1 a multicriteria analysis of energy supply options for municipal and residential customers tadeusz...
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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
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