Quantitative Viability Analysis of Electricity Generation Robert - PowerPoint PPT Presentation

Quantitative Viability Analysis of Electricity Generation Robert Riebolge/David Lenton Carisway/DGA Consulting TUESDAY, 6 OCTOBER 2015

Agenda Overview of Project Objectives and Approach • • Approach to Forecasting SA Demand and Generation Mix Economic Model • • High Level Principles • Costs and Benefits Assessed • Sensitivity and Monte Carlo Analysis

Project Objectives • Quantify the economic viability of a nuclear generator being commissioned in South Australia in 2030 or 2050. • Compare the cost effectiveness of market entry using a nuclear generator against other generation options • Small Nuclear • Large Nuclear • CCGT with CCS • CCGT • Consider the viability against a range of scenarios of demand and renewable generation • Produce a flexible and transparent model that allows the user to modify the assumptions and consider the impact on the relative NPVs

Overview of Approach Forecast of Calculation of Assessment and renewable output for new projection of generation in South Australian demand for South South Australia generators Australia Monte Carlo Determination of Generator analysis on NPV costs & benefits Assumptions & to assess applying per Scenario sensitivity generator option Selection Integrated model so changing demand/renewable scenarios will flow through to NPV Calculations

Approach to Forecasting South Australian Demand and Energy Generation Mix

Overview of Approach • Historic Data Sets • Data sets of demand and renewables generation measured at half hourly (HH) intervals courtesy of SA Power Networks were provided for: Demand Major customer category • • Business consumer category • Residential consumer category • Hot water load Renewables • Photovoltaics (PV) • Wind generation

Overview of Approach Note: This data is sourced from SA Power Networks BUSINESS DEMAND (kW) 2012/13 SETTLEMENT SETTLEMENT Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 DAY PERIOD 1 0:00:00 507082 578654 524685 465071 571891 583575 499259 574170 572810 467666 570179 553491 1 0:30:00 496440 566402 505719 460541 559030 573235 492648 564146 563246 459399 553314 537788 1 1:00:00 489695 559181 494427 457628 549925 561510 481977 557341 556914 452459 548100 522420 1 1:30:00 480606 544134 484389 447330 546619 546904 473357 549322 551643 445206 534263 510946 1 2:00:00 475167 537159 478806 441106 540922 530539 464779 538243 543261 439085 528751 500751 1 2:30:00 462583 531623 470411 437980 535984 521377 460776 535650 540686 436649 527863 492770 1 3:00:00 454213 524770 466051 433732 536863 517812 453477 536936 540353 436977 518042 486969 1 3:30:00 455391 523095 464629 432411 540299 516999 452100 542572 540232 437005 514297 485230 1 4:00:00 452867 528992 467470 436312 550543 516919 455822 552739 548939 442678 517786 480005 1 4:30:00 450683 535909 469357 436534 572372 527896 456916 573281 571621 450799 525521 481412 1 5:00:00 452535 542214 468835 442049 607709 535297 456129 603187 603235 457231 534028 481833 1 5:30:00 459366 566121 480612 452311 644801 529268 437724 663376 664978 476638 562895 494358 1 6:00:00 464939 607098 497522 454144 683427 547203 434046 688732 718997 485854 603432 507606 1 6:30:00 479257 683664 529447 453882 745953 575117 448299 743211 746891 504232 665180 537413 1 7:00:00 487486 756787 519000 469009 821885 605200 466124 800873 799238 486732 716740 561926 1 7:30:00 490673 825129 534619 499321 880889 639228 489015 860887 860637 495157 765764 574440 1 8:00:00 472987 903425 553369 509804 933716 673315 506314 893016 898397 505357 832204 574836 1 8:30:00 481935 1005868 577027 521405 963737 701609 525823 923657 927000 515781 894295 593576 1 9:00:00 489626 1066498 598456 523369 962343 716294 542829 924838 936058 525005 931372 610557 1 9:30:00 512330 1092114 612558 529623 967458 726097 562693 932636 949795 530297 947160 630920 1 10:00:00 523878 1077315 617125 530836 975759 732064 576610 945169 963996 536516 954461 635699 1 10:30:00 538215 1058719 618677 534846 970829 728182 595846 945821 970833 546716 954952 642677 1 11:00:00 557204 1045126 613910 545086 969228 727151 606394 947388 981928 545390 959151 644361 1 11:30:00 562186 1034526 608176 550989 963698 711294 612470 944628 987891 544842 962967 643671 1 12:00:00 561248 1014847 598519 550192 963002 698086 620978 947845 997304 552430 969904 635944 1 12:30:00 553519 993504 587914 552106 964556 690603 623571 940958 1005854 557686 965432 629090 1 13:00:00 555154 983315 581786 547936 961584 686354 627230 939092 1019039 555388 966033 622762 1 13:30:00 553154 968006 576419 543274 956411 680173 623325 928193 1027273 551350 964206 620539 1 14:00:00 548560 956090 568683 537917 947085 678314 625351 915077 1023827 545764 967742 614579 1 14:30:00 548420 946522 563206 538362 935491 675289 625257 893826 1010923 539620 967033 605452 1 15:00:00 544874 933832 557846 536994 909947 670179 626391 872773 991477 537704 952232 599789 1 15:30:00 543945 911346 548609 533820 879498 657431 625738 845751 970353 537979 933185 592001 1 16:00:00 541662 889198 544836 530906 849541 647759 622058 818470 941397 533487 909367 591812 Example of HH daily load data for the business consumer category in 2012/13

Overview of Approach • The importance of ‘Big Data’ • Data sets of demand and renewables generation measured at half hourly (HH) intervals provide for: • More accurate categorisation of load characteristics of: • Peak load values and durations • Temporal (i.e. week days or weekends) and seasonal variability • Disaggregated consumer and renewable generation characteristics Finer HH granularity meaning load shapes more closely mimic • real time load shapes • Greater confidence in forecasting load shapes • ‘Statistically’ more credible forecasts

Historic System Demand Residential load curves at HH granularity in 2012/13

Historic System Demand Business load curves at HH granularity in 2012/13

Historic Renewable Generation PV generation at HH granularity in 2012/13

Historic Renewable Generation Wind generation at HH granularity in 2012/13

Fossil Fuel Generation Fossil fuels at HH granularity in 2012/13

Overview of Approach Historic Demand by Consumer Category • The following figures display demand profiles for a minimum • demand day and a maximum demand day for: • Major customer category • Hot water load Business consumer category • • Residential consumer category • Note that the business and residential consumer categories make up the bulk of the system load in South Australia.

Historic System Demand System daily load curves for a minimum and maximum demand day in 2012/13

Historic Demand by Consumer Category Consumer category daily load curves for a minimum and maximum demand day in 2012/13

Overview of Approach Historic Renewables Generation • The following figures display generation profiles of • renewables by season for: • Photovoltaics (PV) • Wind • Note that PV generation is highly seasonal while Wind does not have the same degree of variability by season.

Historic PV and Wind Generation by Season PV and Wind generation for a week in summer and winter in 2012/13

Historic PV and Wind Generation by Season PV and Wind generation for a month in summer and winter in 2012/13

Overview of Approach Historic Generation to Meet the System Demand • The following figures display generation profiles for: • • Photovoltaics (PV) • Wind • Fossil Fuels needed to meet the system demand for a minimum demand day and maximum demand day. Note that on low demand days much of the system demand is • met by renewables.

Historic Generation to Meet the System Demand PV generation for a minimum and maximum demand day in 2012/13

Historic Generation to Meet the System Demand Add wind generation for a minimum and maximum demand day in 2012/13

Historic Generation to Meet the System Demand Add fossil fuel generation for a minimum and maximum demand day in 2012/13

Forecasting System Demand in 2030 • The model’s ‘Variables Input Sheet’ for demand parameters to be varied provides for the following: • Business consumer category (high, low, medium). • Residential consumer category (high, low, medium). • Major customer category (high, low, medium). • Hot water load (high, low, medium). • Co-generation (yes, no). • Electric vehicles (% of vehicle population).

Example Inputs to the Demand Model in 2030 Growth in Demand Business (% pa) medium 1.2% Residential (% pa) medium 1.5% Major Customers (% pa) medium 0.2% Hot Water Load (% pa) low -0.2% Co-generation (on/off) no 0 Electric Vehicle Market Share (%) 25% • The demand parameters are applied to the HH data sets for: • Business consumer category • Residential consumer category Major customer category • • Hot water load • Co generation is set either ‘on’ or ‘off’ • A percentage of vehicle market share can be chosen for EVs