Prateek Bhargava_ 041

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    The Power Generation Portfolio

    Optimization Techniques(power generation through LPP

    forecasting)

    Prateek BhargavaPrateek Bhargava

    MBAMBA P.M.P.M.

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    INTRODUCTIONINTRODUCTION

    Electricity generation is responsible to a large extent for theElectricity generation is responsible to a large extent for theclimate change all over theclimate change all over the worldworld. To reach the sustainable. To reach the sustainablepower supply thepower supply the RenewableRenewable ratio should be raised and theratio should be raised and theCO2

    CO2

    emission technologies shou

    ldbe ro

    lle

    dback.

    Theemission techno

    logies shou

    ldbe ro

    lle

    dback.

    Theindirect cost of the climate changesindirect cost of the climate changes appearingappearing later, thelater, the

    harmful effects and the irreversible environment changeharmful effects and the irreversible environment changeare expressedare expressed byby the externality cost.the externality cost.

    ThisThis paper shows a linear programming optimizationpaper shows a linear programming optimization

    technique howtechnique how oneone can define the optimal portfolio havingcan define the optimal portfolio havingclear data and targets. Finally we discuss theclear data and targets. Finally we discuss the difficultiesdifficulties ofofthe decision making regarding the power plantthe decision making regarding the power plantconstructions.constructions.

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    Basic power generation

    technologies

    Golden Arch 2008 Golden Arch 2008 33

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    GHG and the energy industry

    The green house gases (GHG) create a special heat trapThe green house gases (GHG) create a special heat traparound the Earth globe and thisaround the Earth globe and this phenomenonphenomenon is consideredis consideredone of the causes of the global climate change, the raise ofone of the causes of the global climate change, the raise of

    thethe yearlyyear

    ly average temperature.

    There are many GHGs, theaverage temperature.

    There are many GHGs, thetwo most important are the CO2 andtwo most important are the CO2 and methanemethane..

    The directCO2The directCO2 emissionemission origins from the combustionorigins from the combustiontechnology of the fossil fueltechnology of the fossil fuel the indirectthe indirectCO2 productionCO2 productionmust be taking into account what is producedduring themust be taking into account what is producedduring the

    whole lifewhole life cyclecycle of the power plant construction andof the power plant construction anddecommissioning and also during mining anddecommissioning and also during mining and

    processing the fuel.processing the fuel.

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    Golden Arch 2008 Golden Arch 2008 55

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    Optimization

    Having known that there are some cloudy targets, thatHaving known that there are some cloudy targets, thatshould be reached by constraints andshould be reached by constraints and wewe are going to findare going to findthe best solution we choose the optimizationthe best solution we choose the optimization techniques.techniques.

    HereHere we seek for Single Objective Optimum (SOOwe seek for Single Objective Optimum (SOO).).

    Our objective is to minimization of following three factors :Our objective is to minimization of following three factors :

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    Golden Arch 2008 Golden Arch 2008 77

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    We used the following fixeddata in our calculations (seeTab

    le

    2).

    - existing power generation capacities

    - enlarging possibilities

    - yearly energy demand (today 40TWh and50TWh in2020

    ) - load factor

    - maximum energy production

    - minimal energy production

    - external cost/TWh - CO2 emission/TWh

    - investment cost

    - lifetime88

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    Golden Arch 2008 Golden Arch 2008 99

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    Golden Arch 2008 Golden Arch 2008 1010

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    The different technologies have different investment needsThe different technologies have different investment needs(power plant construction),(power plant construction), externalityexternality cost andCO2cost andCO2

    emissionemission.The common p

    latform is the cost of a power p

    lant.

    The common p

    latform is the cost of a power p

    lantgenerating 1generating 1TWhTWh electricity. The objective function is aelectricity. The objective function is a

    mathematical expression that shows the value of the featuremathematical expression that shows the value of the featuretoto minimizeminimize. We have the followings:. We have the followings:

    FigFig..O

    bjectiveO

    bjective functions for minimizationfunctions for minimization1111

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    The second step is to set up the different constraints. TheThe second step is to set up the different constraints. The

    constraints are additional inequalities in the equation systemconstraints are additional inequalities in the equation systemthat must be solved (seethat must be solved (see Fig.1).Fig.1).

    FirstFirst we defined an objective function to minimize. This iswe defined an objective function to minimize. This is

    the total external costthe total external cost comingcoming from the individual externalityfrom the individual externalityof each generation type. The linear programmingof each generation type. The linear programming tooltool foundfoundthe solution that meets all the constraints and equationthe solution that meets all the constraints and equationfurthermore it producesfurthermore it produces thethe minimal externality cost (see Figminimal externality cost (see Fig2).2).

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    ExternalityExternality minimizationminimization-- TheThe LP Problem Constraints are:LP Problem Constraints are:

    1.1. MaximumMaximum quantities in yearlyquantities in yearly TWhTWh::

    ## 1 ) 1 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (1 ) 1 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (nucnuc) + 0) + 0(coal) + 0 (gas)

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    2.2. Minimum quantities in yearlyMinimum quantities in yearly TWhTWh::

    # 11 ) 1 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (# 11 ) 1 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (nucnuc) + 0) + 0(coal) + 0 (gas) >= 0,22(coal) + 0 (gas) >= 0,22# 12 ) 0 (hydro) + 1 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (# 12 ) 0 (hydro) + 1 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (nucnuc) + 0) + 0(coal) + 0 (gas) >= 0,5(coal) + 0 (gas) >= 0,5# 13 ) 0 (hydro) + 0 (wind) + 1 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (# 13 ) 0 (hydro) + 0 (wind) + 1 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (nucnuc) + 0) + 0(coal) + 0 (gas) >= 1,6(coal) + 0 (gas) >= 1,6

    #14 )

    0(hy

    dro) +

    0(win

    d) +

    0(biomass) +

    0(PV) +

    0(geo) +

    0(biogas) +

    1(#

    14 )

    0(hy

    dro) +

    0(win

    d) +

    0(biomass) +

    0(PV) +

    0(geo) +

    0(biogas) +

    1(nucnuc) +

    0) +

    0(coal) + 0 (gas) >= 16(coal) + 0 (gas) >= 16

    # 15 ) 0 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (# 15 ) 0 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (nucnuc) + 1) + 1(coal) + 0 (gas) >= 5(coal) + 0 (gas) >= 5# 16 ) 0 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (# 16 ) 0 (hydro) + 0 (wind) + 0 (biomass) + 0 (PV) + 0 (geo) + 0 (biogas) + 0 (nucnuc) + 1) + 1(coal) + 0 (gas) >= 10(coal) + 0 (gas) >= 10

    Fig 1. Constraints for the optimization (constraints table)Fig 1. Constraints for the optimization (constraints table)

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    Fig. 2- The optimized externality result (SOO solution)

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    On solving the above constraints we find the followingexternality minimization solution:

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    Table: Yearly production rate and cost in case of externalityoptimization

    TheThe belowbelow shows the result portfolio of the externalityshows the result portfolio of the externalityminimization.minimization.

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    Table 6: The power generation portfolio developmentalternatives by different objective functions:

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    CO2

    emission

    minimization

    CO2

    emission

    minimization

    EXT

    cost

    minimization

    EXT

    cost

    minimization

    INV.

    cost

    minimization

    INV

    cost

    minimization

    INV

    cost

    minimization

    Total energy 50 50 50 50 40 50 50 TWh

    hydro 2,2 2,2 2,2 2,2 0,22 0,22 2,2 TWh

    wind 2,6 2,6 2,6 2,6 0,5 0,5 0,5 TWh

    biomass 2,2 0 0 0 1,6 1,6 2,2 TWh

    PV 0,3 0 0,3 0,3 0 0 0 TWh

    geo 0,4 0,4 0,4 0,4 0 0 0,4 TWh

    biogas 1,3 1,3 1,3 1 0 0 1,2 TWh

    nuc 29,1 29,1 29,1 29,1 16 16 16 TWh

    coal 0 0 0 0 5 7 7 TWh

    gas 11,9 14,4 14,1 14,4 16,68 24,68 20,5 TWh

    REN 18 % 13 % 13,6 % 13 % 5,8 % 4,64 % 13 % %

    CO2 7,44 7,77 7,65 7,8 13,23, 18,83 17,05 Mt

    External cost 417,35 401,5 395,35 401,35 733,94 1023,94 952,4 MEUR

    Yearly power plant building cost

    182,52 165,94 174,56 173,17 95,62 112,96 125,70 MEUR

    Yearly power plant operation

    costs1304 1403 1383 1401 1540 2128 1882 MEUR

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    From the above table we can say:From the above table we can say:

    Instead of the politicians the numerical solution provides agood sustainable solution.

    The future portfolio is not really sensitive to the amount of

    energy produced. It is forbidden to build up an energetic monoculture.

    In the currentlow cost solution the future operation costsare enormous.

    The power plant building and operation and externalitycosts are in the same range.

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    Hence we can concludeHence we can conclude--

    TheThe total generatedtotal generated energyenergy is 50is 50TWhTWh.. TheThe renewable ratio is 13%.renewable ratio is 13%.

    TheThe total external cost is 401 MEUR.total external cost is 401 MEUR.

    CoalCoal andand biomassbiomass firing is stopped.firing is stopped.

    TheThe CO2 emission is 7,8 Mt per year. It is almost the halfCO2 emission is 7,8 Mt per year. It is almost the halfof theof the presentpresent emission.emission.

    SettingSetting up different objective functions we getdifferentup different objective functions we getdifferentoptimal power mixes.optimal power mixes.

    AllAll areare optimaloptimal by a Single Objective (see Table 6by a Single Objective (see Table 6.). The.). Thedemonstration above showsdemonstration above shows ThisThis methodology ismethodology is

    appropriate for the qualitative strategyappropriate for the qualitative strategy definition.definition.

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    Fig. 17. Visualization of generation ratios in differentoptimization alternatives

    2020

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    The optimisation provides the mathematically optimalsolution regarding a preference. We always start form the

    existing situation and take into account the developmentpossibility. Fig. 17. shows e.g. that

    - We should not run any coal fired unit if we want toreally decrease the CO2. There is enough non CO2 emittivecapacities (externality andCO2 minimization)

    - On the other side if we want to minimize the investmentcost into the power plant (mix) than we must run this typeof plants with high load factor, and we can build more gasand coal plant (minimal investment today maximal

    external cost in the future). - The nuclear, wind and hydro capacity plays important

    role in the externality minimum scenarios.

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    Fig. 18. Yearly costs of the alternatives (MEUR)

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    Fig. 18. shows that the yearly total cost of the fossil basedscenario costs more than the renewable generation solution.

    It comes from the high fuel cost (gas) and externality costs.

    From the optimization we can present other results, too.Figure 19. shows the cost efficiency of the power plant

    development:If we had1 MEUR it is enough to

    Construct this amount of built in power capacity thecheapest is the gas and coal fired plant (green column). If

    we take into account onl

    y the investment onl

    y into theconstruction, we must build more and more gasbased plant. But if we count the lifecycle operation(fuel) and externality costs, this is the worst solution!

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    Fig. 19. How much energy can be generated from 1 MEUR?2424

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    Construct and generate this amount of energy (operationcost ~ fuel cost) (red column) We can generate moreenergy from hydro, geothermal biogas and nuclearsources. The gas is the most expensive! In the long term

    planning hy

    dro, geotherma

    lbiogas an

    dnuc

    lear (!)solutions are fitting to the relevant criteria.

    If we pay the externality we get the previous result (bluecolumn).

    These calculations demonstrate that the rough SOO producesreally distinct scenarios so it is not recommendable to useonly the cheapestOR smallest emission version.

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    As conclusion we can state that

    - The CO2 minimum and external costminimization produces recommendable scenarios

    - The simple cost minimization implies huge CO2emission

    - The gas (and oil) fired plants are cheap to build butexpensive to run for the fuel cost andCO2 production

    - The wind generation is welcome

    - All portfolios contain hydro generation

    - The bulk central biomass firing is not the part ofoptimal mix

    - All the alternatives contain nuclear generation (donot forget that any other pumped storage plant raises

    the external costs)2626

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    More detailed optimization with exact inputdataprovides real results. We recommend this method forthe national planning level too.

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    Drivers and barriers

    One should ask, if the CO2 emission has a significant role inthe global warming, why we do not switch to low emissiontechnologies?The power plant capacity development isdemanddriven, the present philosophy is: Generating as much

    energy as needed. The choice of the power plant type isinfluenced by not only the price, but the area usage, unitmeasure, the specific investment cost, the sensibility for thefuel price, the political stability of the fuel producing region,the water usage, the life time, etc. Among the dozensof aspects nowadays the emission is going to get moreimportance.

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    The decision about a new power plant construction (yes/noor how large, etc.) is often made in the STEPLE frame:

    Social (Employment; Right to energy access; Socialmission; value of human positions - bureaucracy; etc.)

    Technological (Security of supply; Quality of energy;Efficiency of production and usage; Integration)

    Economic (Costs; End price of the energy; Growth rate ofthe economy; Profitability, ROI; Accumulation of the

    investment/development; Lifetime of the assets; etc.) Political (Role of the state decision/subventions;

    Priority of energy supply; National interests; etc.)

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    Legislation (Number of the rules; Controlled competition;Entry barriers; Corruption factor; Cooperative workbetween the players; etc.)

    Environmental (Greenhouse effect EMISSION;

    Used/wasted materials; Area destruction; Ecologicaldestruction; Energy resources; etc.)

    The emission has weak positions nowadays.

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    Conclusion

    In the fight for the sustainable industrialized age the energyindustry has no good position. It is responsible for one thirdof the CO2 emission and we cannot expect any change inthe short future (growth of energy need, small unit measure

    of the renewable generation forms, relatively high directcosts). We have shown that the technologies and itsemissions are well known so by a clear mathematicaloptimization we could plan the optimal power mix. Also thereare many CO2decreasing technologies, as the raising theenergy usage and generation efficiency, trapping theexhaustedCO2 or changing the generation portfolio to nonfossil fuels.

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    All the energy generation technologies have disadvantagesbut the decision space is wider than the simple short termprofit maximization. The politicians should maximize thelong term advantages (or atleast to minimize the harmfuleffects) taking into account the long term external cost.

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    References

    Lee, W., Scott W., (2000). Distributed power generation: planningand evaluation - MarcelDekker, ISBN: 978-0-8247-0336-3

    Lee, K., El Sharkawi, M. (2008). Modern heuristic optimizationtechniques, Wiley & Sons, ISBN: 978-0-471-45711-4

    Patel, M. (2006). Wind and Solar Power Systems: Design Analysis, CRCpress, ISBN: 978-0-8493-1570-1

    Kdr, P. (2009 - I). Storage optimization in a liberalized energy market.Proceedings of7th International Symposium on Applied MachineIntelligence andInformatics, ISBN978-1-4244-3801-3Herany, Slovakia,January 30-31, 2009

    Kdr, P. (2009 - II). Multi Objective Optimization of Smart GridStructure, Proceedings of15th InternationalConference on IntelligentSystems Application to Power Systems, ISBN978-1-4244-5097-8,Curitiba, Brasil, November 8-12, 2009

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    THANKYOU FOR YOURTHANKYOU FOR YOUR

    KIND ATTENTIONKIND ATTENTIONPLEASEPLEASE

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