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«NATIONAL RENEWABLE ENERGY LABORATORY RENEWABLE ENERGY OPPORTUNITY ASSESSMENT FOR USAID MEXICO November 2015 Andrea Watson, Ricardo Bracho, Rachel ...»

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Tie unit commitment and economic dispatch modeling analysis and related input • data requirements to the reforms being considered in the post-reform wholesale market (e.g., the time intervals relevant to forecasting errors should match unit commit reschedule and gate closure times).

Ensure interconnection agreements and PPAs with independent power • producers include requirements to provide forced outage data and real-time generation and metrological conditions to inform centralized forecasting by grid operators.

Develop requirements for self-generators to provide performance data and • forecasts to grid operators to inform centralized forecasting, grid integration scenario development, and resource modeling.

Consider revising grid codes to reflect expectations for wind turbine capabilities.

• In the near term, these capabilities might include fault ride-through, provision of reactive power, and possibly AGC. In the longer term, capabilities might include inertial response and primary frequency response.

81 Parson, Brian et al., “Renewable Electricity Grid Integration Roadmap for Mexico: Supplement to the IEA Expert Group Report on Recommended Practices for Wind Integration” (National Renewable Energy Lab, December 2014) 44 IIE commented that CENACE has an informational database with historical • hourly demand forecasts and actual demand by control area. If possible, begin archiving sub-hourly data.

For integration studies that look 20-30 years in the future, consider including • demand scenarios beyond simply the uniform projection of historic load, such as those that account for electrification of cars, energy efficiency, or different growth rates.

Given concerns regarding the dispersed nature of the Mexican transmission • system, evaluate the need to use nodal representation for transmission analysis as an alternative to standard “copper plate” assumptions (which may ignore transmission limitations).

Expand load flow transmission scenario cases beyond the traditional worst-case • snapshot transmission cases to examine additional temporally coincident cases that screen for a variety of wind and solar power delivery and electric load combinations.

Use the developed wind and solar historic variability datasets for composite wind • and solar output (rather than simply the total nameplate output of all plants) to assess possible renewable curtailment due to transmission limitations.

IIE commented that existing CENACE datasets in its short-term planning model • contain fossil plant performance factors such as ramp rates, minimum generation levels, heat rates as a function of load, minimum up times and minimum down times. It is necessary to enhance combined cycle plant data related to dead zones, feasible transitions between configurations, and minimum and maximum time that plants must remain on a certain configuration.

CENACE databases contain detailed data of both hydroelectric plant models and • hydroelectric systems. It is, however, necessary to update some of these data in order to improve the results of the flexibility studies.

Mexico could consider developing data on demand response supply curves for • production cost models to evaluate as a potential mitigation measure to reduce integration costs, particularly if conventional plant flexibility is insufficient or wind and solar curtailment levels are excessive.

References Ackermann, T.; Ellis, A.; Fortmann, J.; Matevosyan, J.; Muljadi, E.; Piwko, R.; Pourbeik, P.; Quitmann, E.; Sorensen, P.; Urdal, H.; Zavadil, B. (2013). “Code Shift: Grid Specifications and Dynamic Wind Turbine Models.” Power and Energy Magazine, IEEE (11:6); pp. 72-82.

Accessed October 2014:

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6634510.

Bird, L.; Cochran, J.; Wang, X. (2014). “Wind and Solar Energy Curtailment: Experience and Practices in the United States.” NREL/TP-6A20-60983. Golden, CO: National

Renewable Energy Laboratory. Accessed October 2014:

http://www.nrel.gov/docs/fy14osti/60983.pdf.

45 Braun, M.; Stetz, T.; Brundlinger, R.; Mayr, C.; Hatta, H.; Kobayashi, H.; Ogimoto, K.;

Kroposki, B.; Mather, B.; Coddington, M.; Lynn, K.; Graditi, G.; Woyte, A.; MacGill, I.

(2011). “Is the Distribution Grid Ready to Accept Large Scale Photovoltaic Deployment?

- State of the Art, Progress and Future Prospects.” 26th European Photovoltaic Solar Energy Conference: Proceedings of the International Conference, 5-9 September 2011, Hamburg, Germany. Munich, Germany: WIP-Renewable Energies pp. 3840-3853;

NREL/CP-6A00-54390. Accessed September 2014:

http://dx.doi.org/10.4229/26thEUPVSEC2011-5DP.3.1.

Braun, M.; Stetz, T.; Brundlinger, R.; Mayr, C.; Ogimoto, K.; Hatta, H.; Kobayashi, H.;

Kroposki, B.; Mather, B.; Coddington, M.; Lynn, K.; Graditi, G.; Woyte, A.; MacGill, I.

(2012). “Is the Distribution Grid Ready to Accept Large-Scale Photovoltaic Deployment?

State of the Art, Progress, and Future Prospects.” Progress in Photovoltaics: Research and Applications Special Issue: 26th EU PVSEC, Hamburg, Germany, 2011. 20 (6); pp.

681-697; NREL/JA-6A00-57003. Accessed August 2014:

http://dx.doi.org/10.1002/pip.1204.

Cochran, J.; Bird, L.; Heeter, J.; Arent, D. J. (2012). Integrating Variable Renewable Energy in Electric Power Markets: Best Practices from International Experience.





NREL/TP-6A20-53732. Golden, CO: National Renewable Energy Laboratory.

Cochran, J.; Lew, D.; Kumar, N. (2013). “Flexible Coal: Evolution from Baseload to Peaking Plant.” NREL/BR-6A20-60575. Golden, CO: National Renewable Energy Laboratory. Accessed October 2014: www.nrel.gov/docs/fy14osti/60575.pdf.

Denholm, P.; Jorgenson, J.; Hummon, M.; Palchak, D.; Kirby, B.; Ma, O.; O'Malley, M.

(2013a). Impact of Wind and Solar on the Value of Energy Storage. NREL/TP-6A20Golden, CO: National Renewable Energy Laboratory.

Denholm, P.; Jorgenson, J.; Hummon, M.; Jenkin, T.; Palchak, D.; Kirby, B.; Ma, O.;

O'Malley, M. (2013b). Value of Energy Storage for Grid Applications. NREL/TP-6A20Golden, CO: National Renewable Energy Laboratory.

Denholm, P.; Wan, Y. H.; Hummon, M.; Mehos, M. (2014). “Value of CSP with Thermal Energy Storage in the Western United States.” Energy Procedia - Proceedings of the SolarPACES 2013 International Conference, 17-20 September 2013, Las Vegas,

Nevada. 1622-1631. NREL/CP-6A20-60027. Accessed October 2014:

http://dx.doi.org/10.1016/j.egypro.2014.03.171.

Hummon, M.; Weekley, A.; Searight, K.; Clark, K. (2013). Downscaling Solar Power Output to 4-Seconds for Use in Integration Studies: Preprint. NREL/CP-6A20-60335.

Accessed November 2014: http://www.nrel.gov/docs/fy14osti/60335.pdf.

Hummon, M.; Ibanez, E.; Brinkman, G.; Lew, D. (2012). Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis:Preprint. NREL/CP-6A20Accessed November 2014: http://www.nrel.gov/docs/fy13osti/56204.pdf.

–  –  –

Kumar, N.; Besuner, P.; Lefton, S.; Agan, D.; Hilleman, D. (2012). Power Plant Cycling Costs. NREL/SR-5500-55433. Golden, CO: National Renewable Energy Laboratory.

Accessed October 2014: www.nrel.gov/docs/fy12osti/55433.pdf.

Lew, D.; Brinkman, G.; Ibanez, E.; Florita, A.; Heaney, M.; Hodge, B-M.; Hummon, M.;

Stark, G.; King, J.; Lefton, A.; Kumar, N.; Agan, D.; Jordan, G.; Venkataraman, S.

(2013). The Western Wind and Solar Integration Study Phase 2. NREL/ TP-5500Accessed November 2014: http://www.nrel.gov/docs/fy13osti/55588.pdf.

North American Electric Reliability Corporation. (2010). “NERC IVGTF Task 2.1 Report:

Variable Generation Power Forecasting for Operations.” Princeton, NJ: NERC.

Accessed October 2014: http://variablegen.org/wpcontent/uploads/2013/01/Variable_Generation_Power_Forecasting_for_Operations.pdf.

Porter, K.; Rogers, J. (2010). “Status of Centralized Wind Power Forecasting in North America.” NREL/SR-550-47853. Golden, CO: National Renewable Energy Laboratory.

Accessed October 2014: www.nrel.gov/docs/fy10osti/47853.pdf.

“Wind Forecast Improvement Project (WFIP).” (undated). Washington, DC: NOAA.

Accessed September 2014: www.esrl.noaa.gov/psd/psd3/wfip/.

Mexico-specific Checklist: Portfolio Development and System Management Recommendations for Mexico on portfolio development and system management

include:

Adapt and exercise a capacity expansion planning tool specific to Mexico that • enables the exploration of potential wind and solar plant locations and captures the associated transmission costs to estimate the costs of meeting Mexico’s RE targets. ReEDS and RPM are specific examples of existing models developed by NREL that could be adapted to or expanded for Mexico’s use.

IIE commented :

“CFE has three models developed by the IIE, which could be used for this type of

studies:

1) Long-term planning of generation expansion model (PEGyT, utilized by the Under director’s office for Programming at CFE) which makes decisions about lowest total cost of investment, operation and externalities;

2) Multi-year stochastic dispatch model (utilized by the Market Operation Office at CENACE); and

3) Hydrothermal Coordination and Unit Commitment (CHT, utilized by the office of Market Operation at CENACE, previously described).” 47 In response to this comment, the Roadmap authors agree with IIE that, “The current models that CFE uses have some advantages over the NREL models. The CFE models use with certain sufficient details, the characteristics of the hydroelectric power plants, the storage capacity of the reservoirs, the configuration of the waterways, the spending restrictions in waterways, and the operating limitations of the reservoirs, and the uncertainty of the hydrologic runoffs, which for Mexico are relevant. Additionally, the three models currently utilized by CFE have the information of the various generation and transmission elements of the National Electric System, the storage network, and fuel transport system, already loaded and debugged.” The challenge is in assessing the most expedient course. Options include: 1) translating Mexico-specific electric grid data and practices into models that were developed specifically to account for the unique temporal, stochastic, and spatial characteristics of weather-driven wind and solar generation resources; or 2) adapting existing Mexico specific models that contain relevant existing system characteristics to capture the salient RE characteristics and concerns.

Past international experience shows base assumptions can have a large impact, possibly unintentionally. Areas we recommend be examined critically in existing Mexico

specific planning models include:

• Year-to year RE resource variations;

• Maximum potential ceilings for renewable energy, by year;

• Minimum generation from clean energy, by year; and

• Stochastic behavior of renewable energy.

Translating system specific data and practices into models that are specifically designed to evaluate systems with high penetrations of wind and solar is more expedient and accurate in the long run.

Checklist continued:

• Consider modeling at least two scenarios for oil-fired power plant replacements:

one where the highest efficiency gas plants dominate, and another that prioritizes gas plants designed to ramp quickly.

• Develop alternative wind and solar generation expansion scenarios to examine operational cost differences (e.g., centralized solar versus distributed generation, expanding transmission to remote energetic sites versus minimizing transmission expansion costs).

• Include operational reforms such as shifting to sub-hourly dispatch, integrating forecasting into unit commitment decisions, and instituting dynamic reserve practices into production cost modeling. Grid operational specialists from CENACE, grid planners from CFE, market reform and design specialists from SENER and CRE, and other relevant stakeholders can design the relevant scenarios.

• For higher rates of renewable energy (10%), if the production cost simulations suggest that reliability cannot be cost-effectively achieved based on the modified practices described above, consider additional sources of flexibility (e.g., demand control).

48 Use production cost modeling to quantify the effects of choosing different time steps for dispatch and market operations to inform the ISO’s development of operating rules and ancillary service markets for procuring reserves. Plexos commercial software, and, according to IIE, the existing IIE/CFE Hydrothermal Coordination model have this capability.

Due to Mexico’s relatively high anticipated annual electric load growth, give • special attention to defining scenarios for the future generation mix that take into consideration planned additions, retirements, and future flexibility capabilities.

The definition of scenarios is part of the current activities of the Subdireccion de Programacion para la elaboracion (Under Director of Programming in the elaboration of the Electric Sector Project and Investment Program – POISE).

Define and maintain a constant acceptable loss of load expectation due to • insufficient operational reserves across all modeling scenarios to ensure comparability of results.

Given the potential impact of reserve practices on operational costs, consider • examining the most up-to-date methodologies for dynamic reserve analyses.

References Cochran, J.; Bird, L.; Heeter, J.; Arent, D. A. (2012). Integrating Variable Renewable Energy in Electric Power Markets: Best Practices from International Experience.

NREL/TP-6A20-53732. Golden, CO: National Renewable Energy Laboratory. Accessed September 2014: www.nrel.gov/docs/fy12osti/53732.pdf.

Cochran, J.; Lew, D.; Kumar, N.(2013). “Flexible Coal: Evolution from Baseload to Peaking Plant.” NREL/BR-6A20-60575. Golden, CO: National Renewable Energy Laboratory. Accessed September 2014: www.nrel.gov/docs/fy14osti/60575.pdf.

Cochran, J.; Miller, M.; Milligan, M.; Ela, E.; Arent, D.; Bloom, A.; Futch, M.; Kiviluoma, J.; Holttinen, H.; Orths, A.; Gómez-Lázaro, E.; Martín-Martínez, S.; Kukoda, S.; Garcia,



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