electricity to grid with feed-intariff being effective.

Figure 10. Annual operational profit of gasifier and CHP engine as function of gasifier capacity (kW)and total annual heat load (MWh).

Baseline case for the optimisation is the following:

Gasifier capacity: 335 kW

CHP capacity: 100 kWe, 200 kWh

Annual heat load: 1170 MWh (peak load 411 kW)

No extraneous synthesis gas utilisation

The baseline case above was varied with factor of 50% to 150% subject to both plantcapacity and annual heat load. In the case of varying plant capacity, ratio of CHP enginecapacity to gasifier capacity was fixed. It is evident from Figure 10 how increasing heatload affects profitability of low capacity plant, since more and more heat must beproduced by oil boiler and energy entrepreneur sells produced heat for a price lower thanoil price (see Deliverable D2L.4.1). It is also evident that for different heat loads profitcurve as function of plant capacity follows roughly the trend of Figure 9.

In the planning tool described in Figure 7 functions similar to Figure 10 are used for cost

estimation of planned energy production plant. It must be noted that this function mustbe constructed separately for each Concerto area by using country-specific hourly heatconsumption data for optimisation.

4.4.3 Geographical representation of local energy production

Geographical locations of energy production units in the Concerto area can be illustratedeasily as a separate extra layer in map interface of GIS software. As there are merelyfew production plants in area this size, these units can be managed as separate units asopposed to GIS processing of thousands of consumers (buildings). These power plants,e.g. CHP plants, wind mills, heat plants, can have numeric data concerning technicalparameters, annual heat and electricity production or emissions attached to the unitillustrated in the map window for merely information purposes.


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4.5 CASE STUDIES

In this deliverable results from case studies are based on GIS analysis (described in4.4.1) of energy balances of Lapua Concerto area. These case studies are illustrativeexamples of performance GIS tool and are not meant to be examined in numeric detail.However, in order to enlighten the energy balance of the Concerto area, total annual

values of heat energy consumption (not fuel energy consumption) for each heatingmethod estimated from validated statistics as described in subchapter 4.3.2 can berepresented:

Districtheating

Light fueloil

Electricalheating

Wood basedheating

Other heatingmethods

Total heatconsumption

44 887 MWh 30 538 MWh 28 777 MWh 8 544 MWh 5 510 MWh 118 256 MWh

First part of case study consists of GIS analysis on potential expandability of districtheating network. In Figure 11 thematic map of DH consumption in Concerto area isrepresented. It is distinctive how areas around DH network have high energy densities as

usually is the case in communal district heating infrastructure. DH consumption is clearlyconcentrated in city centre but DH network is expanded also into outer regions of Lapua.

In order to analyse potential areas for DH network extension, buildings using otherheating methods must be examined in terms of location subject to DH network. InFigures 11-14 heat energy consumption of buildings is illustrated in the case of majorother heating fuels, i.e. light fuel oil, electrical heating and wood fuel. These figures pointout that there seems to be lucrative areas for DH producer to expand to. Especially oilheating is still used in significant volume even in the grid squares containing DH network.From the renewable point of view replacing wood fuel with DH produced partly by peatfuel is not reasonable, and furthermore areas with high wood fuel density seem to belocated in outer regions of Lapua. In the case of electrical heating there are distinctivecoherent areas of high density, especially south of city centre, to be considered. It is,

however, always a matter of costs when it comes to investments on expansiveinfrastructure. These cost effects should be analysed in compliance with this GISanalysis.

Second part of the demonstrative case study is a scenario analysis of total emissions inConcerto area in terms of differentiation of parameters for heating methods. In this casebuilding data was modified for district heating and electrical heating. There are twodifferent scenarios to be analysed:

1. Base scenario:Original building data with 50% peat fuel share in CHP fuel mixand electrical heating remaining unchanged.

2. Renewable scenario:Peat fuel share in CHP fuel mix lowered into 15% and allthe electrical heating replaced by air heat pumps with ceteris paribusin terms of

other building data.

In Figure 15 prism map of total emissions in Lapua Concerto area in base scenario isillustrated. It must be noted that this prism map is covered with layer consisting ofthematic map of DH use from Figure 11 in order to visualise location of DH network interms of emissions. It is obvious that high volume of DH produced mainly by peat fuel isresponsible of major part of CO2emissions. Total CO2emission values for Concerto areafor the base scenario are 41.5 kt and 27.7 kt for the renewable scenario.

However, as is evident from Figure 16 where renewable scenario is used, increase ofwood fuel in CHP production has significant effect on total emissions. Change fromelectrical heating to heat pumps is not as evident due to the lower volume of electricalheating when compared to DH. The purpose of this GIS example is to demonstrate

scenario based analysis in terms of energy balance of Concerto area. It is relativelyeffortless to produce this kind of scenario analysis with consistent and valid building data.


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Figure 11. District heat consumption and DH network in Concerto area of Lapua.

Figure 12. Light fuel oil based heat consumption in Concerto area of Lapua.


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Figure 13. Electricity consumption (for heating) in Concerto area of Lapua.

Figure 14. Wood fuel based heat consumption in Concerto area of Lapua.


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Figure 15. Prism map of CO2emissions in base scenario with DH consumption surface.

Figure 16. Prism map of CO2emissions in renewable scenario with DH consumption surface.


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4.6 PRELIMINARY RESULTS

The case study of Lapua above utilising GIS software in order to visualize energyconsumption and renewable potential was created as an example or a template for otherConcerto areas. This geographical analysis illustrates that having an extensive andvalidated geographically linked numeric data concerning buildings of the area a robust

and streamlined process by using GIS software can be used in order to producedistinctive and visual information about energy use, renewable energy or CO2emissions.This GIS process can also produce results for the residents of Concerto area, e.g., to beshown on the project website.

For the GIS analysis of heat energy use and balances in any Concerto area, followingdata is required:

1. Building data

Coordinates of the building (map projection type required)

Building type (residential, commercial, industrial, services etc.)

Floor area (m

2

) Division of floor area by use (residential use, other)

Building year

Possible renovation year

Heating fuel type (DH, electricity, wood, fuel oil etc.)

Energy consumption data if available

2. Concerto area information

Geographical definition of area (in form usable by GIS software)

Grid or other area division

3. Energy data for heat energy calculation/simulation

Specific heat consumption as a function of construction year (kWh/m2,a)

Efficiencies for heating methods (boilers, heat pumps)

Emission factors for fuels and electricity (kg/kWh)

4. Energy data for district heating fuel/emission allocation

Emission factor of consumed district heat if available (kg/kWh)

CHP information (see Deliverable 1.3.2)

Heat plant data (efficiency, fuel mix)

Emission factors for fuels (kg/kWh)

In the case of Lapua the analysis was performed for heat energy only as an example.However, this analysis can be also extended for electricity consumption and production ifnecessary. GIS analysis of electricity consumption and production can be useful if there isa closed electricity grid in the area with possibilities of expanding the grid consideredcombined with decentralized local electricity producers. However, in the case of nationalgrid covering the area and centralized large scale electricity production, it is notreasonable to analyse this issue other than by calculating geographical electricitybalances if necessary.


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5 Conclusion

Significant ground work has been carried out in the effort of developing a GIS based EMStool envisaged in the deliverable description. The data base has been compiled and muchof the necessary refining of has data been done. A 250 meter grid presentation format

was chosen as means of illustrating the energy related data in the Concerto area, and forthis purpose, the aggregation of the more detailed data has been accomplished. Whilethe aggregated data is more meaningful for presentation purposes, the quality and levelof detail in the data base is unchanged and can be utilised when necessary.

The EMS tool is building on the results of the work done previously during the project,utilising them wherever reasonable and presenting the results in a way that will benefitthe decision makers on a city level. The simulation and optimisation results will be linkedto the tool by using verified functions and relations. This combination and themethodology of building on the past results enhance the results of the tool way past thecase specific studies carried out previously and represent a natural continuum for thework accomplished so far.

In the next phase of the development process, a set of tools in form of MapBasicProfessional functions are implemented. These tools aim to make the updating of database effortless, to illustrate the results easily and to give the user means of carrying outarea specific studies, e.g. the extension of the district heating network or evaluatingdifferent energy technologies in a specified area.

m28 solution d133 a gis ems tool 2

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