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

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1.1. Background Hawaiian Electric Company (HECO) is an investor owned utility serving the energy needs for the island of Oahu with approximately 295,000 customers. Annual energy production is approximately 8000 GWh and system load typically ranges from a peak of 1200 MW to a minimum of 600 MW. The total generating capacity of the system is 1756 MW comprised of HECO owned generating units and independent power producers (IPPs), primarily fossil-fueled units. Hawaiian Electric operates three power plants on the island of Oahu with a total of 17 generating units. These include (8) baseload steam units, (6) cycling steam units, and (3) three peaking combustion turbine (CTs) units. The steam units burn low sulfur fuel oil (LSFO); two CTs burn diesel fuel and one CT burns biofuel. The IPPs provide baseload energy, which includes: 1) a 46 MW city-owned waste-to-energy unit (HPower), 2) a 180 MW coal-fired unit (AES), and 3) a 208 MW LSFO-fired combined cycle unit (Kalaeloa). The baseload units operate continuously throughout the year except during maintenance outages. HECO-owned cycling units are committed daily to meet system demand and typically shut down following the evening peak. Peaking units are committed as required to meet system demand during system peaks and contingencies. Starting in January 2009, the Hawaii Natural Energy Institute, the Hawaiian Electric Company, and the General Electric Company jointly developed and validated detailed, state-of-the-art power systems models of the Oahu electrical system to study the impacts of integrating the “Big Wind” projects. Different models were developed to analyze various time scales of system operation ranging from seconds, to hours, to weeks, over an entire year of operation. These models were used to assess specific high wind power scenarios, identify the potential challenges of integrating large amounts of wind power, and assess potential solutions to these challenges. The Department of Energy (DOE), the Hawaii Natural Energy Institute (HNEI), and the Hawaiian Electric Company (HECO) provided funding for the Oahu Wind Integration Study. In addition to GE, HNEI and HECO, the project team included AWS Truepower, who provided wind power and wind forecast data, and the National Renewable Energy Laboratory, who provided wind and solar power data and validation of these data. The National Renewable Energy Laboratory also sponsored a Technical Review Committee (TRC), which was assembled five times during the project. The TRC consisted of technical experts from both industry and academia that brought experience from similar projects from around the world. The TRC provided oversight, guidance, and assessment of the work performed in this study. Simulations of the Oahu system were performed for the year 2014. Inputs such as system load, unit heat rates, fuel prices, planned unit maintenance, forced outage rates, etc. were based on forecasts provided by HECO. The wind plants located on Molokai and Lanai were electrically connected to Oahu via a High Voltage Direct Current (HVDC) cable system (see Figure 1-1). These wind plants were not connected to the local island loads. New, on-island resources were also modeled including 100 MW of wind power and 100 MW of combined centralized and distributed solar photovoltaic (PV) power. 4
Oahu 100MW Wind 100MW PV Lanai 200MW Wind Molokai 200MW Wind Figure 1-1. Illustrative schematic of the Oahu, Molokai, and Lanai interconnection. Different wind power scenarios were developed by the project team and were analyzed using the modeling tools. For each scenario, these models were used to identify system performance and operating characteristics such as unit commitment and dispatch, wind energy delivered, fuel consumption, total variable cost, thermal unit ramping, system frequency performance during transient system events, and system frequency performance during variable wind events. Following these assessments a number of potential strategies were simulated to improve system performance (i.e., increase wind plant capacity factors, improve system reliability, and improve system efficiency). Three primary wind scenarios were eventually considered; each examining a staged approach of integrating wind power on the Baseline 2014 Oahu system, as presented in Table 1-1. Scenarios #2 and #4 were removed from the study by combining resources and strategic planning and execution of the study. This allowed the project team to focus on the three scenarios below. Baseline Table 1-1. Oahu Wind Integration Study Scenario Scenario Scenario # 1 Scenario # 3 Scenario # 5 Title 2014 Baseline “Big Wind” Oahu only “Big Wind” Oahu + Lanai only “Big Wind” Oahu + Lanai + Molokai Oahu - 100MW 100MW 100MW Wind Lanai - - 400MW 200MW Molokai - - - 200MW Solar PV Oahu - 100MW 100MW 100MW The study began with Scenario 5 to determine the effects of integrating 400 MW of total wind energy from the islands of Lanai and Molokai to Oahu. Scenario 5 was selected first because it would push the limits of both the simulated system as well as the modeling tools. In Scenario 3, 400 MW of wind energy from the island of Lanai was integrated into the Oahu system. Finally, Scenario 1 studied the Oahu system prior to the integration of any off-island wind energy. The 100 MW of solar PV was deployed in each scenario primarily to evaluate its impact on wind energy delivered to the Oahu system. Many of the solar PV installations across the island will be 5
Page 1 and 2: Oahu Wind Integration Study Final R
Page 3: 1.0 Executive Summary Hawaii is an
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 and 46: human decision-making play a substa
Page 47 and 48: Voltage Governor Support Response P
Page 49 and 50: maneuvering that may be a result of
Page 51 and 52: hours of operation that warrant fur
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Another factor for consideration is
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Figure 5-3 GE MAPS TM model results
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models of all of HECO-owned and IPP
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Table 5-4 Proposed thermal unit gov
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Table 5-6 Summary of various dynami
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Table 5-8. Dynamic Database - Excit
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o 50 MW Oahu2: Modeled as a 50 MW g
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400 MW Off-island Oahu Wind Plant M
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turbine/governor systems in the dyn
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Pulsating logic: o All units under
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Hz 60.8 60.6 60.4 60.2 60 Frequency
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modeled as a single CT/ST and is ba
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6.2.1. Development of Wind and Sola
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Estimated power curve P farm = 30MW
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Figure 6-3 The largest 1 minute win
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Figure 6-5. Histogram of wind power
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At the request of the evaluation te
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6.2.5. Reserve requirements The Oah
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Figure 6-10. Thermal unit heat rate
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6.3.1.5.2. AES coal-fired steam pla
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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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