INTEGRATED ENERGY DISTRIBUTION SYSTEM PLANNING: A MULTI-CRITERIA APPROACH

Transcription

1 INTEGRATED ENERGY DISTRIBUTION SYSTEM PLANNING: A MULTI-CRITERIA APPROACH Audun Botterud 1,3, Mara Catrnu 1, Ove Wolfgang 2, Arne T. Holen 1 1 Dept. of Electrcal Power Engneerng, Norwegan Unversty of Scence and Technology, Trondhem, Norway, 2 SINTEF Energy Research, Trondhem, Norway, 3 Argonne Natonal Laboratory, Argonne IL, USA abotterud@anl.gov, mara.catrnu@elkraft.ntnu.no, ove.wolfgang@sntef.no, arne.holen@elkraft.ntnu.no Abstract Ths paper presents a decson support framework for expanson of local energy dstrbuton systems. We focus on a complex decson envronment, where the planners of the local electrcty dstrbuton system take nto consderaton the competton between dfferent energy carrers n coverng the total energy demand. At the same tme, a number of crtera must be taken nto account n the assessment of nvestment alternatves. By combnng a lnear optmsaton model for the operaton of the energy system wth a preference model based on mult-attrbute utlty theory, we develop an ntegrated plannng framework. In a plot case study we test the framework on a problem wth realstc data from a suburb n Norway. We ntervew fve persons wth background from energy research and ndustry. Ther preferences are used to rank the potental expanson alternatves. The results and experences from the case study are duly dscussed. Keywords: expanson plannng, ntegrated energy dstrbuton networks, mult-attrbute utlty theory, uncertanty. 1 INTRODUCTION Electrcty dstrbuton companes are operatng n an ncreasngly complex envronment. Wth the ongong ndustry restructurng the tradtonal vertcally ntegrated utlty companes are forced to unbundle ther actvtes. However, at the same tme there s often more horzontal ntegraton at the dstrbuton level. The dstrbuton companes are not only dstrbutng electrcty, but also supplyng, or competng wth, alternatve energy carrers, such as dstrct heatng and gas. Integrated analyss of the nteracton between multple energy carrers therefore represents an mportant challenge for the dstrbuton companes. We also see an ncreasng concern about the envronmental mpact of energy use, both at the local and global arena. A multtude of decson makers and stakeholders are usually nvolved n the plannng process, and very often they have conflctng opnons and objectves. The plannng process s further complcated by uncertantes about the future development of load, fuel prces etc. At the same tme, nvestment costs are hgh and expanson decsons rreversble. The complexty n the plannng of local energy systems s dscussed n more detal n [1]. In ths paper we nvestgate how decson analyss and mult-attrbute utlty theory can be used to provde decson ad n ths complex plannng envronment. We develop a plannng framework, whch can contrbute to structure the problem, quantfy the decson makers preferences, and assess potental nvestment alternatves. An mportant advantage of usng such an approach s that the decson process can be formalsed and documented. The paper s organsed as follows. Frst, we gve a presentaton of the ntegrated plannng framework. Then, we apply t on a plot case study, whch llustrates potental use of the methodology. The results from the study are dscussed along wth suggestons for future work, before concludng n the end. 2 AN INTEGRATED PLANNING FRAMEWORK 2.1 The mpact model In order to meet energy planners need for quanttatve smulaton a lnear optmsaton model has been developed durng the last 6 years, see e.g. [2]. A bref descrpton of the model s ncluded here. It mnmses the soco-economc costs of meetng dfferent types of energy demand n a defned area over a gven plannng horzon. The major advantages of the model are: Several energy carrers can be ncluded (electrcty, gas, dstrct heatng etc.) It ncludes energy sources, transmsson, converson, storage, demand as well as energy markets The components n the model have a physcal descrpton The geographcal locaton of demand and nfrastructure s taken nto account The model mnmses the cost of meetng the statonary energy demand wthn an area, takng all the exstng energy sources and transportaton networks nto consderaton. In addton, energy can be sold n defned markets at gven prces and quanttes. The model provdes a general set of system components, from whch the analyst can desgn an energy system wth the desred level of detal. An hourly profle can be specfed for each load type (e.g. electrcty and heat) at several defned load ponts. The tme resoluton and plannng horzon s typcally 1 and 24 hours respectvely n the operatonal analyss. Annual results can then be obtaned by aggregatng the results from several 24 hour perods wth dfferent de- 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 1

2 mand levels. In an nvestment analyss the operatonal results are calculated for all relevant desgns of the energy system, gven a set of possble nvestment components. An nvestment algorthm s already mplemented for cost-based expanson plannng, as explaned n [2]. In ths paper we use the model to calculate not only costs, but also other mpacts from the operatons of the energy systems. Hence, t serves as an mpact model, whose results are used as nput to the preference model, as outlned below. 2.2 The preference model Decson makng for energy planners s a very complex process, hghly exposed to uncertantes. In order to assst ths process, we need, besdes the mpact model that gves an approxmaton of the system s performances regardng dfferent crtera, a model that captures the preferences of the decson maker. Ths can be formally called the preference model. One way to buld t s to use the mult-attrbute utlty theory (MAUT). A decson maker has, practcally, a set of relevant objectves n mnd X 1, X 2.X m when analysng the avalable alternatves, A 1, A 2, A n for the energy system s plannng problem. Each of these alternatves can be charactersed by a set of achevement levels (attrbutes) of the objectves consdered. Moreover, uncertanty can be ncluded n the analyss by assgnng probablty dstrbutons to these achevement levels. The MAUT theory offers the possblty of quantfyng decson makers preferences regardng the set of objectves (X) when the values of the attrbutes (x) are uncertan. If an approprate utlty s assgned to each possble consequence and the expected utlty of each alternatve s calculated, then the best course of acton s the alternatve wth the hghest expected utlty. The theoretcal background regardng MAUT s thoroughly descrbed n several books [3] [4], and the theory has relevant applcatons n energy system problems [5] [6] [7] [8]. However, buldng utlty functons s not an easy task and n order to obtan a better approxmaton of the realty, the theory offers us several frameworks. We use the addtve form for the total utlty functon,.e. the total utlty equals the weghted sum of the sngle attrbute utltes: m u(x) = k u( x ) where u(x) u (x ) k = 1 total utlty for attrbute set x = x 1, x 2,..., x n utlty for sngle attrbute, = 1,2,..., m scalng constant, attrbute There are two man steps n determnng such a mult-attrbute utlty functon. Frst, ndvdual utlty functons, u (x ), must be determned, for each of the objectves consdered. Ths can be done by askng the decson-maker a set of lottery questons wth respect to dfferent achevement levels. The analyst can estmate, based on these answers, a set of qualtatve and quanttatve parameters that characterse the decson-maker s rsk atttude. These estmatons wll be used to approxmate the shape of the ndvdual utlty functon related to each of the objectves consdered. There are several functonal forms that can be adopted. In our preference model we chose the followng exponental functon, based on the descrpton n [9]: β { 1 } ( x x ) /( x x e ) β u ( x ) = 1/(1 e ) where u (x ) β x x utlty for sngle attrbute, = 1,2,..., m rsk parameter, attrbute upper lmt (worst outcome), attrbute lower lmt (best outcome), attrbute At ths pont a consstency check s necessary, to assure that the chosen form for the sngle utlty functons s representng the true preferences of the decson maker nvolved. Ths mples addtonal sessons of questons that the analyst must desgn. The second step s to determne the scalng constants, k, usng questonnares of the trade-off type. In both types of questonnares we use attrbute values calculated wthn the mpact model, pror to the preference elctaton process. After ths two-step process of quantfyng the decson-maker s preferences, the expected utlty for the dfferent nvestment alternatves can be calculated. Uncertantes are descrbed n terms of scenaros wth probabltes, and the expected utlty for an alternatve j can then be expressed as: ( ) n j j k j, k j, k k = 1 E u (x ) = p u (x ) where E(u j (x j )) total expected utlty, nvestment alternatve j u j,k (x j,k ) total utlty, alternatve j, scenaro k probablty for scenaro k p k The rankng of the alternatves can now be done based on the calculated expected utlty. 2.3 The ntegrated framework A flowchart of the proposed ntegrated expanson plannng framework s shown n Fgure 1. Frst, nput data for the analyss wll have to be specfed. It s mportant that the decson makers are nvolved already at ths stage, especally when t comes to decdng on whch attrbutes and uncertantes to consder. A number of techncal specfcatons, such as nvestment and operatng costs, capactes, and emsson and loss factors, also have to be determned for the components n the energy system. Most of the nput data are fed nto the operatons part of the analyss, where the mpact model s used to calculate operatonal attrbutes (e.g. operatng cost, local and global emssons). An algorthm s developed, whch does ths for all alternatves over all scenaros. The results from the operatonal analyss are collected n a mult-attrbute (MA) achevement matrx together wth attrbutes whch are ndependent of the operaton of the system (e.g. nvestment cost and vsual mpact). 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 2

3 the test were asked to magne themselves n the poston of the top manager of an energy company that s the man suppler of energy for the resdental and ndustral customers n the regon. The same problem was proposed to all of them,.e. to decde on an expanson plan for the exstng energy system n order to satsfy the future ncrease n local demand. Fgure 1: Flowchart of ntegrated plannng model. The MA achevement matrx has to be calculated before the elctaton of decson maker preferences can be carred out. Ths s because the rsk parameters and scalng constants are lnked to the upper and lower lmts of the attrbutes. These lmts are a drect result of the operatonal analyss (mpact model). The preference parameters are only vald for the calculated set of attrbute lmts. After ntervewng the decson makers, the derved preference parameters can be combned wth the MA achevement matrx to calculate total expected utltes for the nvestment alternatves, usng equatons -. Afterwards, t s straghtforward to rank the alternatves based on expected utlty. Note that the MA achevement matrx that s calculated n the frst part of Fgure 1 can also be used as nput for alternatve paradgms for decson makng under uncertanty, such as mnmax and mnmax regret. However, n ths paper we focus on the decson paradgm based on MAUT. 3 PILOT CASE STUDY In order to test and mprove the proposed decson support framework we developed a plot case study. We used realstc data from an exstng plannng problem n Norway to analyse the future energy supply nfrastructure for a suburb wth ca households and possble addtonal ndustral demand. Based on results from the mpact model we carred out preference elctaton ntervews wth fve persons wth background from energy research and ndustry. All persons partcpatng n 3.1 Assumptons for the operatonal analyss In order to smplfy the analyss we only consdered the operatons of the system for one tme stage (year) n the future. Hence, n ths analyss we dd not consder the long-term changes n demand, and the tmng of nvestment decsons. Total nvestment costs were converted to annualsed costs and could therefore be compared to the operatng costs. An nterest rate of 7 % was used for nvestment costs. Hourly data for electrcty and heat demand were specfed for 8 dfferent days n the year. The load days represented four seasons and two days wthn the week (weekday and weekend day). A 122 bus network was used for the electrcty grd, wth hourly electrcty load specfed n 55 of them. DC load flow equatons were used to calculate the load flow and correspondng losses n the mpact model. Potental dstrct heatng networks were represented wth ether 14 or 16 heat demand ponts, all of them wth hourly demand data for the 8 load days. Note that whle the electrcty load can only be met by electrcty, any connected energy carrer can meet the heat load. In ths case that s electrcty or dstrct heatng. The mpact model fnds the mnmum cost soluton for meetng both electrcty and heat load for each of the days consdered. The man uncertanty consdered n the analyss was the prce of electrcty. The electrcty prce s very mportant for the total cost of meetng the load, snce there can be substantal exchange of electrcty from the area, both mports and exports. Three scenaros were used for hourly electrcty prces (Fgure 2). For smplcty we used the same prce data for all the 8 load days. Prce [NOK/MWh] Hour hgh base low Fgure 2: Prce scenaros. Currency rate: 1 NOK 8. In addton to the prce uncertanty, we also assumed that the margnal change n global CO 2 emssons from exchange of electrcty was uncertan. Ths factor affects the total CO 2 emssons from dfferent nvestment alternatves. The margnal CO 2 factors for electrcty 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 3

4 exchange were set to 400, 500 and 600 g/kwh respectvely, for the low, medum and hgh prce scenaros, assumng that more effcent technologes are used n the low prce scenaro. Subjectve probabltes were assgned to the scenaros, usng 0.25 for the hgh and low scenaros and 0.5 for the medum prce scenaro. These probabltes were used when calculatng the expected utltes, as expressed n equaton. Other prces, such as the prce for gas supply to CHP plants and gas bolers, and the prce pad for heatng at the ndustral ste were assumed constant n the analyss. 3.2 Objectves The mpact model was set up to calculate four operatonal attrbutes: operatng cost, CO2 emssons, NOx emssons and heat dump from CHP plants to the envronment. In addton, nvestment cost s also an mportant attrbute, whch s not dependent on the system operaton. Other crtera could of course also be consdered n the analyss, ether by extendng the current mpact model or by usng addtonal models to estmate other mpacts from the nvestment decsons. However, n ths case study we lmt the scope to the fve attrbutes summarsed n Table 1. No. Attrbute Unt 1 Operatng cost [MNOK/year] 2 Investment cost [MNOK/year] 3 CO 2 emssons [tons/year] 4 NO x emssons [tons/year] 5 Heat dump [MWh/year] Table 1: Summary of attrbutes consdered n the plot case study. MNOK s mllon NOK. 3.3 Investment alternatves Four nvestment alternatves were analysed wth the mpact model pror to the ntervews wth the decson makers. The frst alternatve conssts of renforcng the electrcty grd wth a new supply lne to the area, so that one can contnue to rely on electrcty to supply the local statonary energy demand. Ths s the alternatve wth the lowest nvestment cost. A dstrct heatng network and a CHP plant s bult n the other three alternatves, to serve the heat demand for the customers n the resdental area. In addton, a gas boler s bult to meet the peak demand for dstrct heatng. In the second alternatve, the dstrct heatng network also covers an ndustral ste outsde the resdental area. The CHP plant s placed at the ndustral ste, and can also meet the heat demand there, whch s currently suppled from a desel boler. In alternatves 3 and 4 the CHP plant s placed nearby the resdental area. The only dfference between these alternatves s the sze of the CHP plant. The bgger CHP plant n alternatve 4 facltates generaton of more electrcty, whch can be sold to the electrcty market when t s proftable. A consequence of hgher electrcty generaton mght be excess heat from the CHP plant, whch must be dumped to the local surroundngs. Table 2 summarses the four alternatves. Alternatve New el lne DH network CHP plant Gas boler 1 yes no no no 2 no large 3.6 MW 5.0 MW 3 no small 3.6 MW 5.0 MW 4 no small 5.0 MW 5.0 MW Table 2: Descrpton of alternatves. The mpact model s results for the four alternatves over all three scenaros are shown n the MA matrx n the appendx (Table 6). We can see from the table that alternatve 1 has hgher operatng cost and CO2 emssons than the other three alternatves. On the other hand, the nvestment cost and the local emssons of NOx and heat are lower n scenaro 1. The dfferences between the last three scenaros are smaller, but stll sgnfcant, especally for NOx emssons and heat dump. There are also dfferences n the level of uncertanty for the attrbutes n the four alternatves, as can be seen when studyng the results from the three prce scenaros n Table 6. The decson makers could of course base ther decson on drect assessment of the nformaton n Table 6. However, even wth the smple example presented here t becomes dffcult to judge the trade-offs and rsks nvolved drectly from the table. The advantages of usng a formal approach based on decson analyss and MAUT are llustrated below. 3.4 Preference elctaton The preference model was used n order to formally ncorporate the man preferences of the decson makers nvolved n the analyss. As mentoned n secton 2.2, two types of questonnares were desgned. It s mportant to add here that the results followng ths type of dalogue are relevant only f the decson makers pay great attenton and f they are wllng to thnk hard about the problem beng analysed. Consequently, the decson makers had to thnk f the results presented to them were relevant for the analyss: f they would lke to consder more crtera or elmnate the ones wth lttle relevance. The frst type of questons was lottery questons for each of the objectves consdered: the decsonmaker was asked whether he would prefer an alternatve wth an uncertan outcome (A) or one wth a certan outcome (B). The value of the certan outcome n B was repeatedly modfed untl the decson-maker became ndfferent between these two optons (Fgure 3). The procedure was repeated for all 5 attrbutes n the analyss. A Indfference between A and B Uncertan outcome B Certan outcome 50 % 50 % 100 % x mn x max x ce Fgure 3: Example of lottery queston for sngle attrbute rsk preference elctaton. 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 4

5 The ranges of attrbute values dscussed n the lottery questons were obtaned usng the mpact model. The answers to the questons were collected by the analyst and used to estmate ndvdual utlty functons. An exponental form for the sngle utlty functon was used, as explaned n secton 2.2. Table 3 shows the decson makers sngle utlty rsk parameters for all attrbutes. A postve β mples a rsk averse atttude, whereas a negatve β expresses rsk proneness. It turns out that all decson makers are rsk averse when t comes to nvestment and operatng costs. In contrast, the decson maker s rsk atttude vares more wdely for the envronmental attrbutes 3-5. For nstance, when t comes to NO x -emssons respondents A, B and D are rsk prone, E s rsk neutral, whereas C s rsk averse (Fgure 4). A B C D E β β β NA 0.00 β β NA 2.48 NA NA Table 3: Sngle utlty rsk parameters (β ) for all attrbutes and respondents (A, B, C, D, E). NA means that the decson maker consders the objectve rrelevant. dfferent ndvdual utlty functons. However, from the preference parameters n Table 3 and Table 4 t appears as f the decson makers tend to be more rsk prone about crtera they care less about. In general, we had the mpresson that decson makers had problems expressng ther rsk preferences for attrbutes they were less concerned about. Attrbute max mn mn A Indfference between A and B B max Ref. Attrbute Fgure 5: Example of queston for trade-off preference elctaton. A B C D E k k k k k Table 4: Trade-off parameters (k ) for attrbutes 1-5. A, B, C, D, E are the fve respondents. Fgure 4: Indvdual utlty functons for attrbute 4,.e. NO x -emssons, for all respondents (A, B, C, D, and E). The second type of questons was the trade-off questons. The decson maker was frst asked whch of the crtera analysed was the most mportant. Ths crteron was used as reference attrbute for the trade-off comparsons. The decson maker was then asked to compare two hypothetcal alternatves A and B, measured along the reference attrbute and one of the other attrbutes, as llustrated n Fgure 5. The ndfference pont was found by changng the reference attrbute level of alternatve B, keepng the level of attrbute at ts best (mnmum), untl the respondent was ndfferent between the two alternatves. Ths was repeated for all crtera except from the reference one. The resultng trade-off parameters, k, are shown n Table 4. Note that these parameters can not be drectly compared for the fve decson makers, snce they have 3.5 Rankng of alternatves Havng derved the decson makers preference parameters we can now calculate total expected utltes based on equatons, and. We have only calculated expected utlty for the four alternatves. However, other alternatves could also be evaluated wth the same preference parameters, gven that ther attrbutes for all uncertanty scenaros are wthn the attrbute lmts n Table 6. The results for the fve respondents are shown n Table 5. Decson makers A, B, D and E end up wth the same rankng of the four alternatves. Alternatve 3, whch s ranked frst for these decson makers, s also the alternatve wth the least expected cost. Respondent C puts more weght on the local polluton (NO x and heat dump), and therefore ranks alternatve 1 frst. Alt. A B C D E Table 5: Expected utlty and rankng of the four alternatves for the fve respondents. 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 5

6 In Fgure 6 we show more detaled results for respondents C and E. The bars represent the total expected utltes for each of the four alternatves analysed. Snce we use an addtve utlty functon, the expected total utlty can be splt nto sub-components for each of the fve attrbutes. We clearly see that decson maker C s concern about the local polluton makes alternatve 1 the one wth the hghest expected utlty. We also see that respondent E s manly concerned wth the cost fgures, and do not consder heat dump at all. The graphs gve a good vsualsaton of how two decson makers n the same poston analysng a problem, can have dfferent preferences resultng n dfferent decsons. It mght also be that the resultng rankng of alternatves based on the total expected utltes s the same, even f the respondents preferences are dfferent. Ths s the case for respondents A, B, D, and E n our study. Expected utlty Expected utlty Respondent C Alternatve Respondent E Alternatve Heat dump NOx CO2 Inv. Cost Op. cost Heat dump NOx CO2 Inv. Cost Op. cost Fgure 6: Expected utlty for respondents C and E. In the end, the plot case study also demonstrates the mportance of ntegratng the plannng of the electrcty dstrbuton system wth the plannng of other energy dstrbuton networks. In ths example the preferences of four of the decson makers ndcate that a dstrct heatng network should be bult nstead of renforcng the electrcty grd. Separate plannng of the electrcty and dstrct heatng networks could easly result n suboptmal solutons. 4 DISCUSSION We beleve that the major advantages of usng multcrtera decson methods le n the structurng of nformaton and preferences. Through the formalsaton of the decson process, t also becomes easer to document the reasonng behnd decsons. Another mportant strength of the MAUT appled n our ntegrated plannng framework les n ts ablty to cope wth uncertanty and rsk preferences n a consstent manner. In our case study we looked at the plannng problem from the vewpont of the local energy dstrbuton company only. However, the decson adng methodology descrbed here can also be useful when dfferent nterest groups are nvolved n the decson makng process (end-users, regulators, NGOs etc.). It mght be easer to reach consensus and agree on a soluton when preferences are formalsed and vsualsed. Extensons of the framework could also be mplemented to further facltate group decson makng. In the case study we only made one ntervew wth each of the respondents. Important assumptons concernng nput data, uncertantes, and choce of crtera were made n advance by the analysts. In a real plannng process t s mportant that the decson makers are nvolved also n ths part of the analyss. Earler nvolvement of the decson maker wll also reduce the analyst s mpact on the results. Furthermore, more tme should be devoted to perform consstency checks n the preference elctaton process, n order to obtan more relable preference parameters. Each of our ntervews lasted approxmately 1 ½ hours, whch was not suffcent for thorough consstency analyss. A number of other extensons could also be done to the ntegrated plannng framework, such as: - Include addtonal mpact models, whch can calculate envronmental consequences n unts that are more relevant and easer to relate to for the decson makers. - Incorporate the decson makers preferences n the operatons of the system, by usng mult-objectve optmsaton n the operatonal analyss n the mpact model. - Introduce several tme perods, n order to analyse optmal tmng of nvestments. - Implement alternatve descrptons of uncertanty, and the possblty of applyng other decson paradgms than the expected utlty for decsons under uncertanty. 5 CONCLUSION New plannng tools are needed to address the ncreasng complexty nvolved n the plannng of local energy dstrbuton systems. In ths paper we have developed an ntegrated plannng framework where a detaled mpact model of the local energy system s combned wth a preference model bult on multattrbute utlty theory. In the plot case study we show that the methodology can be used to quantfy decson makers preferences, both n terms of rsk and trade-offs between conflctng plannng crtera. The derved preferences were used to evaluate and rank a set of nvestment alternatves. Dfferences n the fve respondents preferences were clearly reflected n the results. We beleve that the most mportant advantage of usng the proposed decson adng framework s that the decson process can be structured, formalsed and documented. Ths can clearly contrbute to better nformed decson makng. However, for successful mplementaton t s mportant that decson makers are suffcently nvolved and devoted, also n settng out the assumpton n the early stages of the analyss. 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 6

7 ACKNOWLEDGEMENTS The authors would lke to acknowledge W. A. Buehrng, R.G. Whtfeld and T.D. Veselka at Argonne Natonal Laboratory (Argonne, IL USA) for dscussons and advce on the use of MAUT for energy system plannng. We are also grateful to Arld Helseth at the Norwegan Unversty of Scence and Technology for provdng us wth realstc data for the plot case study. REFERENCES [1] Catrnu M., Løken E., Botterud A., Holen A.T., Constructng a multcrtera framework for local energy system plannng, 17 th MCDM Proceedngs, 10 pages, Whstler, Canada, Aug [2] Bakken B and Holen A.: "Energy Servce Systems: Integrated Plannng Case Studes", Proc. IEEE PES General Meetng 2004, Denver, CO, June [3] R.L. Keeney and H. Raffa, Decsons wth Multple Objectves: Preferences and Value Tradeoffs, Cambrdge Unversty Press, 1993, ISBN [4] V. Belton and T. J. Stewart, "Multple crtera decson analyss - An ntegrated approach", Kluwer Academc Publshers, 2002, ISBN X. [5] Pohekar S.D. and Ramachandran M., Applcaton of mult-crtera decson makng to sustanable energy plannng A revew, Renewable and Sustanable Energy Revews, Vol. 8, pp , [6] W.A. Buehrng, W.K. Foell and R.L. Keeney, Examnng Energy/Envronment Polcy Usng Decson Analyss, Energy Systems and Polcy, Vol.2, No. 3, [7] J. Pan and S. Rahman, "Multattrbute utlty analyss wth mprecse nformaton: an enhanced decson support technque for the evaluaton of electrc generaton expanson strateges," Electrc Power Systems Research, vol. 46, pp , [8] V. Schulz and H. Stehfest, "Regonal energy supply optmzaton wth multple objectves," European Journal of Operatonal Research, vol. 17, pp , [9] R.G. Whtfeld et al., IDEA Interactve Decson Analyss: User s Gude and Tutoral, Report ANL/EES-TM-378, Argonne Natonal Laboratory, Argonne, IL USA, APPENDIX Total Total Annual Annual Annual CO2 Annual NOx Annual Heat annual cost nv. cost operatng cost nv. cost emssons emssons dump Alt. Scen. Prob. [MNOK] [MNOK] [MNOK] [MNOK] [tons] [tons] [MWh] Table 6: Mult-attrbute achevement matrx n plot case study. 15th PSCC, Lege, August 2005 Sesson 2, Paper 4, Page 7