Oahu Wind Integration Study - Hawaii Natural Energy Institute ...

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The final two tools were developed and enhanced specifically for this assessment: 4. Statistical wind power variability assessments for system up-reserve estimates and selection of challenging system events, and 5. Interhour screening to identify the unit commitment during challenging system events associated with high penetrations of renewable energy and relatively low system upreserves. These five tools coupled together in a series of simulation efforts provided information over a range of timescales of interest to the project team. The range of timescales over which these tools work together is shown in Figure 5-1. 46
Voltage Governor Support Response Positive Sequence Load Flow (GE PSLF TM ) 50 40 1 sec Governor Inertia Response Governor Response Response Statistical Wind Power Variability Assessments 30 0% percentile (10min) = -0.14 0% percentile (60min) = -0.45 20 0.1% percentile (10min) = -0.09 0.1% percentile (60min) = -0.27 10 Interval = 10min Interval = 60min 99.9% percentile (10min) = 0.1 99.9% percentile (60min) = 0.3 100% percentile (10min) = 0.1 100% percentile (60min) = 0.4 0 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 50 40 30 0% percentile (10min) = -82.85 0% percentile (60min) = -269.05 20 0.1% percentile (10min) = -54.90 0.1% percentile (60min) = -163.16 10 Hourly results for 1yr 2008 Interval = 10min Interval = 60min 99.9% percentile (10min) = 60 99.9% percentile (60min) = 18 100% percentile (10min) = 105 100% percentile (60min) = 289 0 -150 -100 -50 0 50 100 150 Multi-Area Production Simulation (GE MAPS) Unit commitment/ dispatch, representing operating rules, benchmarked against system operation. Quantify energy production, variable cost, wind power curtailment, emissions, etc for each scenario to assess… • Wind/solar delivered • Unit commitment • Variable cost • Emissions, etc 50 40 30 0% percentile (10min) = -0.14 0% percentile (60min) = -0.45 20 0.1% percentile (10min) = -0.09 0.1% percentile (60min) = -0.27 10 99.9% percentile (10min) = 0.1 99.9% percentile (60min) = 0.3 100% percentile (10min) = 0.1 100% percentile (60min) = 0.4 0 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 50 40 30 0% percentile (10min) = -82.85 0% percentile (60min) = -269.05 20 0.1% percentile (10min) = -54.90 0.1% percentile (60min) = -163.16 10 2008 Automatic Generation AGC Regulation Control Interval = 10min Interval = 60min Interval = 10min Interval = 60min 99.9% percentile (10min) = 60 99.9% percentile (60min) = 18 100% percentile (10min) = 105 100% percentile (60min) = 289 0 -150 -100 -50 0 50 100 150 Inter-hour Variability Analysis 10min results for 1yr Long-term Dynamic Simulat ions (AGC) TM Sub-hourly wind/ solar/ load changes with respect to reserve, based on commitment and dispatch. 1 min 10 min 1 hr 1 day 1 wk Economic Economic Dispatch 100MW Oahu + 200MW Lanai + 200MW Molokai 50 Interval = 1min Interval = 5min 45 Interval = 10min 40 35 30 0.1% percentile (1min) = -12.27 99.9% percentile (1min) = 11.7685 0.1% percentile (5min) = -31.336 99.9% percentile (5min) = 33.0615 25 0.1% percentile (10min) = -49.305 99.9% percentile (10min) = 54.0865 Negative most (1min) = -22.479 Positive most (1min) = 22.5425 20 Negative most (5min) = -54.9215 Positive most (5min) = 65.0885 Negative most (10min) = -90.258 Positive most (10min) = 95.845 15 10 5 0 -50 -40 -30 -20 -10 0 10 20 30 40 50 Farm power (MW per interval) Quantify reserve violations, fast starts & load shed events caused by sub-hourly wind/ solar/ load changes… • Assess reserve requirements • Assess fast-start events • Select windows for further analysis Frequency (%) Interhour Renewables Variability Analysis TM 50 45 40 35 30 0.1% percentile (1min) = -12.27 0.1% percentile (5min) = -31.336 25 0.1% percentile (10min) = -49.305 Negative most (1min) = -22.479 20 Negative most (5min) = -54.9215 Negative most (10min) = -90.258 15 10 5 Interval = 1min Interval = 5min Interval = 10min 99.9% percentile (1min) = 11.7685 99.9% percentile (5min) = 33.0615 99.9% percentile (10min) = 54.0865 Positive most (1min) = 22.5425 Positive most (5min) = 65.0885 Positive most (10min) = 95.845 0 -50 -40 -30 -20 -10 0 10 20 30 40 50 1sec results for one 1hr Planning Arbitrage Long-term Dynamic Simulations (Auto Gen Control) Frequency analysis, including governors and AGC response. Initialized from MAPS and driven by major wind/solar events. Quantify frequency performance during wind/solar variability events and wind ramp events… • Reserve requirements • Types of regulating units, • Benefit of increasing ramp rates Multi-Area Production Simulat ion (GE MAPS TM ) Positive Sequence Load Flow (GE PSLF) Full Transmission model for voltage & stability performance, governor response, contingency analysis. 1ms results for 1min Quantify system stability performance during contingencies… • Wind/solar plant requirements (freq control, LVRT, voltage control) • System contingencies • Generator trip • Load rejection • Other Figure 5-1. GE power system modeling tools for the Oahu Wind Integration Study 5.1.1. GE MAPS TM production cost model Throughout the year, system operators at HECO have to make decisions about which generators should be used to produce electricity in each hour of the day (i.e. commitment and dispatch 47
Page 1 and 2: Oahu Wind Integration Study Final R
Page 3 and 4: 1.0 Executive Summary Hawaii is an
Page 5 and 6: Oahu 100MW Wind 100MW PV Lanai 200M
Page 7 and 8: Figure 1-2. Renewable Integration S
Page 9 and 10: educe its output, while remaining i
Page 11 and 12: Figure 1-6. Share of annual energy
Page 13 and 14: Figure 1-8. Total annual variable c
Page 15 and 16: extreme downward variability over t
Page 17 and 18: 1.6. Short-timescale wind variabili
Page 19 and 20: Figure 1-11. Frequency performance
Page 21 and 22: down-reserve requirement. Alternati
Page 23 and 24: capability, the remaining 163 MW of
Page 25 and 26: o Table 1-2 showed that nearly 95%
Page 27 and 28: 1.8.2. Thermal Unit Modifications
Page 29 and 30: 6.3.1. Overview of the GE MAPS TM B
Page 31 and 32: 10.5.3. Down-reserve...............
Page 33 and 34: Figure 6-13. Baseline 2014 scenario
Page 35 and 36: tripped off-island wind plant carry
Page 37 and 38: Table 8-10. Summary of short-term a
Page 39 and 40: 2.0 Acknowledgements Our team would
Page 41 and 42: are presented in this section for S
Page 43 and 44: 4.1. Study objectives The General E
Page 45: human decision-making play a substa
Page 49 and 50: maneuvering that may be a result of
Page 51 and 52: hours of operation that warrant fur
Page 53 and 54: and maintenance costs) based on ass
Page 55 and 56: Another factor for consideration is
Page 57 and 58: Figure 5-3 GE MAPS TM model results
Page 59 and 60: models of all of HECO-owned and IPP
Page 61 and 62: Table 5-4 Proposed thermal unit gov
Page 63 and 64: Table 5-6 Summary of various dynami
Page 65 and 66: Table 5-8. Dynamic Database - Excit
Page 67 and 68: o 50 MW Oahu2: Modeled as a 50 MW g
Page 69 and 70: 400 MW Off-island Oahu Wind Plant M
Page 71 and 72: turbine/governor systems in the dyn
Page 73 and 74: Pulsating logic: o All units under
Page 75 and 76: Hz 60.8 60.6 60.4 60.2 60 Frequency
Page 77 and 78: modeled as a single CT/ST and is ba
Page 79 and 80: 6.2.1. Development of Wind and Sola
Page 81 and 82: Estimated power curve P farm = 30MW
Page 83 and 84: Figure 6-3 The largest 1 minute win
Page 85 and 86: Figure 6-5. Histogram of wind power
Page 87 and 88: At the request of the evaluation te
Page 89 and 90: 6.2.5. Reserve requirements The Oah
Page 91 and 92: Figure 6-10. Thermal unit heat rate
Page 93 and 94: 6.3.1.5.2. AES coal-fired steam pla
Page 95 and 96: Figure 6-12. Baseline 2014 scenario
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Methods to forecast solar power wer
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1. No forecast of wind and solar (S
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Table 6-3. Scenario 5: Wind and sol
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Figure 6-18. Scenario 5. Fuel energ
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Table 6-4. Scenario 3: Wind and sol
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6.7. Conclusions As the wind energy
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Figure 6-21. Scenario 3. Fuel energ
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The results of the scenario analysi
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Reduce On-line Regulating Reserves
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energy is accepted, these units are
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Figure 7-3. Scenario 5C: Fuel energ
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Sce nario 5C Scenario 5F1 40MW down
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Scenario 5F1 Scenario 5F2 Seasonal
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7.4.1. Sensitivity to Solar Forecas
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on the time of day and the load lev
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Figure 7-15. Scenario 5F3. Number o
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Figure 7-18. Renewable energy deliv
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more expensive cycling energy displ
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Figure 7-23. Delivered wind energy
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18-weeks during the year was includ
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should be noted that the effective
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Note that the average heat rate for
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The heat rate by unit type is shown
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The capacity factors of the renewab
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On the other hand, short-term analy
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Table 8-2. Long term screening resu
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Figure 8-4. Drop in wind and solar
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emain flat in their generation for
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Hz Hz 60.2 60 Frequency AGC Mode 59
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Figure 8-9. Long-term analysis for
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The up-range adequacy of the system
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The short-term screening results fo
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MW 120 100 80 60 40 20 0 0 600 200M
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Hz Hz 60.2 60 59.8 59.6 59.4 0 600
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events., which will be described la
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Figure 8-18. Down-range10 at the be
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o Frequency performance (Section 8.
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MW 450 400 350 300 250 200 Scenario
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8.5.3. Results Using the volatile w
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MW 80 60 40 K2 K3 K4 K5 K6 MW 100 2
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Figure 8-30 shows that the maneuver
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• The load flow model in GE PSLF
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Frequency RMS (Hz) 0.035 0.03 0.025
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Intermediate Farm power 1 1+ sT + _
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In step 2 above, the 0.1% percentil
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1 / 120 per unit Ramp Rate Power (M
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MW 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0
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MW MW 1.800 1.600 1.400 1.200 1.000
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Under the scenarios considered in t
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- No response (base case) - Moderat
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Hz MW 62 61.5 61 60.5 System freque
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esulted in noticeable improvements
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In both scenarios none of the 200 M
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Figure 8-49: Transient simulation r
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Figure 8-52: Transient simulation r
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o Wind turbine inertial response tr
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o Reducing the on-line regulating r
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9.2.5.3. Wind Plant Ramp Rate Limit
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o Given the high power rating of th
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power, thereby contributing to the
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10.1.5. Recommend that HECO evaluat
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10.2.2. Recommend solar plants to p
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o This recommendation assumes that
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acking down thermal units. In Scena
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o In Scenario 5F3 and 5F2 the regul
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10.5.5. Additional Operator Informa
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simultaneous occurrence of the larg
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