GUIDE TO MAXIMUM DEMAND DATA

GUIDE TO MAXIMUM DEMAND DATA. Published: September 2014 GUIDE TO MAXIMUM DEMAND DATA. IMPORTANT NOTICE Purpose AEMO has prepared this document to ...
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GUIDE TO MAXIMUM DEMAND DATA.

Published: September 2014

GUIDE TO MAXIMUM DEMAND DATA.

IMPORTANT NOTICE Purpose AEMO has prepared this document to provide supplementary information to the NEFR 2014.

Disclaimer This document contains data provided by or collected from third parties, and conclusions, opinions, assumptions or forecasts that are based on that data. AEMO has made every effort to ensure the quality of the information in this document but cannot guarantee that the information, forecasts and assumptions in it are accurate, complete or appropriate for your circumstances. This report does not include all of the information that an investor, participant or potential participant in the National Electricity Market might require, and does not amount to a recommendation of any investment. Anyone proposing to use the information in this report should independently verify and check its accuracy, completeness and suitability for purpose, and obtain independent and specific advice from appropriate experts.

Accordingly, to the maximum extent permitted by law, AEMO and its officers, employees and consultants involved in the preparation of this document: 

make no representation or warranty, express or implied, as to the currency, accuracy, reliability or completeness of the information in this document; and



are not liable (whether by reason of negligence or otherwise) for any statements or representations in this document, or any omissions from it, or for any use or reliance on the information in it.

© 2014. The material in this publ ication may only be us ed in accordance with the copy right p ermiss ions on AEMO’s w ebsite.

Australian Energy Market Operator Ltd NEW SOUTH WALES

ABN 94 072 010 327

QUEENSLAND

SOUTH AUSTRALIA

www.aemo.com.au VICTORIA

[email protected]

AUSTRALIAN CAPITAL TERRITORY

TASMANIA

GUIDE TO MAXIMUM DEMAND DATA.

1 – EXPLANATION OF MAXIMUM DEMAND RESULTS 1.1 Simulation methodology AEMO has published a spreadsheet titled MD data1 to illustrate the impact of rooftop PV generation on nonindustrial maximum demand (MD). This document explains the data contained within that spreadsheet. To illustrate what the values in the spreadsheet represent, this document steps through the process AEMO uses to forecast maximum demand. In summary, this process involves two main steps: AEMO uses seasonal weather outcomes that are randomly generated using historical data2; then Monash University’s demand model is applied to each of these seasons’ outcomes to produce demand simulations. For example, to calculate MD for the 2014-15 summer, AEMO simulates 1,000 different possible 2014-15 summers3 and calculates the expected demand for each half-hour of every summer. Figure 1 shows five of the daily demand profiles (LoadWithPV) from one of the simulated summers.4 In total, there are 182,000 daily demand profiles generated.5 This results from:

#days × #simulations = 182 × 1000 = 182,000. Each half-hourly period therefore has 182,000 values, and these are used to produce distributions of demand for each half-hourly period (see Section 1.2). Similarly, Monash University’s PV model estimates the amount of rooftop PV generation occurring for each day of the weather simulation. Figure 2 shows the amount of rooftop PV generated for the same five simulated days in Figure 1. Again, 182,000 daily rooftop PV generation profiles are produced. Total consumption is determined by adding the daily demand profiles and daily rooftop PV generation profiles. Figure 3 shows “demand plus the amount of rooftop PV generated” profiles (LoadWithoutPV) for the same five simulated days. The 182,000 daily demand profiles are produced in this manner.

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Available at http://www.aemo.com.au/Electricity/Planning/Forecasting/National-Electricity-Forecasting-Report/NEFR-Supplementary-Information. Refer to Monash technical report for a detailed discussion of this method, available at http://www.aemo.com.au/Electricity/Planning/Forecasting/National-Electricity-Forecasting-Report/NEFR-Supplementary-Information. 3 The same methodology holds for winter. 4 Note that the values in the figure are dummy data and are only being used as a means of explanation. 5 Note that for all regions except Tasmania summer is defined as the period from October to March and includes 182 days. For Tasmania, summer is defined as the period from December to February only. 2

© AEMO 2014

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GUIDE TO MAXIMUM DEMAND DATA.

Figure 1 – Demand profiles for 5 days of a simulated summer.

Figure 2 – Rooftop PV generation estimates for the same 5 days of a simulated summer.

© AEMO 2014

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GUIDE TO MAXIMUM DEMAND DATA.

Figure 3 – Demand plus PV generation estimates for the same 5 days of a simulated summer

1.2 Hh profile chart The “Hh profile chart” tab in the spreadsheet gives the distribution of all half-hourly demands predicted over the 1,000 weather simulations. As discussed in Section 1.1, 182,000 values are generated for each half-hourly period and are used to produce distributions for LoadWithPV, PV, and LoadWithoutPV. These distributions are used to generate probability of exceedance (POE) values for each half-hour. Taking the ninetieth percentile6 of one of these distributions gives the 10% POE. Similarly, the fiftieth percentile corresponds to the 50% POE and the tenth percentile corresponds to the 90% POE. The profiles for each of these POE levels are given in the “Hh profile chart” tab as shown in Figure 4. These POE values are not comparable to the MD POE values because all 182,000 half-hourly demands are used in the distribution. Refer to Section 1.3 for a discussion of annual POE values. Figure 4 – Hh profile chart showing half-hourly 10% POE values for NSW 2014-15 summer (medium scenario) Half-hourly non-industrial demand profiles of all simulated demand values NSW summer 2015 base 10

Sum of MW Row Labels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Column Labels LoadWithoutPV LoadWithPV 6169 5914 5609 5306 5052 4902 4844 4882 5078 5357 5933 6511 6935 7380 7627 7823 8112 8284 8412 8504 8568 8663

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Demand profile 10000

9000

8000

PV 6175 5919 5614 5310 5057 4907 4849 4887 5085 5362 5935 6492 6891 7286 7479 7631 7874 8009 8103 8168 8208 8285

0 0 0 0 0 0 0 0 0 10 19 64 110 174 241 292 346 385 427 450 479 488

7000

6000

Power (MW)

Region Season FYE Scenario Probability

LoadWithoutPV

5000

LoadWithPV PV

4000

3000

2000

1000

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Period

Ninety per cent of observations are below this value.

© AEMO 2014

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GUIDE TO MAXIMUM DEMAND DATA.

An important aspect of the simulation results is that POE levels for different demand types do not necessarily correspond to the same drivers. For example, the 10% POE value for load with PV may occur under different conditions than the 10% POE value for rooftop PV generation. This is most noticeable in winter, when high demand and low PV generation occur on cold days (and vice versa for warm winter days). In summer this is less of an issue as high temperatures usually result in both high demand and high rooftop PV generation. Given this, comparisons between demand and PV generation profiles should only be used as an approximation.

1.3 Annual forecast chart To generate annual POE forecasts, AEMO records the peak demand in each simulated season, as demonstrated in Figure 5.7 Two possible simulations and their seasonal peaks are shown. Another 998 season simulations are also generated and their seasonal peak recorded. This gives 1,000 simulations, resulting in 1,000 peaks from which AEMO constructs a peak demand distribution. The percentiles of these distributions correspond to the different POE levels.8 The “Annual forecast chart” tab in the spreadsheet shows the forecast seasonal POE levels. It also includes the impact of PV9 and energy efficiency10 on these seasonal peaks. These forecasts only consist of seasonal peaks, so they are significantly higher than the values in the “Hh profile chart” tab which consist of all half-hourly demands. These seasonal peak distributions are used to generate the POE forecasts for MD in the NEFR.

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Note that these values are dummy data and are only being used as a means of explanation. For example, the ninetieth percentile corresponds to the 10% POE. 9 Refer to the Monash technical report for a discussion of the methodology used to estimate rooftop PV generation, available at: http://www.aemo.com.au/Electricity/Planning/Forecasting/National-Electricity-Forecasting-Report/NEFR-Supplementary-Information. 10 Refer to the 2014 NEFR forecasting methodology information paper for a discussion of the methodology used to estimate the impact of energy efficiency on MD, available at http://www.aemo.com.au/Electricity/Planning/Forecasting/National-Electricity-Forecasting-Report/NEFRSupplementary-Information. 8

© AEMO 2014

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GUIDE TO MAXIMUM DEMAND DATA.

Figure 5 – Example of peak demand in each simulated season used for annual POE forecasts.

The only value used in this simulation is the peak demand.

Another simulated season might have this demand as a peak.

© AEMO 2014

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GUIDE TO MAXIMUM DEMAND DATA.

Figure 6 shows an example of the “Annual forecast chart” tab. The impact of rooftop PV generation and energy efficiency is evident in the drop in forecast values.11 While these values are comparable to the 2014 NEFR forecasts, they do not give any information on the time that MD occurs. Figure 6 – Annual forecast chart showing seasonal 10% POE forecasts for NSW summer (medium scenario) 2014 non-industrial maximum demand forecasts for seasonal peaks NSW summer base 10

Sum of MW Row Labels 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034

Column Labels LoadWithoutPV LoadWithPV LoadWithPVandEE 12444 12196 12008 12893 12507 12275 13227 12829 12539 13470 13041 12666 13700 13332 12867 14013 13549 12991 14295 13715 13094 14451 13941 13266 14674 14089 13345 14899 14299 13497 15117 14516 13666 15320 14638 13759 15583 14824 13919 15862 15089 14162 16061 15277 14332 16319 15491 14526 16282 15407 14410 16505 15613 14596 16611 15654 14614 16825 15771 14708

POE forecasts 18000

16000

14000

12000

Demand (MW)

Region season scenario POE

10000 LoadWithoutPV LoadWithPV

8000

LoadWithPVandEE 6000

4000

2000

0 2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

Year

11

Note that transmission losses and auxiliary load are not included in these values.

© AEMO 2014

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