Barth, R.

Brand, H.

Hasche, B.

Meibom, Peter

Ravn, H.

Weber, C.

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017All island grid study - Results concerning system operationMeibom, Peter; Barth, R.; Hasche, B.; Brand, H.; Weber, C.; Ravn, H.Publication date:2008Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Meibom, P., Barth, R., Hasche, B., Brand, H., Weber, C., & Ravn, H. (2008). All island grid study - Resultsconcerning system operation [Sound/Visual production (digital)]. 2008 European Wind Energy Conference andExhibition, Brussels, Belgium, 31/03/2008All I l d G id St d R lt i t ti s an  r  u y – esu s concern ng sys em opera onPeter Meibom, Risø DTURüdiger Barth, Bernard Hasche, Heike Brand, IER – University of StuttgartChristoph Weber University of Duisburg Essen ,   -Hans Ravn, RAM-løse EDBEWEC 2008, Brussels, April 2th 2008Content presentation • Purpose • Assumptions• ToolR lt• esu s• ConclusionsP rposeu• All Island Grid Study – Work stream 2B: Wind variability management studies• Impacts of renewable generation on All-Island power system:• Variability and predictability of renewable generation     • Costs and benefits of absorbing various levels of renewable generation• Emissions and costs of existing units• Most suitable mix of complementary conventional plantsD t il d d l f t ti• e a e  mo e  o  sys em opera onAss mptionsu• No consideration of the grid structure and load flow issues in the All Island system• Six portfolios investigated: determined using investment model (WS2A)• 1000 MW transmission capacity to Great Britain• Reduced representation of the power system in Great Britain:• Eight aggregated power plant classes• Wind power production time series equal to Irish wind power production time series shifted one hour in time       • Usage interconnector determined day-ahead and not changed intra-dayO er ie portfolios Installed capacities of ne plantsv v w  –    w 90007000800050006000WP1P2P330004000MWP4P5P6100020000Coal OCGTs ADGTs CCGTs BaseRenewTidal Wind WaveSched ling modelu  • Stochastic, mixed integer, linear optimisation model• Stochastic input in the form of a scenario tree• Stochastic input: Wi d d ti f t (di t h)• n  power pro uc on orecas s spa c• Electricity demand forecasts (dispatch)• Forecasts of demands for replacement reserves (unit       commitment)• Replacement reserve: demand for positive reserves that replaces i i ( ti ti ti b 5 i t )sp nn ng reserves ac va on mes a ove  m nu es :• Demand dependant on forecast horizon (forecast horizons from 5 minutes to 36 hours ahead)• Demand dependant on wind power and load forecasts Sched ling modelu  • Optimisation over all outcomes represented by the scenario tree taking both demands for electricity and demand for spinning and replacement reserves into account• Minimisation of expected costs Expectation taken over branches in   .      scenario tree• Unit restrictions: minimum up time, minimum down time, start-up ti i i t bl ti l l i i li f lme, m n mum s a e opera on eve , p ece-w se near ue  consumption curve, restriction on ability to provide spinning reserveRolling planning in sched ling model   u  Sched ling modelu  • Output from SM:• In general: levels of each variable and marginals (shadow prices) of each equation.• Realised hourly unit commitment and dispatch of each unit        • Realised distribution of each reserve power category on units• Hourly power exchange with Great Britain• Hourly emissions of CO2 from each unit • Total system-wide operational costs: fuel, start-up, variable ti d i t t t d ith iopera on an  ma n enance, cos s connec e  w  consum ng CO2 emission permitsScenario Tree Tool  • Probabilistic approach• Generates scenario trees• Generate time series for forced outages of power plantsE ti t L f l d t ti (LOLE) f ti tf li• s ma e oss o  oa  expec a on  o  genera on por o o• On the basis of:• Historical hourly time series of wind power production (or wind speed)           and electricity demand• Wind production forecast accuracies and load forecast accuracies for different forecast horizons  • Forced outage rate and mean time to repair of power plants• Scenarios of installed wind power capacity and yearly electricity demands• Assumption about what quantile of total forecast error distribution to be covered by reservesNet load realised load min s realised ind po er prod ction  –   u   w  w  u10000800040006000ad [MW]2000Net loa01 673 1345 2017 2689 3361 4033 4705 5377 6049 6721 7393 8065 8737-2000P1 P2, P3, P4 P5 P6Negative net load: export to GB, pumped hydro, wind curtailmentΔ net load change in net load from one ho r to the ne t :       u    x  P1 [MW] P2, P3, P4 [MW] P5 [MW] P6 [MW]Maximum 1600 1822 2572 3732 Minimum -1619 -2383 -3366 -4473 Positive Mean 338 361 392 412 Negative Mean -289 -315 -346 -373 Standard deviation 417 447 489 529   90% percentile 538 572 610 647 10% percentile -486 -518 -561 -602  Most hours: Δ net load below 1000 MW (at least in 8192 hours out of 8760 hours in portfolio P6) A erage demand replacement reser esv    v 15001300W]9001100t reserves [MW700Replacement300500RPortfolio 1 Portfolio 2 Portfolio 3 Portfolio 4 Portfolio 5 Portfolio 61 6 11 16 21 26 31 36Forecast horizon [h]Pro ision of replacement reser esv    v• Replacement reserve:• Nearly 100% from offline units (OCGTs)• Supply often higher than demand -> costs of providing replacement reserve often zero   Pro ision of positi e spinning reser ev   v   v• Pumped hydro storage (70 MW when pumping)• Old coal units: • In P1-P5 due to high part-load efficienciesN t i P6 d t hi h CO2 i• o  n  ue o g   pr ce• ADGTs: high part-load efficiencies, used especially in P3 due to lack of base load units• Provision from wind power: increases with increased share of wind power (1322 hours in P6)Yearl prod ction distrib ted according to f el t pey u  u    u  y300002000025000Wh]15000uction [GW500010000Produ0ADGASERITGASCOALGASOILBIOGASPEATORAGEWATERWINDBASELOMIDM STP1 P2 P3 P4 P5 P6D ration c r e po er e change ith Great Britainu  u v  w  x  w    1000600800200400ort [MWh]-400-20001 673 1345 2017 2689 3361 4033 4705 5377 6049 6721 7393 8065 8737mport / Expo-800-600Im-1000P1 P2 P3 P4 P5 P6Dispatch thermal plants in P1 to P5      • Coal and new CCGTs: • Low number of start-ups (below 100 per year)• High capacity factors (0.6-0.9)St t i d it f t d ith i i• ar -ups ncrease an  capac y ac ors ecrease w  ncreas ng wind• OCGTs: low capacity factors (0.1),  around 300 start-ups per yearA erage earl n mber of start ps per nit for each f el t pev  y y u   -u   u    u  y700 500600300400t-ups/Unit100200Start0OALGASGASSOILEATCBASELOADMIDMERIT GAPP1 P2 P3 P4 P5 P6Reliability Portfolio Hours where load isHours where demand spinning reserve is not metHours where demand for replacement reserve is not met due to lack of  not met           capacity P1 0 4 96 P2 3 6 101 P3 0 1 98 P4 1 5 115 P5 0 3 88 P6 23 77 544 P6: reliability problems due to usage interconnector determined day-h da eaWind c rtailment u P1 P2 P3 P4 P5 P6 Provision of spinning reserves 0 0 0 01 0 0 07 0 10[TWh] .  . .Other reasons than provison of spinning reserve [TWh]0 0 0 0 0.02 0.48   Total curtailment as percentage of wind power production 0 0 0 0 0.5 2.3  N b f it li i th 1000 h ith thum er o  un s on ne n e  ours w  e lowest number of units online for each portfolio.1012ne68units onlin4umber of u02Nu1 72 143 214 285 356 427 498 569 640 711 782 853 924 995P1 P2 P3 P4 P5 P6Units bro ght online/offline from one ho r to the ne t u     u    x 1520510online501 717 1433 2149 2865 3581 4297 5013 5729 6445 7161 7877 8593a units o-10-Delta-20-15P1 P2 P3 P4 P5 P6Val e of impro ed ind po er and load forecastsu   v  w  w      P1 P2 P3 P4 P5 P6 Absolute cost reductions due 1 2 8 0 4 8 13 6 18 5 65 0to perfect forecast [MEuro] . .  . . . .Relative cost reductions due to perfect forecast [%] 0.05 0.4 0.2 0.7 1.2 3.6 • Improved forecasts -> lower demand for reserves -> additional savings related to investments in peak load plants (not included in this study)• P4 with may base load units has higher costs of partial predictability d t P3 ith OCGTcompare  o  w  many sConcl sionsu• Model development:• Advanced methodology for reserve estimation dependant on forced outages, and uncertainties in wind power production and load forecast implemented  • Wind power able to provide positive spinning reserve• Stochastic unit commitment and dispatch model using mixed integers• Rescheduling every third hour taking updated wind power and load forecasts and associated change in demand for reserves         into accountConcl sionsu• Renewable power production: From 16 % of yearly electricity demand in portfolio P1 to 59 % in portfolio P6.• Increased wind in system: lower and more variable net load, increased demand for replacement reserves    • Transmission Great Britain: Net import decreases as wind increases• Conventional unit operation when wind increases: • Only small problems with following variations in net load and provide reserves• More start ups and reduced capacity factors as wind power -         increases• Wind curtailment: Negligible in P1-P4 and amounts to 0.5 % in P5 and 2.3 % in P6 in terms of percentages of yearly wind power production. • Small number of units online in some hours in high wind           portfolios: requirements on number of units online will increase wind curtailment Concl sionsu• Reliability of the All Island power system:• Portfolios rely on the production from wind power and import from Great Britain to cover peak load• Portfolio P3 shows the highest overall reliability      • Impact of improved forecasting:• Cost reductions due to perfect forecasts relatively small in comparison to the total system operation costs of the All Island power system.• Absolute sum of the cost reductions is not negligible        . • Value of perfect forecast increases with increasing wind power capacity installed.• Value of perfect forecast higher in P4 (base load plants) compared to P3 (peak load plants)

All island grid study - Results concerning system operation

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All island grid study - Results concerning system operation

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