Curtailment and Network Voltage Analysis Study (CANVAS)

Page 70

Figure 44 Total annual potential revenue loss due to tripping (anti-islanding and limits for sustained operation) and V-VAr curtailment for VPP operator with 996 BESS sites analysed for three scenarios (real case, TS-129 and ENA)

It is seen that in the real case, highest potential revenue loss is less than $10k/year which increases up to $15k/year and $36k/year for the analysed TS-129 and ENA. Table XIV presents the corresponding minimum, average, and maximum annual revenue loss for the VPP operator according to analysed spot market prices. Table XIV Potential annual revenue loss for the VPP operator due to V-VAr and tripping curtailment: minimum, average, and maximum loss for real-case, TS-129 and ENA scenarios

Minimum loss ($/year) Average loss ($/year) Maximum loss ($/year)

Real 6 266 9,500

TS-129 7 637 14,689

ENA 26 1743 36,124

Further research is needed to understand how these results may change with increasing levels of DER. On the one hand, with increasing BESS fleet, higher instances of curtailment may increase the overlap with high sport market price events. On the other hand, increasing D-PV installations may put further downward pressure on day-time spot market prices during the day. Future research aims to further investigate potential correlations between these peak spot market prices and high curtailment instances.

6.5.2 Financial impact for D-PV sites For the Solar Analytics fleet with D-PV systems, the financial loss calculation was more straightforward as the sites did not have storage capability like the BESS. Therefore, curtailed D-PV generation was a definite loss and could not be used after the curtailment event. Because the Solar Analytics data did not include net load for the households, some assumptions had to be made for estimating the revenue loss for the energy users: •

Low loss case: it is assumed that all curtailed energy was during net-export conditions (energy that could be exported if it were not curtailed) and therefore, loss revenue is calculated from an average feed in tariff (FiT) rate of 10 c/kWh.

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Appendix A: Methodology

7min
pages 79-83

8.1 Next steps

2min
page 78

References

5min
pages 89-93

Appendix B: Details of project plan

11min
pages 84-88

8 Concluding remarks

3min
page 77

7 Socio-technical insights

9min
pages 72-76

6.5.2 Financial impact for D-PV sites

1min
page 70

6.5.3 Upscaled curtailed generation & emissions impact

2min
page 71

6.4 Summary of curtailment findings

1min
page 67

6.3.4 Volt-var curtailment (scenarios

3min
pages 64-66

6.3.3 Volt-var curtailment (real case

6min
pages 58-63

6.3.2 BESS and D-PV Volt-VAr curves

5min
pages 52-57

5.4 Measures to address curtailment

15min
pages 36-40

6.2.2 BESS ‘tripping’ (anti-islanding and limits for sustained operation

0
page 48

2.4 Prior work on social aspects of curtailment

3min
page 17

5.3 Perceived impacts of curtailment

9min
pages 33-35

2.6 Key gaps that CANVAS aims to address

3min
page 20

4.2.2 Tripping (anti-islanding and limits for sustained operation) curtailment

1min
page 27

4.2.3 Volt-VAr curtailment

4min
pages 28-29

3.1.4 Bureau of Meteorology (BOM) weather data

0
page 22

2.5 Prior data-driven technical analyses of DER voltage control and curtailment

7min
pages 18-19

5.2 Knowledge and experiences of curtailment

3min
page 32
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