Case Study - Kisa

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Energy System Modelling

and OptimisationShahnaz Amiri

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EU energy policy:‘‘20-20-20’’targets In 2008 the European parliament and council also agreed upon

a legally binding climate change package known, as the‘‘20-20-

20’’targets, which aim to:

• Reduce the CO2 emissions to a level that is at least 20%below the 1990 emission level,

• Reduce primary energy use by 20% compared to projectedlevels for 2020,

• To have 20% of the energy use within the EU to come fromrenewable energy (compared to 2005) until 2020.(European Comission, 2011)

• A 10% share of 'green fuels/biofuels' in transport is alsoincluded 2


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Sweden

• EU: To have 20% of the energy use within the EU to come

from renewable energy (compared to 2005) until 2020.(European Comission, 2011).

• In Sweden, a target of 50% renewable energy for year 2020has been proposed by the Swedish Government.

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Local, Regional, National

Energy System Modellingby

MODEST

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MODEST

Method for O ptimisation of Dynamic Energy

Systems with Time dependent components and

boundary conditions.

With the MODEST model framework a representation of anenergy system can be made.

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MODEST

• Is an energy system optimisation model ,use linearprogramming (LP) for cost minimisation and has beendeveloped in the Linköping University (IEI), Department of

Management and Engineering, Division of Energy Systems(1986, Backlund L).

• Is used for operational optimisation and evaluating

economic and enviromental consequences of future changesin the Energy system.

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MODEST

• MODEST uses the optimisation method linear programming tocalculates how energy demand should be satisfied at lowest possible cost:

The total system cost is minimised through the optimisation. The resultincludes the total cost for satisfying the DH-demand, as well as theoptimal investments that should be made , plant operation, marginal costfor supplying various energy forms and emissions the system causes.

• Energy demand could be power ,heat, biogas-production and coolinggeneration in local, regional and national energy systems.

• The best system design and operation is obtained considering presentand possible plants, energy costs, available resources etc.

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Energyproduction

Energydistribution

Energydemand

System boundary for MODEST

Industry

sector

Residential

sector

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MODESTSummary

• Country, municipality

• Electricity, heat, biogasproduction and cooling generation

• Short and long-term variations

• Cost minimisation with the optimisation method linearprogramming.

• Investments in new plants: type, size

• Given energy service demand : energy conservation does notreduce the benefit of energy use.

Which combinations of energy sources, conversion plants and energy

conservation measures are most beneficial?

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Advantages of MODEST

• With the MODEST model framework a comprehensive representationof an energy system can be made, where the level of detail can bechosen.

• MODEST has arbitrary division possibilities concerning space (e.g. cityor province), sector (e.g. heating or industrial processes), time (e.g. day,season) and quality (e.g. heat, electricity).

• MODEST has a flexible detailed time division, which can reflect demandpeaks and long-term variations in energy demand and other parameterse.g electricity prices, capacity. It can be adapted to real variations.

• Flexible boundary system which does that the MODEST can be usedboth for analyzing municipalities district heating systems and for nationalenergy systems.

• Short time concerning optimization. 11


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Documentation Optimisation

Results

Totalcost

Investments Marginal costs operation

Diagram

Input data

Time division

Plants & equipments

Supply Conservation

Costs and other properties

Emission

M

O

D

E

ST

Energy demand

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MODEST

• MODEST has been used to analyze district-heating systems,electricity production and cooling generation in manySwedish municipalities.

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Responsible institute

MODEST

Institution: Linköping University/IEI/Division of

Energy SystemsContact person: Bahram Moshfegh

Phone: +46- 13-28 11 58

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Modelling of a Swedish

Integrated Energy System

Department of Management and Engineering, Division of Energy

Systems, Linköping University

Ph. D. Student Shahnaz Amiri

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Integrated ESCO and Industries in municipality

of Kisa

• District heating in the society of Kisa is locally produced from biofuels. The Tekniska Verken (ESCO]owns the district heating system. The oilboilers are used as a reserve and peak load boilers

• Södra Timber Kinda is a sawmill industry unit and is situated in the

municipality of Kisa in the county of Östergötland. The sawmill unit, hasbeen linked to the municipalities district heating network. The heatproduced in the boilers at Södra Timber Kinda covers not only theirown energy needs but also deliver waste heat (surplus energy in the formof heat spillage ) to the Kisa-municipal district heating system.

• Swedish Tissue industry unit is located in the municipality of Kisa. The boilers owned by Södra Timber Kinda are also used to producesteam for the Swedish Tissue company.

• The increasing use of district heating contributes to reducingmunicipalities’ need for fossil fuels. This in turn contributes to reducing

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Modelling of a Swedish

Integrated Energy System

• Integration Possibilities between municipal DistrictHeating System and Industry.

• Analysing the Integrated System from Cost Efficiency

point of view.

• Analyses of synergies between Municipal District HeatingSystem and Industry.

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Simplified view of integration betweenmunicipal district heating system in Kisa

(ESCO) and Industries (Sawmill industry andPaper mill industry).

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DH-Demand

DH-network

Biofuel

Swedish Tissue, Paper Mill Industry

Värdgårdsskolan, Kisa Municipal DH-System

Oil

Waste

heat

MWh/år

Waste

heat

MWh/år

Paper based waste

products

Steam

network

Södra Timber Kinda, Sawmill Industry

Steam

Demand

Existing

Future

Heat

Steam

Prod Biofuel

Waste heat

Biofuel

Oil

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Biofuel:waste products (bark, chips)


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Modelling of integrated municipal district

heating system (ESCO) and Industries .

• In this study, the integrated energy system is modelled

• An appropriate time division is defined and the components'properties are given as input data (e.g. electricity prices,efficiencies, power).

• The total system cost is minimised through the optimisation. The

result includes the total cost for satisfying the DH-demand, as well as the optimal investments and operation of the integratedsystem.

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Objectives

• The objectives of this study are: – to present a model for a integrated energy system in order

to achieve a cost-efficient system, and

– to assess the effects of the integration on Kisa municipaldistrict heating system and the environment.

– to analyze the sensitivity of the system by varyingimportant parameters (electricity and fuel prices,

electricity certificate price ) – to analyze the conditions for connecting a combined heat

and power (CHP) plant to the system.

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Case study design

• Building the model.

• Collection of data for the model.

• Design of scenarios.• Sensitivity analysis

• Results

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Bt BP

SV

VIVV

5 MW

5 MW

OPEo

Bi

Bb

5MW (including FGC)

Ob

2,4 MW

SD

ALBm SP

Heat

Steam

1,5MW

OB

CU

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Design of scenarios

• Case 1: The existing integrated Energy system

• Case 2: Case 1+Biofuel from Sawmill industry to theKisa municipal DH-system.

• Case 3 : Case 1+deliver waste heat (surplus energyin the form of heat spillage ) from paper millIndustry to the Kisa municipal DH- system.

• Case 4: Case 1+Case 2+Case 3• Case 5: Case 1+CHP

• Case 6: Case 4+CHP

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Sensitivity analysis

Changing of following parameters for scenarios:

•

Electricity prices (Euro/MWh)• Biofuel prices (Euro/MWh)

• Electricity certificate trading market price

(Euro/MWh )

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Sensitivity analysis

Changing of important parameters in Case 1, Case 4

and Case 6 and analysis of the system:

• Case S1 = Case 1+25% increasing electricity price.• Case S2 = Case 4+25% increasing electricity price.

• Case S3 = Case 6+25% increasing electricity price.

• Case S4 = Case 1+25% increasing Biofuel price.



• Case S7 = Case 1+25% increasing electricity certificate trading market price.



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Results

• Amount of used biofuel, oil.

• Energy production (heat and electricity)

• System cost and profit.• Local and global CO2 emissions.

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CO2 emissions and marginal electricity

production accounting modell

• Bio-fuel is assumed to be CO2 neutral, according to theIPCC guidelines for national greenhouse gas accounting.

• Coal-fired condensing power (CFCP) plants have been

assumed to be the short term marginal power plant in theEuropean electricity system and the electricity market is fullyderegulated.

• The local electricity production e.g. by bio-fuel CHP power

plants will replace the electricity produced by coal-firedcondensed power plants. This means that CO2 emissions canbe credited for the local electricity production:

1 GWh electricity = 974 tonne CO2

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System Cost

• The aim of the optimisation is to find that way to satisfy theenergy demand which has the lowest system cost for acertain number of years.

• The system cost is the present value of capital costs, fixedcosts, costs related to the output power and costsassociated with the amount of energy used (energy

costs). The later includes costs of purchased fuel,electricity and heat as well as operation and

maintenance costs.

• The sum of all energy costs discounted to their present values is one part of the system cost.

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System Cost

• The present cost of using the energy concerned during theyear studied. The energy costs of each year are discounted totheir present values and added. The sum of the discountedenergy costs is:

C.dfn

C= Fuel flow[MW] the time period length per yearEnergy cost per MWh [Kr/MWh].

• dfn is discounting factor :dfn=(1-(1+r)-P )/r

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Sweden’s Green Electricity certificate

• Sweden’s Green Electricity certificate scheme is a market-based supportsystem to assist expansion of production of electricity in Sweden fromrenewable sources.The objective of the system is to increase theproduction of electricity from such energy sources by 25 TWh by 2020

(as compared with production in 2002). The scheme is intended to rununtil the end of 2035.

• Producers of renewable electricity receive one certificate unit from thestate for each MWh of electricity that they produce. This provides an

incentive to expand electricity production from renewable energy sourcesand/or using new technology. Demand for certificates is created by thefact that all electricity suppliers are required to buy certificatescorresponding to a certain proportion (their quota) of their electricitysales or use.

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Sweden’s Green Electricity certificate

• In 2009, users were required to buy certificatescorresponding to 17.0 % of their electricity use.

• The price of certificates depends on the interaction of supplyand demand on a c ompetitive market. Several factors affectthe price levels, such as the expected demand for electricity.If the market is expecting, for example, a shortage of

electricity, then the price of certificates will rise, and vice versa .

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Policy instrumentsElectricity certificates (250 kr/MWh renewble electricity, year 2010)

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Documents

Case Study - Kisa