ABSTRACT. 1. The Residential Heating Energy Sector in Greece

Transcription

1 MONTE CARLO FORECASTING OF RESIDENTIAL NATURAL GAS AND DISTRICT HEATING DEMAND AND TARIFFS, AND COMPARISONS BASED ON THE CURRENTLY USED HEATING DIESEL, IN A CITY IN NORTH GREECE. by Chrstos Em. Papadopoulos, Assst. Professor of Natural Gas Techology Metrologcal Aalyss & Maagemet Dept. of Ol & N. Gas Egeerg Faculty of Egeerg Kavala Isttute of Techology GREECE Phoe: /Fax: /E-mal: cpapad@oteet.gr ABSTRACT Eergy demad forecastg the resdetal sector s extremely dffcult to perform relably, eve for a developed coutry. The ma reasos are the complexty ad the assumptos ad ucertates volved, ad the predcto of ecoomc growth, whch s the ma drver for eergy growth ad presets smlar forecastg dffcultes. A very terestg case study s curretly uder developmet the cty of Serres, North Greece regardg ts eergy supply. A ew dstrct heatg dstrbuto system s curretly uder costructo by a prvately owed compay. Ivestg a ew Combed Heat ad Power plat (CHP) at the suburbs of the cty, they wll supply the hot water byproduct of ther Power Plat to the cty s resdeces for heatg ad coolg use. I parallel, t s startg the costructo of a compettve atural gas dstrbuto etwor for the supply of the same cty resdeces. Offcal local clmate ad cesus data as well as heatg desel cosumpto ad respectve prce data were collected for the perod of the last fve years. The cty maly cossts of blocs of flats, wth a few commercal cosumers o the groud floor, ad famly houses, all havg desel cetral heatg systems (blocs ad houses). Dstrct heatg demad forecastg mplemetato s based o the avalable heatg desel cosumpto profle, by tag also to accout the respectve effceces, sce the dstrct heatg etwor s coected as a by-pass to the bloc s cetral heater/boler system, wth the latter remag place as bac up. Natural gas demad forecastg presets more dffcultes sce ts use s ot lmted for cetral heatg oly but exteds to others uses also (coog, etc.). For ths reaso, ot oly the heatg desel cosumpto profle was used but also cesus ad respectve emprcal data avalable from the use of Natural gas other ctes as well as electrcty cosumpto ad tarff data. Tarffs estmato ad forecastg s based o the supply cotract avalable for dstrct heatg ad o prces of atural gas earby ctes respectvely. Mote Carlo smulatos for forecastg ad ucertates estmatos were employed all cases. Not oly heatg desel cosumpto but also tarff profles of the varous blocs of flats revealed a excessve varablty, for varous reasos. Although dstrct heatg cetral systems seem to offer tally some savgs to the cosumers, atural gas demad ad cosumpto, depedg also o uses other tha cetral heatg, may offer addtoal savg beefts to cosumers the log ru.. The Resdetal Heatg Eergy Sector Greece Greece, as a member of the Europea Uo, s worg to tercoect ts gas ad electrcty dstrbuto etwors ad to partcpate the tegrato of EU eergy etwors ad marets, wth the ultmate am of esurg that all eergy users get relable supples o the best terms. By taclg admstratve barrers to a commo maret, the Uo ams to ope up competto, thereby fosterg ovato, greater effcecy ad a more effectve use of lmted resources. Moreover, oce the remag admstratve barrers are goe, the ablty to mprove frastructure, tegrate effectvely the atoal etwors ad vest better ad safer eergy supples for the future wll aturally be possble. The opeg up of the marets to all o-domestc cosumers from July 004 ad to all cosumers July 007 requred ad led to a seres of measures (codes, procedures ad methods) to be put place

2 order to eable ew operators, the drvers of competto, to eter the maret ad serve the very may ew elgble customers. The case study of Serres descrbed here s of partcular terest sce o the oe had a totally ew dstrct heatg dstrbuto system s uder costructo by a prvately owed compay ad o the other had, ad parallel, t s startg (wth some tme lag) the costructo of a compettve atural gas dstrbuto etwor for the supply of the same cty resdeces. Heatg Desel (Gas Ol) covers the ma household heatg eeds Greece. Oly durg the last decade atural gas has bee troduced to the Gree eergy mx, partally substtutg heatg ol cosumpto the bg ctes (Athes, Thessalo, Larssa ad Volos) stuated ear the ma atural gas trasmsso ppele. Ed-user eergy demad for heatg maly the resdetal ad commercal sectors ad to some extet power geerato (where there s sgfcat heatg or coolg load) s strogly correlated to the weather. The Dstrct Heatg (DH) compay vestg a ew CHP plat at the suburbs of the cty, t wll supply the hot water byproduct of ts Power Plat to the cty s resdeces for heatg ad possbly coolg use. Fgure Greece Natural Gas Ma Trasmsso ppele Serres, s the closest cty to the orth borders wth Bulgara (fgure), from where the ma tercoector eters Greece supplyg Russa atural gas to the coutry. I addto, a ew ppele system wll be created soo wth the completo of the ew Turey-Greece-Italy tercoector (fgure ). Fgure Itercoector Turey-Greece-Italy (ITGI) Greece s total fuels eergy cosumpto data are show fgures 3a & b. I fgure 3a the total household eergy cosumpto has bee plotted agast the type of fuel used, order to show the clear evdece of a exclusvely strog depedece of the fal total ol cosumpto Greece o the

3 households eergy cosumpto. Ths evdece s show eve better fgures 4a & b where t has bee calculated ad draw st ad d order sestvtes of total ol cosumpto to the ol cosumpto per sector ad a lear correlato coeffcet, R 0,7 has bee calculated for household sector both cases whch s much hgher tha all the other sectors. Ths strog relato ca smply be explaed by the wde ad almost exclusve use utl recetly of gas ol for heatg the resdetal sector Greece, wth all the mplcatos that ths may have. Greece Fal Eergy Cosumpto by type of Fuel Greece Fal Eergy Cosumpto by Sector Mtoe 5,00 4,00 3,00,00,00 0,00 9,00 8,00 7,00 6,00 5,00 4,00 3,00,00,00 0, Year Mtoe 9,00 8,00 7,00 6,00 5,00 4,00 3,00,00,00 0, Year Sold fuels Ol Gas Electrcty Reewables Households Idustry Trasport Households Commerce, etc. (a) (b) Fgure 3. Greece fal eergy cosumpto by type of fuel (a) ad by sector (b) (Source ENERGY & TRANSPORT006 IN FIGURES) Sector st Order Sestvty of Total Ol Cosumpto Greece 0,80 0,60 θ(fc)/year 0,40 0,0 0,00-0,0-0,40 Households R 0,70 Commercal R 0,443 Trasport R 0,908 Idustral R 0,0-0,60 -,00-0,50 0,00 0,50,00,50 θ(toc)/year Idustry Trasport Commerce, etc. Households Lear ( Idustry) Lear ( Trasport) Lear ( Commerce, etc.) Lear ( Households) Fgure 4a. Total Ol cosumpto depedece o households heatg ol cosumpto Greece, 3

4 Sector d Order Sestvty of Total Ol cosumpto Greece 0,80 0,60 θ(θ(fc)/year) 0,40 0,0 0,00 Households R 0,787 Commercal R 0,565 Trasport R 0,354 Idustral R 0,0476 -,50 -,00-0,50 0,00 0,50,00,50-0,0-0,40-0,60-0,80 θ(θ(toc)/year) Idustry Trasport Households Commerce, etc. Lear ( Idustry) Lear ( Trasport) Lear ( Households) Lear ( Commerce, etc.) Fgure 4b. Total Ol cosumpto depedece o households heatg ol cosumpto Greece,. Serres case study, Most domestc heat ed users Greece are essetally captve the short term sce they have o mmedate alteratve to usg heatg ol, so that overall demad may be prce elastc, depedet of the prce of heatg ol the short term. Ay demad respose by these customers to a prce chage usually lags by several years!! I most coutres, almost all resdetal customers ad the majorty of other sectors customers do ot mata dual-frg equpmet to eable rapd swtchg away from or to some specfc type of fuel. Some large customers, however, may be able to swtch fuels at very short otce thas to dual-frg or, the case of power geerators, by swtchg to alteratve fuel fred plat. The ew dstrct heatg etwor Serres, s coected as a by-pass to the bloc s cetral heatg/boler system, wth the latter remag place as bac up. The terest of ths case study arses from the capablty gve to the ed users (gve also that the whole scheme wll receve the ecessary support, maly poltcal, to the ed) to swtch at short otce betwee dfferet heatg sources/fuels (hot water, heatg gas ol ad atural gas) uder a full competto evromet, somethg uprecedeted for the Gree eergy maret. Oe addtoal advatage of the above scheme s that t allows extra flexblty to eergy supplers. Captve customers requre uterrupted supply at all tmes. Demad seasoalty mposes addtoal supply costs. No-captve customers wth the ablty to swtch fuels may be suppled uder terruptble cotracts, allowg supples to be dverted to captve customers at tmes of pea demad. It s ow well ow that the troducto of competto atural gas marets North Amerca ad Brta has led to chages the structure of gas prces ad reductos, o average, real pre-tax prces parallel wth rsg quattes delvered. Cosumer choce, cludg the rage of servces o offer, has expaded. These treds suggest that gas s beg produced, trasported ad delvered more effcetly ad that these effcecy mprovemets have a drect mpact to the come of the ed users [Note of DG Eergy & Trasport, (004)]. There s a wealth of lterature cocerg the dsadvatages of moopoly ad the advatages of competto maxmsg ecoomc effcecy. Uder moopoly, there s o automatc substatal cetve for the partcpats to mmse costs, maxmse effcecy ad productvty, ad reduce prces to cosumers. Competto forces them to do these thgs order to survve. The establshmet of a maret structure wth competg supplers ad cosumers who have the rght to exercse choce spurs supplers systematcally to see out productvty gas ad comparatve 4

5 advatages. Ths s a self-reforcg process that ultmately leads to developmet. As eergy marets become more compettve ad more complex, ew forms of competto emerge ad dustry structures, rss, ad opportutes evolve accordgly. As ew maret etrats appear, they dsturb the rules of the game ad geerate ew compettve pressures ad commercal tatves. The drve for ecoomc effcecy leads evtably to a radcal reorgasato of maret ad dustry structure. The way govermets see to meet ther socal, evrometal ad securty of supply objectves also must chage respose to these pressures [The Eergy Commuty, (006)]. Today, lots of dscussos are uderway about how all these ca be effectvely combed for a better developmet. Perhaps some d of a Sustaable Competto, whch ca tae to accout all these apparetly cotradctory matters, as wth the evromet ad eergy demad ad use, should offer some solutos. Fuel-swtchg behavor s drve by competto ad proft. Compaes ad utltes ad eve domestc cosumers gve the ablty to swtch from oe fuel to aother, eve for oly a few days, could beeft from a sudde shft relatve fuel prces, although the cotractual ad techcal dffcultes of dog ths vary. I Serres, wth the scheme uder developmet, the margal ed-use resdetal cosumer wll easly have the opto of swtchg fuel or usg a dfferet combusto ut (cetral gas ol (desel) ut, cetral DH ad cetral/or home atural gas ut) at short otce. Serres Resdetal Heatg Demad - Data & Method Descrpto, The cty of Serres cossts maly of blocs of flats, wth a few commercal cosumers o the groud floor, ad famly houses, all havg cetral desel heatg systems (blocs ad houses). The curret lac of cosstet log term regoal data for domestc heatg cosumpto Greece maes the developmet of dvdual eergy demad models for ay rego almost mpractcal. For ths reaso, t s qute commo to obta regoal forecasts based o a allocato of the atoal forecast. Natoal forecast data are usually retaed by publc authortes (Gree Mstry of Developmet, Mstry of Evromet etc.) ad are avalable to the publc the Natoal, Europea or Iteratoal databas. Due to above reasos, ad the fact that the oly data avalable regardg heatg eergy cosumpto for Serres are those comg from heatg ol loads suppled to the buldgs, a ot so ordary approach was developed ths study. Offcal local clmate ad cesus data as well as heatg desel cosumpto ad respectve prce data were collected for the perod of the last fve years. These data have bee classfed ad are preseted ext alog wth the forecastg methodology developed. Cesus Data A, Populato The last offcal populato cesus data 00 (NSSG -00) reported Serres wder urba area of 97 m a populato of 56,400 people, gvg a populato desty of 58 people/ m. Serres rego (couty s) reported populato for the same perod was 94,5 people. Today t s estmated that aroud 58,000 thousad people lve Serres mucpalty area (assumg a steady crease o average of aroud 0.35%/year of Greece populato observed durg the last 6 years [Eergy & Trasport fgures-geeral Data (006)]. Ths populato crease s mostly the tows ad to the local uversty that welcomes a cosderable umber of studets durg the last 0 years. It s cotrast though wth the Serres rural populato whch presets dmshg yearly rates of crease due to exactly the same reaso. B. Buldgs & Households The results from the cesus (NSSG-000) of Costructos-Number of buldgs o December, 000, ad that are relevat to ths study are preseted fgure 6. The data ecessary for our demad forecastg modelg were those regardg households ad umbers of occupats. O March 00 there were 8,566 households the ma cty, ad about 80% of them (4,786) had cetral heatg stallatos. Sce the ad utl December 006 (NSSG-Yearly Reports) there has bee a crease of about 6,700 ew households (3,50 wth up to rooms ad 3,80 wth 3 rooms ad more) the wder cty area, rasg the total umber of households to aroud 5,000 today, may of them though uoccuped. The estmated average umber of occupats per household s estmated at about 5

6 ,8. Varous factors fluece eergy cosumpto buldgs. Amog them are age dstrbuto of the exstg buldgs, weather codtos, umber ad sze of buldgs, type, age ad effcecy of heatg stallatos, fuel splt for heatg ad hot (servce) water supply etc. Serres Buldgs age dstrbuto s show fgure 7. The buldgs costructed before 980 correspod to 67.7% of the total umber of buldgs. Noe of these buldgs are thermally sulated ad exhbt a poor eergy performace, whle the vast majorty of them they are equpped wth old heatg stallatos. Balaras C.A. ad co-worers (000, 005 & 007) have doe sgfcat wor regardg Gree ad Europea buldgs eergy cosumpto. Greece s resdetal buldgs have bee classfed three categores accordg to the year of costructo. The frst category cludes the buldgs costructed before 980, whch have o thermal sulato sce they were costructed before the mplemetato of the atoal thermal sulato regulato. The secod category cludes the buldgs costructed durg the perod , whch are cosdered to be partally or fully sulated. Serres' households Number - Rooms & Members per houshold member members 3 members 4 members 5 members 6 members 7 members 8 members 9 members 0 & more Totals Room Rooms 3 Rooms 4 Rooms 5 Rooms 6+ Rooms 6+ Rooms 5 Rooms 4 Rooms 3 Rooms Rooms Room Totals Fgure 6. Serres Households Number Rooms & Members per household [NSSG (000)], Serres' Buldgs Age Dstrbuto (%) 0,0 8,0 6,0 4,0,0 0,0 % Buldgs 8,0 6,0 4,0,0 0, Fgure 7. Serres Buldgs Age Dstrbuto [NSSG (000)], 6

7 Despte the troducto of the Hellec Buldg Thermal Isulato Regulato (HBTIR, OHJ/36/4-7-79) sce 980, Balaras (007) has reported that the tegrato of thermal sulato Greece was problematc durg the frst decade of ts mplemetato addg that oly recetly ew buldgs have thermal sulato o the load bearg structure to elmate thermal brdges ad that double glazg has become a commo practce all ew buldgs as well as the most frequet refurbshmet actvty exstg buldgs. Fally, the thrd category cludes the buldgs that wll be costructed up to the year 00. From ther audt campag 93 Europea resdetal buldgs [9], ther reported results o actual total heatg eergy cosumpto (per ut of heated floor area) show a wde varablty ragg betwee 30.6 Wh/m ( Greece, a souther coutry) to KWh/m ( Polad, a orther coutry), wth a average of 74.3 Wh/m. The average actual heatg eergy cosumpto reported was 44, KWh/m Demar, 08.4 KWh/m Greece, 6. KWh/m Polad ad 7,0 KWh/m Swtzerlad. A extesvely wde varablty observed ad our heatg ol cosumpto data terms of the cosumpto per household ad per m of heated area that s maly due to the reasos explaed by Balaras (000 & 007) ad cosderg also the buldg age dstrbuto profle for Serres fgure 7. Clmatc Data Serres mothly mea temperatures as reported by the Natoal Meteorologcal Servce (NMS) of Greece are show fgure 8. Serres' Mothly Mea Temperatures O C 35,00 30,00 5,00 0,00 5,00 Mothly Mea Temperature Mothly M Temperature Mothly Max Temperature 0,00 5,00 0,00-5,00 OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP Fgure 8. Serres Mothly Mea Temperatures (NSSG -00), I Greece, the durato of the heatg systems operato ad the total amout of the Heatg Degree Days (HDDs) vares from oe locato to the other, as the ar temperature (apart from the prevalg atmospherc crculato codtos) depeds strogly o geographcal factors, such as lattude ad logtude, alttude, dstace from the sea, etc. [Matzaras (004)]. Durg the coldest wter moths (December to February) the absolute mmum temperature ca reach - 0 o C, usually Jauary, accompaed by the respectve pea load of heatg demad. Apart from techcal parameters ad clmatc codtos, occupat behavor plays a determat role o the actual heatg eergy cosumpto [Matzaras (004)]. Depedg o persoal thresholds ad desrable thermal comfort codtos, occupats usually adjust space thermostats at dfferet door temperature levels or vetlato rates, where avalable, thus dfferetatg the fal eergy cosumpto eve f all other parameters (.e., buldg costructo, locato ad stallatos) are the same. For these reasos, the buldgs ths study were selected to be more represetatve terms of typcal sze, costructos, umber of households, heatg perod etc,. 7

8 Heatg Ol Cosumpto Data Data o heatg ol cosumpto ( lters) were collected from 7 buldgs (4 blocs of flats wth 49 households total ad 3 famly homes) Serres. Each buldg was dfferet-sze, umber of households, sze of households areas etc. The cosumpto data were collected for the perod betwee October 00 ad Aprl 006 (5 complete heatg seasos) ad are summarzed fgures 9 ad , , , , , , , ,00 000,00 000,00 0,00 Buldg 9 Buldg 8 Buldg 7 Buldg 6 Buldg 5 Buldg 4 Buldg 3 Buldg Buldg Buldg 5 Buldg 4 Buldg 3 Buldg Buldg JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR TOTAL JUN JUL MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL Fgure 9. Mothly Heatg Ol Cosumpto data (lt) for 4 typcal buldgs (49 households) Serres ad for the perod betwee Oct, 00 ad Apr, 006, More detals about the households umber per buldg, households areas ad represetatve cosumpto data for moths are show table. A average area (weghted) per household of 94,4 m was estmated. It should be oted that Aprl, the usual moth for the ed of the heatg seaso, there are o supples of heatg ol to some of the buldgs. From these data, mothly ad yearly total averages of heatg ol cosumpto lters per household ad lters per household ad per m were calculated as show table. Each mothly ol cosumpto value reported table has bee calculated as a total 5 years mothly average for all buldgs ad households ad respectve households areas ad s weghted accordgly, For example, each value of table for March has bee dvded by the respectve umber of households ad the a total for all buldgs ad a Households 5 years mothly average was calculated , , , ,0 5000, ,0 0000, ,0 0000,0 5000, ,0 0,0 0000,0 Buldg Buldg Buldg 3 Buldg 4 Buldg 5 Buldg 6 Buldg 7 Buldg 8 Buldg ,0 Buldg Buldg 8000,0 7485,0 Buldg 8950,0 7900,0 Buldg ,0 000,0 Buldg ,0 5000,0 Buldg ,0 4500,0 Buldg ,0 7000,0 Buldg ,0 7700,0 Buldg ,0 3350,0 Buldg , ,0 0000,0 5000,0 0, Buldg 786,0 9480,0 9975,0 Buldg 900,0 9500,0 900,0 Buldg ,0 8900,0 3700,0 Buldg 4 000,0 5000,0 4000,0 Buldg ,0 6930,0 8550,0 Buldg 4 Buldg 3 Buldg Buldg Fgure 0. Yearly Heatg Ol Cosumpto data (lt) for same buldgs of fgure 9, 8

9 Buldg No of Average m per MARCH APRIL No Households Household TOTAL Weghted Average 94,4 Table. Buldgs sample, households areas ad typcal Ol Cosumpto data (lt) Moth Heatg days per moth (total) Heatg Hours per moth (total) 5 Years Mothly Averages (lt/hs) 5 Years Mothly Averages (lt/hs*m ) OCT ,46 NOV ,84 DEC ,44 JAN ,54 FEB ,78 MAR ,77 APR ,3 YEARLY TOTALS ,06 Table, 5 Years Mothly averages of Heatg Ol Cosumpto lters/household(hs) ad lts/(hs*m ) Serres, It was estmated that there was a total aual cosumpto of heatg ol of aroud,97 ltres per household ad 3.06 ltres per household ad per m. These were the basc data used for the further developmet of Natural gas ad dstrct heatg demad forecastg models by Mote Carlo methods. Table shows that a ormal heatg seaso Serres starts usually early October (aroud 0 th of October) ad eds by the md to the ed of Aprl (5 th of Aprl) summg to a total of aroud 96 heatg days per year (or 4,704 hours per year total). I terms of Heatg Degree-Days (HDDs o C/year), whch s a dex expressg the umber of degrees that the daly temperature s below a specfed threshold ad s ormally used eergy forecastg, the reported umbers vary cosderably Greece [Matzaras (004)]. Those values that have bee reported specfcally for Serres rage betwee 00 ad 500 a dfferece that s maly due to the heat bass temperature used the calculatos. Daly values of HDDs ca be obtaed oly by meas of calculatos. The HDD amout, for each day, expresses the dffereces betwee the ar temperature ad the basc temperature, whe the basc temperature s hgher tha the ar temperature ad o a yearly bass for example Serres, ca be expressed as a tegral tme: HDDs ( Tb Tar) dt So, ths value expresses the total defct of outdoor ar temperature relato to the basc temperature, useful estmatg heatg costs. Values of HDDs for Serres ( betwee /0/005 to 3/04/006 ad /0/006 to 3/04/007 (the last two heatg seasos, daly values per seaso) as publshed from a local meteorologcal stato o a daly bass ad for the last two wter seasos are show fgure. 9

10 30 5 Mmum Temp. & HDDs (8,3 o C heat base) betwee /0/005 to 3/04/006 ad /0/006 to 3/04/007 Serres (Prot) HDDs(005-6) -5,365E-06*DN 3 +,74E-03*DN -,408E-0*DN + 7,68E-0 R 9,993E HDDs(006-7) -3,39E-06*DN 3 + 9,6E-04DN + 3,06E-0*DN - 3,039E-0 R 9,99E-0 HDDs cumsum Mea Temp cumsum HDDs Mea Temp Poly. (cumsum 005-6) Poly. (cumsum 006-7) Fgure. Aalytcal Daly Mmum Temperatures ad HDDs for ad wter seasos as reported ole ( from a local weather stato earby Serres, Heatg Degree-days are egatvely correlated wth temperature ad postvely correlated wth heatg eergy demad. Oe terestg aspect preseted fgure s the smooth tegral profles (cumulatve sums) of heatg degree-days for the last two heatg seasos. They that ca be useful for demad forecastg reasos but eed further research wor. I ths study o HDDs methods for demad forecastg have bee used. Istead, the 5 year mothly heatg ol cosumpto data collected ad the respectve 5 years mothly ad weghted averages (grad total) of table have bee used. The data comes from a typcal sample of Serre s buldg stoc, wth a umber of households per buldg ragg from 9 to 39 ad wth a mea home area ragg from 65 m to 0 m, whch costtutes a farly represetatve sample. Weather aspects, domestc users habts regardg the operato of ther heatg stallatos as well as other effects o demad such as seaso, day of the wee, ad chageable prce aspects (after cosderg the varablty ad sgfcat crease of ol prces durg the last 5 years) are heret ad costtute hdde factors specfc for the cty of Serres. These data were trasformed to atural gas eergy terms ad were fed as prmary puts to the Mote Carlo Natural Gas ad Dstrct Heatg demad forecastg models, alog wth some addtoal specfc factors descrbed ext. Demad Forecastg Data - Methodology & Models Natural Gas Demad For Natural gas demad forecastg, the values for heatg ol of table were trasformed to respectve mothly averages ad respectve yearly totals of heatg ol eergy demad per household ad per m usg the equatos: Edol V * ρ * H MJ/Hs () 0

11 Edol V * ρ * H * AHS MJ/(Hs* m ) () V Where ρ are the heatg ol values of table, H s the heatg ol desty (~0,85 Kg/lt), s the A Gross calorfc value of Heatg Ol (~44,47 MJ/Kg), ad HS s the average household area (~94,4 m ). Assumg that the effcecy of a ew atural gas cetral heater/boler s about NG 0,9 ad a effcecy of the exsted old heatg ol bolers aroud 0,8, the respectve atural gas mothly ad yearly demad volumes per household ad per m ca be estmated from: VHeatg NG E dol (3) H NG η η NG H NG s the Gross Calorfc value (GCV) or heatg value of Natural gas. North Greece s suppled wth Russa atural gas of a GCV value o a ormal volumetrc bass of aroud MJ/ m 3. Typcal daly demad was calculated based o the estmated umber of heatg days per moth ad all the values were ormalzed to a 30 days moth. Ths has bee doe because the factors ad costat of equato 4, appled for the allocato to hourly values ad descrbed below, eed to be ormalzed to 30 days moth data. From the last equatos ad usg the heatg ol data reported table, mothly ad daly atural gas heatg demad data were obtaed ad are show table 3. The estmated,096 Nm 3 /Hs yearly total was allocated to hourly typcal values accordg to the followg procedure. Sce 984 ad utl the troducto of atural gas the Gree eergy system 997, tow gas was produced the Aspropyrgos Referes by DEFA (publc compay) ad dstrbuted for household cetral heatg to buldgs the ceter of Athes. Natural Gas Forecasted Average Demad for Resdetal Heatg Serres Moth Heatg days per moth Heatg Hours per moth (total) Mothly Demad Nm 3 /(Hs*moth) Daly Demad Nm 3 /(Hs*Day) Daly Average Nm 3 /(Hs*hour) Pea Load Nm 3 /(Hs*hour) Normalzato (to 30 days) Factor OCT 504 8,74 0,44 0,5 0,7 NOV ,07 0,67 0,464,0 DEC ,9 0,378 0,658,0 JAN , 0,380 0,66,0 FEB ,5 0,38 0,4 0,9 MAR ,7 0,7 0,474,0 APR ,9 0,54 0,67 0,8 Yearly ,66 Table 3, Natural Gas Forecasted Average Demad for Resdetal Heatg Serres, Papaas D., (997) reported hourly ad mothly load factors of gas cosumpto for cetral heatg accordg to DEFA (old gas compay Athes area) ad are show table 4. These are factors that ca produce a typcal daly household heatg demad profle ad combed together ca allocate yearly demad per household to hourly values. I addto, all these hdde aspects that affect atural gas heatg eergy demad, ad are specfc for a Gree rego are also heret. The hourly factors have bee adopted ths study just to reproduce the typcal hourly heatg demad profle of a household Greece. The Mothly factors have bee adjusted ad modfed accordg to the followg procedure tag also to accout the heatg demad data specfcally collected for Serres. Based o the Load Factors, the yearly total demad per household was allocated to daly (4hours) demad profles, characterstc for each moth, usg the followg equato: Vheatg VHeatg NG NG Lh Lm Nm 3 /(Hs*hour) (4) 360

12 Hourly Heatg Load Factors (Lh) Hour Heatg Load Factor Hour Heatg Load Factor Mothly Heatg Load Factors (Lm) Moth Papaas D., (997) Serres' Adjusted Factors 0,00 3 0,047 October 0,0000 0,94 0, ,0477 November 0,95, ,04480 December,6770, ,0580 Jauary,635, ,05896 February 3,47, ,0730 March,7457, ,0854 Aprl,3369 0, ,0 0 0,0896 May 0,0000 0, , ,0896 Jue 0,0000 0, , ,0808 July 0,0000 0,0000 0, ,06840 August 0,0000 0,0000 0,04 4 0,0477 September 0,0000 0,0000 Table 4, Hourly ad Mothly (modfed) Load Factors for Resdetal Heatg Serres, These data, as show table 5, were fed as the prmary puts to the Mote Carlo Natural Gas demad forecastg model. It should be oted that the daly totals show of table 5, after the allocato procedure are equal to the daly averages calculated earler ad show table 3, Ths s the expected result of the proper adjustmet ad modfcatos to the mothly heatg load factors (Lm) developed specfcally for Serres ad show the last colum of table 4. Natural Gas Forecasted Daly Household Demad Profles for Heatg Serres (Values Nm 3 /(Hs*hour)) Hour October November December Jauary February March Aprl :00 0,034 0,063 0,0896 0,0900 0,0574 0,0645 0,0364 :00 0,0066 0,0 0,073 0,074 0,0 0,05 0,0070 3:00 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 4:00 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 5:00 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 6:00 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 7:00 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 8:00 0,0595 0,099 0,558 0,566 0,0999 0, 0,0633 9:00 0,0858 0,585 0,47 0,58 0,440 0,68 0,09 0:00 0,4 0,08 0,989 0,3003 0,95 0,5 0,3 :00 0,388 0,564 0,3635 0,3653 0,39 0,67 0,475 :00 0,55 0,34 0,306 0,304 0,939 0,79 0,8 3:00 0,34 0,445 0,3467 0,3484 0, 0,496 0,407 4:00 0,35 0,448 0,347 0,3488 0,4 0,499 0,409 5:00 0,56 0,30 0,390 0,3306 0,08 0,369 0,335 6:00 0,45 0,683 0,3804 0,38 0,438 0,739 0,544 7:00 0,653 0,3054 0,4330 0,435 0,775 0,37 0,757 8:00 0,050 0,3786 0,5368 0,5394 0,3440 0,3865 0,79 9:00 0,34 0,475 0,606 0,609 0,3884 0,4364 0,460 0:00 0,53 0,464 0,658 0,663 0,47 0,4738 0,67 :00 0,53 0,464 0,658 0,663 0,47 0,4738 0,67 :00 0,48 0,453 0,5888 0,597 0,3773 0,439 0,390 3:00 0,98 0,354 0,503 0,5047 0,39 0,366 0,039 0:00 0,35 0,448 0,347 0,3488 0,4 0,499 0,409 Daly Totals,744 5,068 7,86 7, 4,605 5,74,97 Daly Averages 0,44 0,67 0,378 0,380 0,4 0,7 0,54 Pea Demad 0,5 0,464 0,658 0,66 0,4 0,474 0,67 Table 5, Natural Gas Forecasted Daly Demad Profles for Heatg of a typcal household Serres, I terms of atural gas potetal use for coog, hot servce water etc. Serres, because of the complete lac of statstcal data for such a use of Natural gas Greece, the respectve load factors for atural gas coog ad hot servce water (wth separate factors gve for hot water for tche ad for bath) gve by Papaas (997) were adopted. These factors are comg from offcal statstcal reports of NSSG, that are based o commo use of electrcal cooer ad electrcal hot water bolers, curretly

13 used the vast majorty of the Gree households. Based o them ad o the older electrcal eergy demad data for such uses show table 6, the average gas demad profles for coog ad hot servce water, were developed for the cty of Serres usg smlar equatos as above. For example, coog atural gas demad per perso per year was obtaed as: Edcoog VNGcoog Nm 3 /(Pers*year) (5) coo er H NG Where Edcoog s the demad for coog estmated from hstorcal electrcty data to be about 850 MJ/(Per*year) ad cooer ~0,5 the effcecy of cooer. Edcoog VNGcoog ALhcoo Lmcoo Nm 3 /(Hs*hour) (6) 360 The daly demad profles for hot water demad (servce ad bath) were obtaed a smlar way. Mote Carlo Iput Data for N. Gas Demad Forecastg (Coog & Hot Water) Edcoog 850 MJ/(pers*year) Cooer Effcecy( cooer ) 0,5 Hot servce Water Seasoal factor (Wter/Summer) 0,8 Hot Water Demad 9 lt/(per*day) Water heat capacty (Cp) 4,868 KJ/(Kg*K) Servce Water Temperature chage, DTs 40 o C Natural Gas Gross Calorfc Value (H NG ) 4,030 MJ/m 3 Aver, No, of People per Houshold Serres,8 Per/Hs O/Off Electrcal bath boler frequecy of use 8 tmes/(hs*year) Hot Water Boler Capacty 50 lt Bath Water Temperature chage, DTb 5 o C Hot Water Boler Effcecy (cetral system) 0,75 Table 6, Iput Data for Natural Gas Demad Forecastg (coog & Hot water) Serres, Dstrct Heatg Demad Dstrct heatg demad was also calculated a smlar way based o the avalable heatg desel cosumpto profle ad tag to accout the respectve effceces, sce the dstrct heatg etwor s coected as a by-pass to the bloc s cetral heater/boler system, wth the latter remag place as bac up. The resdetal heatg load s ow from the heatg ol cosumpto data ad ca be calculated terms of hot water supply. I Dstrct heatg system, demad maagemet ca be acheved through effcet cotrol of hot water mass flow or of the supply temperature, usually both. Because of log tme respose of such systems usg temperature cotrol to demad, usually load cotrol s carred out wth automatc flow adjustmet to the demad for a specfc supply temperature, depedg o the weather codtos, for each day. Flow cotrol s carred out prmarly wth the use of varable speed pumps operatg ear the heat producg plat ad secodarly wth partal use of flow cotrol valves. The hot water mothly averages ad respectve yearly total demad per household ad per m for a typcal household Serres, are show table 7. They were also obtaed based o the values for heatg ol table ad usg the equatos: * M DH V Cp ρ WATER H ( Ts TR ) DH Kg/(Hs) (7) * M DH V Cp ρ WATER H A HS ( Ts TR ) DH Kg/(Hs* m ) (8) 3

14 Cp Where WATER T 4,868 KJ/(Kg*K) s the heat capacty of water, s s the supply temperature of the dstrct heatg etwor to the heatg boler, TR s the retur temperature to the dstrct heatg etwor, ad DH 0,9 the effcecy of a ew dstrct heatg boler 0,8 the effcecy of the exstg old heatg ol bolers. The data show the tables -7, as well as data regardg dfferet scearos were those that were fally used as puts to the Mote Carlo Demad Forecastg Models for ucertaty estmato ad comparsos. Dstrct Heatg (Hot Water) Forecasted Average Demad for Resdetal Heatg Serres Moth Heatg days per moth Heatg Hours per moth (total) Mothly Demad Kg/(Hs*moth) Daly Demad Kg/(Hs*Day) Daly Average Kg/(Hs*hour) Pea Load Kg/(Hs*hour) Normalzato (to 30 days) Factor OCT ,74 0,09 0,59 0,7 NOV , 0,69 0,94,0 DEC ,55 0,40 0,47,0 JAN ,57 0,4 0,49,0 FEB ,87 0,5 0,67 0,9 MAR ,8 0,7 0,300,0 APR ,85 0,097 0,69 0,8 Yearly ,5 0,49 Table 7, Dstrct Heatg (hot water) Forecasted Average Demad for Resdetal Heatg Serres, 3. Demad Forecastg ad Mote Carlo Ucertaty, Effcet maagemet of eergy supply requres forecastg of eergy demad. The prvatzato of the utlty dustres recet years has forced them to reapprase ther proft margs. The prces these ew prvatzed compaes are able to charge are costraed by govermet regulatory bodes. To couter the possble eroso of ther proft margs, the compaes, stead have looed to mmze ther costs. O a daly bass, the compaes ca become more effcet by accurately predctg demad wth a cosequet reducto storage ad dstrbuto costs [Fldes (997)]. Eergy demad projecto for a area ad for a defte tme perod s a dffcult tas because of the complexty, assumptos ad ucertates volved estmatg such projectos. It s also dffcult due to the extet of weather ad clmatc fluece o demad. I order to crcumvet these problems, usually forecastg s based o stadardsed, so-called temperature corrected cosumpto fgures. Cosumpto statstcs are corrected order to obta the cosumpto durg "ormal" clmatc codtos. The stadardsed values allow predcto of demad the log term ad ecessary vestmets to be made order to cover t. Aual gas demad forecast modelg utlses a umber of techques, from ecoometrc modelg to a assessmet of dvdual load equres. It s axomatc that f a developg coutry cotues to acheve ecoomc growth eergy demad wll eep o rsg. Apart from mature ecoomes, a decle eergy demad sgals ecoomc decle or recesso. A typcal example of ths s what has happeed the East Europea coutres. I these coutres eergy demad fell dramatcally wth ecoomc collapse expereced the late eghtes. May of the DH compaes operatg for may years these coutres ow face eormous rehabltato challeges. There s a well prove strog l betwee domestc heatg demad ad clmatc codtos. However, dfferet dstrbutors throughout Europe have developed models whch dffer to some extet. The smplest tae to accout the degrees-days defed above. Others tae to accout temperatures of the precedg days, the wd ad the sulght. Stll others reta oly the part of cosumpto whch s susceptble to clmatc codtos or weght the degrees-days (the weght factor s less summer ad hgher wter) allowg the etrety of the cosumpto to be corrected. 4

15 Thus, as t was already aalysed, a umber of dfferet sources of formato provde the data used to calculate the aual demad forecasts. Apart from the weather data, hstorcal gas demad (f avalable) adjusted to the demad that would be expected seasoal ormal codtos, dces of retal eergy prces relatve to GDP deflator, dces of real prce of gas ad heavy fuel ol ad a varety of commercal cesus sources or ecoomc dcators such as household umbers, household dsposable come, employmet dex, fuel prce forecasts etc. are also employed. A typcal Greece atural gas demad forecast dagram utl 05 by the Gree mstry of developmet s show fgure. I fgures 3 ad 4 are show basc scearos regardg atural gas ad DH peetrato ad demad Serres domestc heatg maret Natural Gas Demad Forecast (bcm) ,5 4,06 4,67 5,5 5,94 6,5 6,8 6,93 7,5 7, Data Source: Mstry of Developmet, MD 3/3/006 0,600 0,500 0,400 0,300 0,00 0,00 0,000-0,00 Estmated Peetrato Rate (% of total umber of households) of N. Gas Serres (Based o the respectve peetrato rate data for Thessalo ad Thessaly areas). %TotalHs 0,08488*year - 0,0306*year + 0,00000 R 0, Year % Total Hs Poly. (% Total Hs) Fgure. Greece Natural Gas demad forecast (of the MD) utl 05. Fgure 3. Estmated Peetrato Rate (as % of the total umber of elgble households) of N. Gas Serres (Based o the respectve peetrato rate data for Thessalo ad Thessaly areas) Forecasted Demad of DH KgH O/year ad N. Gas m 3 /year Serres (assumg,5 tmes hgher peetrato rate for DH due to the earler developmet) DH Demad (KgHO/year) N. Gas Demad (Nm3/year) Year Fgure 4. Forecasted Demad of DH KgH O/year ad N. Gas m 3 /year Serres (assumg,5 tmes hgher peetrato rate for DH due to the earler developmet) All these models ad projectos are just pot estmates, havg a commo lmtg factor, the heret ucertaty. Most eergy studes are performed by costructg scearos based o sets of ey put parameters that correspod to ether a hgh, low or omal eergy cosumpto future. These pot estmates provde, at best, a rage of equally probable outcomes ad do ot corporate ay otos of 5

16 probablty or dstrbuted outcomes. Thus, factorg a elemetary oto of ucertaty, wthout a better meas of characterzg ad dealg wth t, maes t mpossble to mae reasoable polcy decsos[tschag, (995]). Forecastg s the process that carres ucertates by defto. Ths cossts of ucertaty the forecast model structure as well as ucertaty the exogeous ad varous put data avalable ad especally those regardg ecoomc growth ad meteorologcal forecasts. Ths ucertaty propagates through the model to create ucertaty the outputs. Data qualty geeral, s a mportat ssue forecastg, as t s the most, f ot all, moder huma actvtes. Eergy demad formato of hgh qualty s requred for varous reasos such as for vestmet plag, early operatoal plag that leads to better effcecy, storage plag ad plag of securty of supply, evrometal maagemet as well as the settg of a effectve eergy prcg polcy. By defto ay forecastg model/equato, whch s bascally a fuctoal relato betwee the forecasted quatty ad several depedet varables, s oly a abstract represetato of the real world. A wdely adopted geeral represetato of such a fuctoal relatoshp betwee N put varables (X, X, X N ) ad a forecasted quatty Y has the form: Y f ( X ),X,X 3..., X N (9) X cludes correctos (or correcto factors), as well as quattes that tae to accout varous other sources of ucertaty, such as dfferet observers, samples, ad tmes at whch observatos are made (e.g. dfferet days), etc. Each of X, X etc. may actually represet results of partal estmato processes wth varous sources of elemetal ucertates [Papadopoulos, (00)]. Thus, the geeral fuctoal relatoshp f (9) expresses a whole process, ad partcular t cotas all these relevat data avalable that ca cotrbute a sgfcat ucertaty to the forecast. I Mote Carlo methods, estmators are assged to each of the varables whose respectve probablty desty fuctos are cosstet wth the avalable formato about the correspodg quattes. The estmators' dstrbutos express the state of complete owledge about the varables. The use of Mote Carlo as a ucertaty estmato method has receved proper atteto oly the last few years. Its potetal as a computer based method, as well as ts felds of applcato are curretly uder extesve research. Mote Carlo smulato ca be afflated to those umercal methods that ca be geerally called as computer-tesve methods, ad partcularly to the computer based radom samplg methods. However wth today's ever-creasg computer power ad cosderg the beefts of the method ucertaty aalyss compared wth aalytcal methods, the word tesve becomes qute relatve. Orders ofmagtude creases computatoal power eable desgers to smulate the performace of large ad complex systems. What was a major tas oly a few years ago today s a route classroom exercse. Aalysts are able ow to smulate the respose of complex systems thousads of tmes wthout much effort. The crease processg speed ad memory capacty computers s, however, oly the begg of the story. The professo has bee developg a rage of dfferet methods for drawg meag out of these calculatos so that the results ca be used productve ways. I short, as these advaces cotue, we are the process of a revolutoary chage the way we ca aalyze, ad thus maage, ucertaly. Itally, t was devsed as a expermetal probablstc method to solve dffcult determstc problems sce computers ca easly smulate a large umber of expermetal trals that have radom outcomes. Eve aspects that are clearly determstc, such as the perodc varatos of evrometal temperature durg a 4-hour day or smpler susodal waves ca be also charactersed usg probablstc methods. Dfferet combatos of values are obtaed by radomly samplg across the dstrbutos ad are used to talze a model. A dstrbuto of outputs s obtaed correspodg to the dfferet model rus. These eable aalysts to deal realstcally wth the may states ad scearos that are part of a full aalyss of ucertaty for a system. Ideed, a thorough aalyss of ucertaty mght easly requre the cosderato of hudreds, f ot thousads of versos of a determstc aalyss. As a geeral rule, ths computatoal problem has lmted the ablty of the varous aalyss professos to develop credble aalyses for the maagemet of ucertaty. Now, however, the computatoal problem s dsappearg. 6

17 The ucertaty ca be aalyzed wth varous techques, cludg smulato methods le Mote Carlo aalyss (ad varatos le Lat Hypercube Samplg), ad aalytc methods such as frst-order (or Gaussa) approxmato [Ima ad Helto, (988), Morga ad Hero, (990)]. Whe appled to ucertaty estmato, radom umbers are used to radomly sample parameters' ucertaty space stead of sgle pot calculato carred out by covetoal methods. So the Mote Carlo method reles o "radomess". Such a d of aalyss, accordg to the author s pot of vew, seems to be closer to the uderlyg physcal realty. Ay 'state' of a system must realty be uderstood ad subsequetly represeted, ot as a pot a multdmesoal phase space but rather as a small rego whose sze reflects the fte owledge. So, o the probablstc vew we loo at ay system through a 'wdow' (phase space cell), whereas the determstc (aalytcal) vew t s uderstood that we are exactly rug o a phase space trajectory, whch s clearly a urealstc assumpto vew of the presece of assumptos ad errors [Papadopoulos, (000)]. Accordg to the Ergodc Hypothess, tme averages are equvalet to esemble averages. Mote Carlo smulatos deal specfcally wth the probablty of geeratg esembles of mcro or eve macro states ad use esemble averages to obta or smulate macroscopc quattes. So, what ucertaty s all about are these probable states of ature. The prevously descrbed estmates are referred to radom varables that assged to each of the physcal quattes Y, X, X tag geeral values wth a reasoable rage. The wdths of the correspodg probablty destes p(y), p(x ), p(x ), gve by the respectve stadard devatos, serve as the parameters that qualtatvely descrbe to what extet the uow values are ow, or other words the wdth gves the ucertaty. Every put varable of equato 9 s examed separately order to aalyse how ucertates the puts "propagate" to the result. All the above ca be geeralsed assumg that a forecasted result s obtaed from N radom varables. They jotly follow a N-dmesoal Gaussa (ormal) dstrbuto wth the assocated N-fold probablty desty fucto gve by: p N C Cj, j e N N ( X, X,..., X ) ( π ) C ( X ( ))( ( )) E X X j E X j where C s the covarace matrx of the C j defed below, C s the determat of C, ad (0) C j s the cofactor of C j determat C. Partcularly, the cofactor C j of ay elemet C j s defed to be the determat of order N- formed by omttg the th row ad jth colum of C, multpled by (-) +j. The covarace matrx s gve by: C C N C where C σ ( X ) C j... C N N C C C... N C C C N NN... ( ) N ( X ( )) ( ) E X X j E X j ad (), j are the respectve varaces ad covaraces. So, ther jot dstrbuto represets the actual state of owledge of the multple formato. The expectato values of the elemetary dstrbutos serve as the results,.e. as estmates of termedate or the elemetary puts. The square roots of the dagoal ad the off dagoal elemets of the covarace matrx, the stadard devatos ad covaraces, represet the dvdual ad mutual ucertates of the puts. Wth ths approach, elemetary covarace matrces actually may represet group ucertates. Addtoally, these could be geometrcally llustrated by vectors wth legths equal to the stadard devatos a Eucldea N-dmesoal ucertaty hyperspace. I case of depedet Gaussa 7

18 8 elemetary probablty dstrbutos, ths could be actually a N-dmesoal hyper-ellpsod whch s composed of may lower dmeso ellpsods correspodg to the group ucertates. The coses (or possbly eve better, the hyperbolc coses) of the agles betwee pars of the elemetary vectors ad group vectors equal the correspodg partal ad group correlato coeffcets respectvely. Sets of stadard ormal dstrbutos () ~ N(0,) have bee geerated to model the put data as radom fuctos of these stadard ormal dstrbutos usg e.g. the trasformato: () z s x X * + I order to mprove the accuracy of smulatos, z() were obtaed from () by applyg the followg procedure as descrbed by Papadopoulos, (000), removg ths way ay bas from the raw computer geerated dstrbuto. The procedure s qute smple. After havg obtaed the frst computer geerated stadard ormal dstrbutos () ~ N (0,), a trasformato s appled by calculatg () ( ) ( ) z σ. Sce () follow ormal dstrbutos (stadard), z() also follow stadard ormal dstrbutos gve by: ( ) Z z e f π whch actually defes the dstrbuto of the mea (0 ths case) of a radom sample from a ormal populato. I other words the stadard ormal dstrbuto of the raw computer geerated stadard ormal dstrbuto s obtaed. So, the mea of the geerated stadard ormal varable, after the d trasformato, obtaed aalytcally s: ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 0 z σ σ σ σ σ σ σ σ Smlarly, the varace of z obtaed aalytcally s: ( ) ( ) ( ) ( ) ( ) ( )( ) ( ) 0 z z z σ σ σ So the varables z() obtaed after the d trasformato ad used to model the Mote Carlo puts, are truly stadard ormal varables havg exactly a mea 0 ad a stadard devato, dow to the computer roud off accuracy. The same procedure wors wth other ds of dstrbutos (e,g. uform). It s a varace reducto techque, whch mmses the chace of bas the overall set of results. The effect of ths adjustmet s show fgure 5. Fgure 5. Adjustmets of put dstrbutos for Bas removal

19 Results of Mote Carlo Demad Forecastg Typcal Mote Carlo Natural Gas ad DH hourly, weely, ad yearly demad profles for households heatg obtaed (o a hourly bass) for Serres are show fgures 6 to 0. Fgure 6. Mote Carlo Natural Gas daly demad profle for a typcal October day Serres, Fgure 7. Weely Mote Carlo N. Gas Demad profle for Jauary Serres, Fgure 8. Mote Carlo Forecasted Yearly Natural Gas demad profle obtaed for Serres, I weely atural gas heatg demad profle of fgure 7, weeed demad has bee cosdered 0% creased from a typcal weeday. 9

20 Fgure 9. Mote Carlo Forecasted Natural Gas Yearly Demad profle for Serres (009-04), Fgure0. QQplot of sample 5 years N. Gas Mote Carlo demad data Fgure. Mote Carlo Daly N, Gas demad forecast for coog of a typcal household Serres, Fgure. Mote Carlo Daly N, Gas demad profle for hot servce water of a typcal household Serres Fgure 3. Mote Carlo Itegrated profle (4 hours) of demad for coog of a typcal household Serres, Fgure 4. Mote Carlo Itegrated profle of N. Gas demad for hot water of a typcal household Serres, 0

21 Fgure 5. Mote Carlo DH Daly demad forecast for a typcal household Serres. Fgure 6. Mote Carlo Itegrated profle (4 hours) for DH demad of a typcal household Serres, Fgure 7. Dstrbutos of Daly (per Moth) demad per typcal household Serres Developg the Mote Carlo Models for Serres Natural Gas ad DH resdetal Demad forecastg requred the ablty to ru a varety of forecastg scearos for multple purposes ad wth dfferet outputs. Each Mote Carlo scearo ru volves adjustg the puts based o the varous sythetc dstrbutos ad re-estmatg the model, havg ts ow ratoal assumptos. The forecast put dstrbutos are based o assessmets of lely varatos for each seres ad the seres are ept trscally cosstet each Mote Carlo ru. To gve a typcal example of ths, the 5 year forecast demad, tally for DH ad at a later stage for Natural gas, ther peetrato rates to the Serres resdetal heatg maret the begg (year ad ) are ucorrelated. However, after the start-up of the Natural Gas supply, estmated for year 3, these are led together ad become terrelated. After some tme ad as they are approachg the pot of maret saturato they gradually become egatvely correlated varables.

22 Daly demad profles for each category of demad (e,g, atural gas demad for heatg, coog, etc.), weely profles o a hourly or daly bass, mothly or yearly profles as well as further future projectos based o varous rates of maret peetrato, wth ther respectve ucertaty estmates, ca be obtaed ad assessed wth ths method. Due to the umber ad the varety of exstg possbltes, oly a typcal sample of the forecasts obtaed are preseted here. Mote Carlo Natural Gas ad Dstrct Heatg Tarffs, Tarffs estmato ad forecastg s based o the supply cotract avalable for dstrct heatg ad o prces of atural gas earby ctes respectvely. Tarfs for DH are estmated as: HEL BP BPo Euros/MWh where BPo s the basc prce that s gog to be set by the DH HELo compay, HEL s the offcal heatg ol prce for the bllg moth, publshed by the local observatory offce of local fuel prces, ad HELo s the heatg ol prce publshed by the same local authorty for October 006. I addto ad the ed of every heatg seaso, the DH compay wll compare the total paymet per buldg/household order to guaratee that the bll was ot hgher tha the 80% of the respectve cost of heatg eergy suppled from ol heatg/boler systems based o the heatg ol prce, the fxed cost of stallato ad a stable cost of mateace. Smlar tarff polcy s followed by the atural gas supply compaes wth the ma excepto that the customer has to be burdeed the cost of the gas heatg stallato (heater/boler etc.). DH compay has aouced that the tal stallato costs (DH substato, boler etc.) are covered them so are zero, somethg that clearly gves them a substatal completve advatage, at least the begg. Heatg ol prces (ex-factory) for the last heatg seasos are show fgure 8 whle fgure 9 a resdetal heatg ol tarffs Euros/(lt*m ) varablty wth seaso s show, related to selected buldgs of Serres. The hgh varablty observed fgure 9 s due both the varablty of ol prce wth the same seaso but also o the reasos explaed above regardg the eergy demad varablty also observed (dfferet thermal problems of buldgs, old stallatos etc.), It must be added that the same problems affect maly the low come households for profoud reasos whch have bee extesvely aalysed by Satamours (006). Heatg Ol Prce 0/005-04/007 (Euro/lt) Resdetal Heatg Ol Tarfs (Varablty wth Seaso) 500,000 0, ,000 0, ,000 0, ,000 40, , , , ,000 30, ,000 5/9/005 4//005 4/3/006 /7/006 0/0/006 8//007 8/4/007 6/8/007 Heatg Ol Prce 0/005-04/007 (Euro/lt) P rce, E uro/(lt*m ) 0, , , , , , , , Lters of Heatg Ol Euro/(lt*m) Hetg Ol (Data Source- Hellec Petroleum). Fgure 8. Heatg Ol prces (ex-factory) for heatg Fgure 9. Resdetal Heatg Ol Tarff Varablty wth seasos (0/005 to 04/007). heatg seaso for selected buldgs Serres.. A typcal Mote Carlo tarff scearo profle for DH s Serres that s based o the avalable heatg ol cosumpto mothly data, tarff model descrbed ad prces per MWth based o dstrct heatg eergy prces two other ctes North Greece (Amyteo ad Ptolemas) s show fgure 30. The Mote Carlo demad ad tarff forecastg models developed here ca be easly adapted ad erched wth real

23 local DH ad Natural Gas cosumpto profles data, whe they wll become avalable, ad they wll be fatherly expaded after operatos start-up of both fuel etwors. Fgure 30. Mote Carlo Yearly DH Tarff profle (per Moth) for a typcal household Serres Coclusos The ew Dstrct Heatg cetral system uder developmet the cty of Serres, brgs substatal potetal savgs to the cosumers wth the troducto of competto wth heatg ol. Wth the future troducto of Natural gas, where ts demad ad cosumpto depeds o uses other tha oly cetral heatg, addtoal savg beefts wll be offered to the same cosumers the log ru. Mote Carlo demad forecastg models ad methods were developed to assess ths ew stuato, whch s of partcular terest because of the uprecedeted (for Greece) competto the scheme brgs to the cty of Serres. Mote Carlo, as a Probablstc ucertaty estmato method, maes use of radom varables ad radom fuctos to descrbe the varous sources of ucertaty durg all stages of a process (from specfcatos ad desg up to the proper terpretato of the results) ad ca be appled a varety of moder processes. Kle S.J., (985) much earler has metoed that although a great deal of wor has bee doe o the "trees" of ucertaty estmato procedures, t would seem that there s o geeral recogto that a "forest" called ucertaty aalyss exsts as a vald part of expermetal wor. Kottegoda N.T., ad Rosso R., (998) later added that past decades there has bee a rreversble chage emphass from descrptve to feretal statstcs. I ths respect, sce all statstcal fereces ad the rs ad relablty of decso mag are tag place uder ucertaty, the ecessty of a practcal assessmet of what s partally ow or ucerta s self evdet. I would le to state also my full agreemet wth the more recet vew of De Meyer Aroud, (00) that t s obvous from the may spectacular project falures that the tme has come to reth some of the tradtos project maagemet but also of busess admstrato geeral. I the era of rapd chage, ucertaty s a rule, ot a excepto ad compaes that uderstad that have the greatest chace to produce spectacular project successes. 3