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AA MULTICRITERIA ANALYSIS OF ENERGY MULTICRITERIA ANALYSIS OF ENERGY

SUPPLY OPTIONS FOR MUNICIPAL AND SUPPLY OPTIONS FOR MUNICIPAL AND

RESIDENTIAL CUSTOMERSRESIDENTIAL CUSTOMERS

AA MULTICRITERIA ANALYSIS OF ENERGY MULTICRITERIA ANALYSIS OF ENERGY

SUPPLY OPTIONS FOR MUNICIPAL AND SUPPLY OPTIONS FOR MUNICIPAL AND

RESIDENTIAL CUSTOMERSRESIDENTIAL CUSTOMERS

Tadeusz BewszkoTadeusz Bewszko

Rzeszow University of TechnologyRzeszow University of TechnologyFaculty of Electrical and Computer Engineering, PolandFaculty of Electrical and Computer Engineering, Poland

________________________________________________________________________________________________________________________________________________The 20th Workshop on Complex Systems Modeling, The 20th Workshop on Complex Systems Modeling,

IIASA, August 28-30, 2006. IIASA, August 28-30, 2006.

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CCONTENTS:ONTENTS:

1. Energy planning as a multicriteria problem.

2. Literature review of application of multi-criteria methods to

energy supply of residential customers.

3. Problem formulation.

4. New method of selecting energy supplying option for residential

customers.

5. Application of new method to real life problems.

6. Results, future work.

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EENERGY PLANNING AS A MULTICRITERIA PROBLEM:NERGY PLANNING AS A MULTICRITERIA PROBLEM:

Heat pump

Electricity

Electricity (night)

Coal

Biomass

Gas

LPG

Oil

District heat

Investment cost

Total operation cost

LCC Cost

Emission of CO2

Emission of SO2

Emission of NOX

Efficiency

Comfort of use

Used resources

ENERGY DEMANDS

Heating Hot water Cooking

Supply elect. applliances

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LLITERATURE REVIEW OF APPLICATION OF ITERATURE REVIEW OF APPLICATION OF MULTICRITERIA METHODS TO ENERGY SUPPLY OF MULTICRITERIA METHODS TO ENERGY SUPPLY OF RESIDENTIAL CUSTOMERS:RESIDENTIAL CUSTOMERS:

AAuutthhoorrss ((YYeeaarr)) MMeetthhooddss OObbjjeecctt NNuummbbeerr ooff eenneerr.. ddeemmaannddss##

NNuummbbeerr ooff ccrriitteerriiaa

IInntteerraaccttiivvee MMooddeell

Mezher et al. (1998) MOLP/GP(L) Households sector of Lebanon

6 8 No Determin.

Chedid et al. (1999) FL MOLP Households sector of Lebanon

9 6 Yes Fuzzy

Mezher i inni (1998) MOLP/GP(L) Sektor komunalny w Libanie

6 8 Nie Determin.

Chedid i inni (1999) FL MOLP Sektor komunalny w Libanie

9 6 Tak Rozmyty

Ramanathan i inni (1993) MOLP/GP(L) Miasto Madras, Indie 4 8 Nie Determin.

Ramanathan i inni (1995) MOLP/GP(W) + AHP

Miasto Madras, Indie

4 12 Nie Determin.

Budynek jednorodzinny

2 (o, c.w.u.) 2 (o, c.w.u.)

2 3

Nie Nie Determin.

Jędrzejuk (1995)

MOLP/ NPM

Grupa budynków jednorodzinnych

2 (o, c.w.u.) 2 (o, c.w.u.)

2 3

Nie Nie Determin.

Jędrzejuk (1999a) MOLP/ NPM Budynek jednorodzinny 2 (o, c.w.u.) 2 Nie Determin.

Jędrzejuk, Owczarek (1999c)

MOLP/ NPM Budynek jednorodzinny

2 (o, c.w.u.) 3 Nie Determin.

Jędrzejuk, Marks (2002c) MOLP/ NPM Budynek wielorodzinny

1 (o) 3 Nie Determin.

# h – energy for heating, w – energy for hot water

Ramanathan et al. (1993) MOLP/GP(L) Households sector of city Madras 4 8 No Determin.

Ramanathan et al. (1995) MOLP/GP(W) + AHP

Households sector of city Madras

4 12 No Determin.

Single family house

2 (h, w.) 2 (h, w.)

2 3

No No Determin.

Jędrzejuk (1995)

MOLP/ NPM

Group of single family houses

2 (h, w.) 2 (h, w.)

2 3

No No Determin.

Jędrzejuk (1999a) MOLP/ NPM Single family house 2 (h, w.) 2 No Determin.

Jędrzejuk, Owczarek (1999c)

MOLP/ NPM Single family house

2 (h, w.) 3 No Determin.

Kotarba (1985) WSM

Single family hause 1 (h) 7 No Determin.

Jędrzejuk, Marks (1999b) MOLP/ NPM Multifamily block

2 (h, w.) 3 No Determin.

Jędrzejuk, Marks (2002c) MOLP/ NPM Multifamily block

1 (h) 3 No Determin.

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CCONCLUSIONS AFTER LITERATURE REVIEW:ONCLUSIONS AFTER LITERATURE REVIEW:

Modern, friendly for DM, interactive multi-objective decision making methods have not been used so far to solve decision-making problem of selecting optimal energy supplying option for municipal and residential customers.

All energy demands for residential customers have not been taken into account.

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PPROBLEM FORMULATION:ROBLEM FORMULATION:

Examination of possibility of application and effectiveness of interactive multi-objective decision making methods to solve decision-making problem of selecting optimal energy supplying option for municipal and residential customers.

AAIM OF WORK:IM OF WORK:

- to build mathematical model of decision problem of selecting optimal energy supplying option for municipal and residential customers,

- to make multicriteria analyses with different sets of decision criteria by using existing software implementation of interactive multiobjective decision making method

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CCHOOSING INTERACTIVE MULTI-OBJECTIVE DECISION HOOSING INTERACTIVE MULTI-OBJECTIVE DECISION MAKING METHOD MAKING METHOD

There exist a lot of interactive multi-objective decision making methods

A few of existing software implementation of interactive multi-objective decision making methods

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CCHOOSING INTERACTIVE MULTI-OBJECTIVE DECISION HOOSING INTERACTIVE MULTI-OBJECTIVE DECISION MAKING METHOD MAKING METHOD

Software Methodology Publication

MCMA Aspiration-Reservation Based Decision Support (ARBDS)

(Makowski, 1994a) (Makowski, 1994b) (Granat, Makowski, 1998)

MOLIP Interactive classification based algorithm

(Vassilev, Staykov i inni 2003)

MOLP-16 Satisficing trade-off method and STEM

(Vassilev, Atanassov i inni 1993)

NET-LBS Light Beam Search method (Jaszkiewicz, Słowiński 1999)

NIMBUS Multiobjective proximal bundle (MPB)

(Miettinen, 1999) (Miettinen, Mäkelä, 2000)

VIG Dynamic reference direction approach

(Korhonen, 1998)

Method of model generation

Software Possibility of solving MOILP

Max number of criteria Modelling

Languages Text file Algebraic

equations

MCMA Yes No limitations Yes No No

MOLIP Yes No limitations No No Yes

MOLP-16 No No limitations No No Yes

NET-LBS Yes No limitations Yes No No

NIMBUS Yes No limitations No No Yes

VIG No 10 No Yes No

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MMETHOD OF SELECTING ENERGY SUPPLYING OPTION FOR ETHOD OF SELECTING ENERGY SUPPLYING OPTION FOR MUNICIPAL AND RESIDENTIAL CUSTOMERSMUNICIPAL AND RESIDENTIAL CUSTOMERS

1. Building a multicriteria model of decision problem of supplying energy to a selected customer.

2. Making multicriteria analyses with different sets of decision criteria.

3. The use of a scenario analysis for supporting decision–making process which involves uncertainty.

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MMULTICRITERIA MODELLING OF DECISION PROBLEM ULTICRITERIA MODELLING OF DECISION PROBLEM OF SUPPLYING ENERGY TO RESIDENTIAL CUSTOMERS:OF SUPPLYING ENERGY TO RESIDENTIAL CUSTOMERS:

Specification of: - set of energy demands of customer- set of available energy carriers - set of energy conversion technologies

Define decision variables

Define constrains

Define outcome variables

Verification of the model

DETERMINISTIC MOILP MODEL OF DECISION PROBLEM

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TTYPES OF USERSYPES OF USERS AND ANALYSES AND ANALYSES::

Economical customer: economical and comfort criteria

Environmentally friendly customer: economical, ecological, and

comfort criteria

Policy maker: economical, ecological, and energy safety criteria

Criteria

Economical Comfort Ecological Energy safety

INV OMC LCC TAC

ComUse TotEmCO2

TotEmSO2 TotEmNO2 TotEmPM

ImpRes TotRes

InvTotSysEff

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A scenario analysis includes: an evaluation of scenarios of changes in future of values of some

model parameters, examination of changes of values of some outcome variables for

some solutions taken from multi-criteria analyses.

AA SCENARIO ANALYSIS FOR SUPPORTING DECISION– SCENARIO ANALYSIS FOR SUPPORTING DECISION–MAKING PROBLEM WHICH INVOLVES UNCERTAINTY:MAKING PROBLEM WHICH INVOLVES UNCERTAINTY:

Uncertainty - possibility changes of parameters of the mathematical model (prices of energy carriers, energy demands).

A SCENARIO ANALYSIS – one of the methods of coping with uncertainty.

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AAPPLICATION OF THE METHOD TO REAL LIFE PROBLEMS:PPLICATION OF THE METHOD TO REAL LIFE PROBLEMS:

Selecting energy supplying option for: a single family house (two users: B1, B2), a flat in multifamily block (two users: M1, M2).

For each decision making problem: multicriteria model of problem has been built and used for multicriteria analyses and scenario analysis.

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SSELECTING ENERGY SUPPLYING OPTION FOR SINGLE ELECTING ENERGY SUPPLYING OPTION FOR SINGLE FAMILY HOUSE:FAMILY HOUSE:

Design officeAGROBISP Type WB-3344

Two single family houses (user B1, B2), Both buildings take advantage of all available technical solutions to minimize

thermal losses, Number of inhabitants are known, Energy demands were taken from statistical data, Investments costs and prices of energy carriers were taken from Rzeszow area.

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MMODEL DESCRIPTIONS:ODEL DESCRIPTIONS:

Energy demands j 0 1 2 3 i Heating Hot water Cooking Elect.

0 Coal + 1 Natural gas + + + 2 LPG + + + 3 Fuel oil + + 4 Biofuel (fuelwood) + 5 District heat + + 6 Ther. Elect. Heat pump (tariff G11 ‘day’) + + 7 Thermal electricity (tariff G11 ‘day’) + + + +

Energy carriers

8 Thermal electricity (tariff G12 ‘night’) + +

Decision variables:

xij - energy supplied by the i-th carrier j-th demand

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OOUTCOMEUTCOME VARIABLES:VARIABLES:

INV - The Investment Cost of a Whole System, [PLN] OMC – The Total Annual Operation and Maintenance Cost, [PLN] TAC – The Total Annual Cost of Using Energy System, [PLN] LCC – The Life Cycle Cost, [PLN] TotEmk – The Total Emission of Different Pollutants, [kg];

k POL = {CO2, SO2, NO2, PM}

Inv_TotSysEff – The Total System Efficiency, [-]

TotRes – The Total Amount of Used Resources, [GJ] ImpRes – The Total Amount of Imported Resources, [GJ]

ComUse – Comfort of Use, [-]

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Four types of analyses were carried out:

Analysis A: economical criteria (INV, OMC, TAC) and comfort criterion (ComUse),

Analysis B: economical criterion (LCC) and comfort criterion (ComUse),

Analysis C: economical criteria (INV, OMC), comfort criterion (ComUse) and ecological criteria (TotEmCO2, TotEmSO2, TotEmPM),

Analysis D: economical criteria (INV, OMC), comfort criterion (ComUse), ecological criteria (TotEmCO2, TotEmSO2) and energy safety criterion (ImpRes).

VVARIOUS TYPES OF USERS AND ANALYSES: ARIOUS TYPES OF USERS AND ANALYSES:

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RRESULTS OF MULTICRITERIA ANALYSIS (1):ESULTS OF MULTICRITERIA ANALYSIS (1):

Table 1. Results of analysis A

Outcomes

of the solutions

INV OMC TAC Com Use

Input (a mix of energy carriers and

technologies)

PLN PLN PLN - Heating Hot water Cooking Elect.

A.1 21 535 2 901 5 732 1 Gas Gas Gas Elec.

A.2 16 735 2 632 4 832 0 Biomass Elec. night Elec. Elec.

A.3 21 335 3 069 5 874 1 Gas Elec. night Gas Elec.

A.4 52 235 2 284 9 152 1 Heat pump Heat pump Elec. Elec.

A.5 12 135 6 371 7 967 1 Elec. day Elec. night Elec. Elec.

A.6 55 685 2 200 9 521 1 Heat pump Heat pump LPG Elec.

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RRESULTS OF MULTICRITERIA ANALYSIS (2):ESULTS OF MULTICRITERIA ANALYSIS (2):

Table 2. Results of analysis D

Outcomes of the solution

INV OMC Com Use

TotEm CO2

TotEm SO2

Imp Res

Input (a mix of energy carriers

and technologies)

PLN

PLN - kg kg GJ Heating Hot water Cooking Elect.

D.1 52 235 2 284 1 6 288 7,68 0 Heat pump Heat pump Elect. Elect.

D.2 20 385 2 947 0 1 702 0,04 29,4 Biomass LPG LPG Elect.

D.3 57 185 2 370 1 5 520 4,70 31,6 Heat pump gas gas Elect.

D.4 12 135 7 349 1 21 536 26,2 0 Elect. day Elect. day Elect. Elect.

D.5 12 135 6 371 1 21 535 26,2 0 Elect. day Elect. night Elect. Elect.

D.6 27 835 3 394 1 20 674 24,8 8,33 Elect. night Elect. night gas Elect.

D.7 19 635 3 555 0 5 399 6,19 8,33 biomasa Elect. day gas Elect.

D.8 24 935 3 448 1 21 536 26,2 0 Elect. night Elect. night Elect. Elect.

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Scenarios of energy demand:

1. Long winter, number of inhabitants increases

2. Normal winter, number of inhabitants increases

3. Normal winter, fixed number of inhabitants

4. Warm winter, fixed number of inhabitants

TTHE USE OF SCENARIO ANALYSIS:HE USE OF SCENARIO ANALYSIS:

Two parameters of the mathematical model could change in 15 years’ time:

CEi – cost of 1 GJ of i-th energy carrier

Scenarios of prices of energy carriers:

1. Base2. Pessimistic3. Optimistic 4. Gas crisis5. Oil crisis6. Cheap elect.

YeEnDe – total energy demand

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RRESULTS OF SCENARIO ANALYSIS:ESULTS OF SCENARIO ANALYSIS:

Fig. 2. Trajectory of values of outcome variable OMC for B1 user for decision: [gas, gas, gas, elect.]

0

2 000

4 000

6 000

8 000

10 000

12 000

14 000

16 000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

OMC [PLN]

Base

Pessimistic

Optimistic

Gas crisis

Oil crisis

Cheap elect.

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Fig. 3. Cost LCC for B1 user for decision: [gas, gas, gas, elect.]

0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

90 000

1 scenario energydemand

2 scenario energydemand

3 scenario energydemand

4 scenario energydemand

LCC [PLN]

Base

Pessimistic

Optimistic

Gas crisis

Oil crisis

Cheap elect.

RRESULTS OF SCENARIO ANALYSIS:ESULTS OF SCENARIO ANALYSIS:

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   Decision-making process is friendly for DM.

   Problem of selecting energy supplying option for municipal and

residential customers is a part of much broader subject: local energy

planning.

CCONCLUSIONS:ONCLUSIONS:

   Interactive multi-objective decision making methods could be effectively

used to solve decision-making problem of selecting energy supplying

option for municipal and residential customers.

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   Planning energy supply for municipal systems.

   Regional energy planning:

- heterogeneity (across temporal and spatial scales) energy users

- uncertainty (price of energy carriers and energy demands)

- apply new technologies

- apply locally available renewable energy resources

- model based decision support systems.

FFUTURE:UTURE:

   Multicriteria modelling and analyses of many heterogeneous energy

users

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MMULTI CRITERIA MODEL ANALYSIS:ULTI CRITERIA MODEL ANALYSIS:

Various trade-offs criteria

Analysis of various Pareto solutions

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VVARIOUS TYPES OF ANALYSES:ARIOUS TYPES OF ANALYSES:

economical and comfort

economical, ecological, and comfort

economical, ecological, and energy safety

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MMODELOWANIE MATEMATYCZNE ZASILANIA W ENERGIĘ ODELOWANIE MATEMATYCZNE ZASILANIA W ENERGIĘ BUDYNKU MIESZKALNEGO JEDNORODZINNEGO:BUDYNKU MIESZKALNEGO JEDNORODZINNEGO:

Model matematyczny y = f (x,z)

Użyt-kownik

P(x,y)

zy

gdzie: x Rn oznacza wektor zmiennych decyzyjnych, y Rm oznacza wektor rezultatów decyzji (zmiennych wyjściowych), z wektor parametrów (decyzji zewnętrznych), P(x,y) preferencje decydenta.

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OOGÓLNY SCHEMAT INTERAKTYWNEJ WIELOKRYTERIALNEJ GÓLNY SCHEMAT INTERAKTYWNEJ WIELOKRYTERIALNEJ METODY ANALIZY MODELU SYTUACJI DECYZYJNEJ :METODY ANALIZY MODELU SYTUACJI DECYZYJNEJ :

START

Wybór kryteriów oraz określenie ich typów

Znalezienie rozwiązania Pareto optymalnego problemu wielokryterialnego

Ocena otrzymanego rozwiązania Pareto optymalnego

STOP

Czy dalejanalizujemy model?

T N

Czy zmianazbioru kryteriów?

TNWybór rozwiązania satysfakcjonującego

Określenie preferencji decydenta (poziomy aspiracji i rezerwacji)