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Models in local energy planningThe REAM model as an example

2008-04-11

Profu i Gö teborg AB

G ö taforsliden 13, nedre

431 34 M ö lndal

031 – 720 83 90

031 – 720 83 93

031 – 720 83 99

070 – 564 28 20

john.johnsson@profu.se

www.profu.se

Profu

Adress:

Telefon:

Direkt:

Telefax:

Mobil:

E- post:

Hemsida:

John Johnsson

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Profu is an independent consulting and

research firm founded in 1987. Profu has

18 employees who provide our customers

with qualified analyses and recommen-

dations in the fields of energy, climate,

environment and waste.Pro

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Profu and energy planning

• Participated in 35-40 energy plans/strategies

• Energy planning since 1980, both as researchersand as consultants

• Handbooks in energy planning, Swedish andinternational version

• Model development: KRAM in Skaraborg, 1997REAM for EU (3-nity), 2008

• During 2007-08, finished local projects inHärnösand and Hjo. On-going projects in Habo,Mullsjö and Mark.Participating in Path-to-RES and 3-nity projects

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The planning process

Introduction Orientation Main studyEvaluationDecision

Carrythrough

RapportsFeed-back

Generate two or morescenarios to describe theuncertain future. Energy

flows, technologies,emissions and costs

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The model in the planning process

Generate consistent scenarios for the plan/strategy

• Handle all alternatives on the same basis – both supply technologies and efficiency measures

• Handle the complex energy system, both in detail and to give the comprehensive view of the system

• Handle the influences to the energy system: energy prices, taxes, policy instruments, new technologies, emissions restrictions, …

• Analysis the competition between different solutions

• Follow-up the consequences of the system development

• …

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0

20

40

60

80

100

120

140

160

Reference scenario 2015, -5% 2015, -10%

[GWh]

Biopellets

Wood

Biomass

Light oil

Elc

Operation elc50%

14%

36%

42%

7%

51%

42%

7%

51%

Energy supply – HjoExcl. Industry and Transportation - 2015

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0

1

2

3

4

5

6

Reference scenario 2015, -5% 2015, -10%

[kton]

Single family - Other

District heating

Public Service

Privat service

Multifamily-CC

Single family - CC

Local CO2 emissions - HjoHeating sector - 2015

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Tools in the planning process

• Specialised models such as REAM and KRAM

• Excel

• Other models

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The REAM model

• Developed and used during 2006-2008 in the 3-nity project

• Based on the KRAM model and Profus and IFEsexperiences of local energy planning

• Developed by Profu

• Will be distributed from May 2008

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Result presentation

Calculation algorithm

Model structure

General description

The REAM model

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The REAM model, 1

• Simulation model for local/regional energy planning

• The stationary energy system

• Geographical dimension

• Analyses development of the energy system over time

• Heating, Cooling and Electricity

• Flexible degree of detail

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The REAM model, 2

• Supply technologies as well as energy efficiency measures

• Large scale conversion technologies and small scale

• Includes costs, technologies and emissions

• Analyses the development on a least cost basis

• ReferensEnergySystem-oriented

• Language flexibility, partly unit flexibility

• File-explorer look-alike

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The REAM model

Result presentation

Calculation algorithm

Model structure

General description

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Principal RES

En

ergy

dem

an

dE

nergy

dem

an

d

Ind

ustry

Hou

seh

old

s/Servic

e

Distr

ict h

eat g

rid

Ele

ctric

ity g

rid

Power plant

Combined heat

and power

District heating

plant

Water/Wind/Solar/Wave

Oil

Bio energy

Gas

Oil/Gas

Bio

Coal/CokeCoal/CokeE

ner

gy

dem

an

dE

nergy

dem

an

d

Ind

ustry

Hou

seh

old

s/Servic

e

Distr

ict h

eat g

rid

Ele

ctric

ity g

rid

Power plant

Combined heat

and power

District heating

plant

Water/Wind/Solar/Wave

Oil

Bio energy

Gas

Oil/Gas

Bio

Coal/CokeCoal/Coke

Coo

ling

El a

pp

lian

ces

En

ergy d

eman

d

Hea

ting

En

ergy d

em

an

dE

nerg

y d

eman

d

boilers

Gas

Oil

Bio

Heat pump

Cooling device

Wood stove

Bio

Electricity

Oil

Gas

Coo

ling

El a

pp

lian

ces

En

ergy d

eman

d

Hea

ting

En

ergy d

em

an

dE

nerg

y d

eman

d

boilers

Gas

Oil

Bio

Heat pump

Cooling device

Wood stove

Bio

Electricity

Oil

Gas

Overview level Demand category level

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Energy system / model structure

Urban area

Region Demand technologies

Households

Industry

Primary sector

etc

Community 1

Rural area

Area Demand category

other

Buildings Direct elec. heating

Oil boiler

Wood stove

Cooling unit

Elec. to appliances

District heating

etc.

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Demand category

• Homogeneous demand groups

• Total energy demand (per type)

• Load curves

• Small Scale Technologies (demand technology)

• Extra investments (e.g. central heating and chimney/tank)

• Calculation rate

• VAT

• Emission fee share

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Small scale technologies

• Supply and efficiency technologies

• Heating, cooling and electricity

• The technologies are described by:

• Fuel(s) with maximal share

• Efficiency (per fuel), [%]

• Capacities (residual, upper, lower, fix), [EU/year]

• Grid connections

• Investment, [MU/EU]

• Life length, [years]

• Extra investments, [MU/EU]

• Variable cost (per fuel), [MU/EU]

• Emission coefficients (per fuel), [µg, mg, g /EUfuel]

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Input data for local energy planning

• Energy demand (per sector and geographical area)

• Supply technologies (per sector and geographical area)

• Technical data (e.g. efficiency and capacity)

• Economy (e.g. investments, fixed and variable cost)

• Emissions (e.g. Sulphur, NOx and CO2)

• Energy measures (per sector and geographical area)

• Technical data (e.g. reduction and capacity)

• Economy (e.g. investments, fixed and variable cost)

• Energy carriers• Prices

• Taxes

• Possible restrictions

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Sources to the present situation

Elc. grid statisticsDistrict heating statisticsLocal investigationsTenant associationsMunicipal estate companiesOther estate companiesEnergy companiesTechnical administrationsEnergy advisory serviceConsultantsNational investigationsThe chimney sweeperThe national statistic bureauKey persons

…

ReferenceEnergySystem to

identify unbalances in the numbersand to prepare for the modelanalysis

[GWh]

Oljepanna

Elpanna

Vedpanna

Direktel

VP

Olja/el

Olja/ved

Ved/el

Olja/ved/el

Pelletspanna

Fjärrvärme

VP-direktel

Oljepanna

Elpanna

Direktel

VP

BC olja/pellets

Olja/el

Fjärrvärme

Oljepanna

Elpanna

Direktel

VP

Olja/el

Fjärrvärme

BC olja/pellets

Oljepanna

Elpanna

Direktel

VP

Olja/el

Fjärrvärme

BC olja/pellets

Oljepanna

Elpanna

Vedpanna

Direktel

VP

Blockcentral

Olja/el

Olja/ved

Ved/el

Olja/ved/el

OP-Pulsonex

Pelletspanna

VP-direktel

Småhus i centralorten

0,9

1,6

0,4

15,0

3,6

0,3

4,3

1,0

1,5

0,3

5,7

1,1

1,2

1,8

0,6

15,8

1,4

0,4

6,3

0,4

Flerfamiljshus i

centralorten

2,5

0,0

4,0

1,0

0,0

0,0

11,4

3,3

0,0

4,2

0,4

12,7

0,3

0,0

1,1

0,0

0,0

3,9

0,0

Privata lokaler i

centralorten

0,0

0,0

1,2

0,0

4,3

0,0

0,0

0.1

0,0

0,0

3,9

0,0

Offentliga lokaler i

centralorten

0,0

0,0

0.1

0,0

4,3

1,2

0,5

6,2

8,0

2,4

0,3

0,1

4,8

2,1

0,8

0.0

1,5

1,5

Ej tätort

1,6

0,6

8,8

8,4

1,0

,4

0.0

1,9

0,6

35,7

18,9

5,3

4,9

29.4

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Flexibilities

• Structure

• Detail

• Language

• Monetary units

• Energy units: Watt or Joule

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The REAM presentation

Result presentation

Calculation algorithm

Model structure

General description

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Calculation algorithm – small scale

• The model is driven by the changes in the demand categories (market changes)

• Replacing phased-out capacity, alternative

• Replacing technologies with variable cost > new total cost

• The substitution is made on the basis of lowest total cost

• Alternatively is the development specified by the user

• The calculation of the total cost in a specific period is based on the assumptions only in this period →sequential calculations

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Substituting phased-out capacity

New technology

Energy

TimeT1 T2 T3 T4

ResidualTechnology 2

ResidualTechnology 1

T5

New technology

New technology

New technology

New technology

New technology

Total demand

New technology

New technology

New …

New …

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Supply and efficiency measures• Optimal level for supply vs. efficiency measures on a cost basis

• Supply and efficiency handles in the same way

Total demand, [GWh]

Specific cost, [€/MWh]

Cost curveEfficiency measures

Cost curveSupply technology 1

Tota

l dem

and

Optimal level of efficiency measures

Efficiency package 1

E -

P 2

E –

P 3

E –

P …

E –

P …

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Calculation algorithm – large scale• Dispatch model (total variable cost ranking)

• Alternatively user specified production schedule

Example - District heating production

0

5

10

15

20

25

30

35

40

45

50

Jan Jan Feb Feb Mar Mar Apr Apr May May Jun Jun Jul Jul Aug Aug Sep Sep Oct Oct Nov Nov Dec Dec

[GWh]

Biooil-HP

Biomass-CHP

Municipal Waste-CHP

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The REAM presentation

Result presentation

Calculation algorithm

Model structure

General description

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Result presentation

• Flexible presentation system

• File-explorer look-alike structure

• Tables and diagrams

• Possible to export to Excel, pdf-files, …

• Crystal reports-system

• Results from all levels, geographical areas and energy types

• Energy supply

• Energy production

• Emissions (also external emissions from fuels)

• Costs (fuel costs, taxes, fee costs, fixed and variable cost and capital cost)

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Results in the model

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Extra

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