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NERC | Report Title | Report Date I

A Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse White Paper

April 2017

NERC | A Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse | April 2017 ii

Table of Contents

Preface ....................................................................................................................................................................... iii

Introduction ............................................................................................................................................................... iv

Chapter 1: Background ...............................................................................................................................................1

Photovoltaic Generation Considerations ................................................................................................................2

A Review of 2015 European Solar Eclipse ...............................................................................................................2

Chapter 2: Problem Formulation ................................................................................................................................4

Chapter 3: Study Methods and Assumptions .............................................................................................................5

Data Collection ........................................................................................................................................................5

Hourly Load Selection .............................................................................................................................................6

Photovoltaic Generation .........................................................................................................................................6

Eclipse Bands ..........................................................................................................................................................7

Distribution System Photovoltaic Generation ....................................................................................................9

Chapter 4: Simulation Results ................................................................................................................................. 11

Test Cases ............................................................................................................................................................ 11

Results and Observations .................................................................................................................................... 11

Top 10 Areas with the Greatest Amount of Total System Photovoltaic (PV) Nameplate Capacity ................. 12

California .......................................................................................................................................................... 16

North Carolina ...................................................................................................................................................... 18

Chapter 5: Summary and Recommendations ......................................................................................................... 20

NERC Staff Contributors .......................................................................................................................................... 21

Appendix A: Simulation Set-Up ............................................................................................................................... 22

Appendix B: Distribution Photovoltaic Generation Eclipse Map ............................................................................. 23

Appendix C: State and Province Specific Forecasted Data ...................................................................................... 24

NERC | A Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse | April 2017 iii

Preface

The North American Electric Reliability Corporation (NERC) is a not-for-profit international regulatory authority whose mission is to assure the reliability and security of the bulk power system (BPS) in North America. NERC develops and enforces Reliability Standards; annually assesses seasonal and long‐term reliability; monitors the BPS through system awareness; and educates, trains, and certifies industry personnel. NERC’s area of responsibility spans the continental United States, Canada, and the northern portion of Baja California, Mexico. NERC is the Electric Reliability Organization (ERO) for North America, subject to oversight by the Federal Energy Regulatory Commission (FERC) and governmental authorities in Canada. NERC’s jurisdiction includes users, owners, and operators of the BPS, which serves more than 334 million people. The North American BPS is divided into eight Regional Entity (RE) boundaries as shown in the map and corresponding table below.

The North American BPS is divided into eight RE boundaries. The highlighted areas denote overlap as some load-serving entities participate in one Region while associated transmission owners/operators participate in another.

FRCC Florida Reliability Coordinating Council

MRO Midwest Reliability Organization

NPCC Northeast Power Coordinating Council

RF ReliabilityFirst

SERC SERC Reliability Corporation

SPP RE Southwest Power Pool Regional Entity

Texas RE Texas Reliability Entity

WECC Western Electricity Coordinating Council

NERC | A Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse | April 2017 iv

Introduction

NASA has predicted that the path of a total solar eclipse will directly affect North American bulk power system (BPS) operations on August 21, 2017.1, 2 The United States power generation resource mix has become more diverse. The diversity in generation is driven in part by national and state renewable portfolio standards as well as increased environmental regulations. Total solar capacity (distribution and transmission connected) in the United States has increased from 5 MW in 2000 to 42,619 MW in 2016. A total solar eclipse occurred across Continental Europe, Nordic Countries, and Great Britain in 2015. This solar eclipse showed, “… a great deviation in the amount of solar generation that was available before, during, and post eclipse which caused the need for far more advanced coordination of primary, secondary, and tertiary reserves across Europe within a reduced time frame (faster than 10–15 minute intervals).”3 This deviation in available solar generation indicates a need to study the potential effects that a solar eclipse would have on the North American BPS. This Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse document was created in order to evaluate potential reliability consequences of the total solar eclipse on the BPS. This assessment focuses specifically on impacts of system loading and potential reliability implications when an area experiences a large reduction of distributed energy resource (DER) capacity due to a total solar eclipse. Ramping is a specific concern for areas with large amounts of variable generation in their resource mix. The areas that have ramping issues will need further evaluation by the Regions and include areas that are not in the direct path of the eclipse. An example of such an area is California, where the transmission (utility) installed nameplate capacity for solar generation is 11,444 MW. Some areas in Northern California are projected to experience up to 95 percent of the obscuration of the Sun from the August 21, 2017 eclipse. In developing this white paper, NERC reviewed European assessment4 of the 2015 eclipse while developing this white paper and gleaned applicable lessons from European assessment. Additionally, the study produced results on an extreme case scenario basis (i.e., perfect weather conditions which allow for total obscuration of the Sun and a heavily loaded system). This white paper provides a macroscopic view of system loading conditions and provides recommendations to the affected regions. The main objectives performed in the assessment for this white paper are the following:

1. Develop a United States extreme peak load case for the August 21, 2017 eclipse.

2. Scenario eclipse test cases which include hourly load data and forecasted photovoltaic generation with a built in buffer (apply an interval or threshold range around the data and analysis).

3. Identify and assess the eclipse test cases for any potential system reliability and/or operational impacts in areas with high penetration of DER resources and with significant sunlight reduction due to the eclipse.

These objectives will assist in identifying the obscuration effects of the eclipse on the amount of solar power generated required to serve the forecasted hourly load. The NERC assessment results of the total eclipse showed no impacts to the of BPS operations. Specific states with large amount of photovoltaic resources are expected to experience an increase in load and possible ramping and balancing concerns. It is recommended that all states secure non-photovoltaic resources for system operation during the 2017 total eclipse.

1 NASA Solar Eclipse August 21, 2017 (2017). Retrieved February 6, 2017, from http://eclipse.gsfc.nasa.gov/solar.html 2 Total Solar Eclipse of 2017 August 21 (2017) Retrieved February 6, 2017, from http://eclipse.gsfc.nasa.gov/SEplot/SEplot2001/SE2017Aug21T.GIF 3 “Solar Eclipse March 2015: The Successful Stress Test of Europe’s Power Grid- More Ahead,” Prepared by European Network of Transmission System Operators for Electricity, July 15, 2015, Retrieved February 6, 2017 from http://www.entsoe.eu/Documents/Publications/ENTSO-E%20general%20publications/entsoe_spe_pp_solar_eclipse_2015_web.pdf 4 “Solar Eclipse 2015 Impact Analysis,” Prepared by Regional Group Continental Europe and Synchronous Area Great Britain, February 19, 2015, (2017, February 6) Retrieved from https://www.entsoe.eu/Documents/Publications/SOC/150219_Solar_Eclipse_Impact_Analysis_Final.pdf

http://eclipse.gsfc.nasa.gov/solar.html

http://eclipse.gsfc.nasa.gov/SEplot/SEplot2001/SE2017Aug21T.GIF

http://www.entsoe.eu/Documents/Publications/ENTSO-E%20general%20publications/entsoe_spe_pp_solar_eclipse_2015_web.pdf

https://www.entsoe.eu/Documents/Publications/SOC/150219_Solar_Eclipse_Impact_Analysis_Final.pdf

NERC | A Wide-Area Perspective on the August 21, 2017 Total Solar Eclipse | April 2017 1

1:Chapter 1: Background

In the next seven years, two total solar eclipse trajectories will traverse across North America. Figure 1.1 shows the path of the upcoming North American total solar eclipse of 2017 and the total solar eclipse of 2024. Both eclipse routes (path of the moon occulting the sun) move from the west to the east across North America. The map below shows that the August 21, 2017 eclipse proceeds across the United States in southerly movement with first and last total eclipse observations occurring in Oregon and South Carolina respectively. The April 8, 2024, eclipse advances northerly; the total eclipse will be first viewable in Sinaloa, Mexico and lastly visible in Newfoundland and Labrador, Canada.

Figure 1.1: Solar Map—Projected Trajectory of the 2017 and 2024 Total Solar Eclipses5

The total solar eclipse of August 21, 2017, is the first eclipse in the United States in twenty-six years and is the topic of this white paper. The eclipse will first be observable in Oregon at 10:15 a.m. (Pacific). The southern part of the state of Illinois will observe the eclipse for the longest duration of time, a total of two minutes and forty seconds at approximately 1:19 p.m. (Central). The eclipse will be last observed in the state of South Carolina at 2:49 p.m. (Eastern). The duration of the total eclipse across the United States is a short period of time; it takes roughly one hour and thirty-three minutes to traverse the country. However, a large amount of photovoltaic resources during the eclipse will be removed due to the obscuration of the Sun. Utilities are expected to experience a sudden (less than 5 minutes) increase in load that was previously being supplied by behind-the-meter photovoltaic generators. This increase in load may cause local ramping and balancing concerns which should be investigated by each area’s respective utility. In this white paper, the focus is on the peak load time period and investigates the eclipse with respect to effects on the BPS. In the next section of this chapter, study considerations for photovoltaic generation are provided.

5 Jubier, Xavier, Total Solar Eclipse on Monday, August 21, 2017 and April 8, 2024 in the United States of America, (2017) Retrieved

February 6, 2017, from http://xjubier.free.fr/en/site_pages/solar_eclipses/TSE_2017_2024_GoogleMapFull.html

http://xjubier.free.fr/en/site_pages/solar_eclipses/TSE_2017_2024_GoogleMapFull.html

Chapter 1: Background


Photovoltaic Generation Considerations The total power generated by a photovoltaic generator varies by the technology of the solar generator, the age of the hardware components, the location of the unit, and the total solar plant size. A solar plant (or solar farm) consists of many individual solar panels connected together to create a larger solar generator. Many factors determine the size of a solar plant, and the predominant factors that limit the size of a solar plant are the type of electric customer and the physical amount of available space. All solar generator sizes were included in the study that went into this white paper; classification is made by customer type as discussed in the next paragraph. The customer type for the purpose of this white paper will consist of three categories: utility transmission system customers, non-residential distribution customers, and residential distribution customers.

Utility customers are connected to the transmission system and may be supplied and/or may generate three-phase balanced power. Balanced power is defined as the power generated or consumed is equal across the phases at the point of electrical connection (node).

Non-residential distribution customers consist of distribution utilities as well as industrial and commercial customers on the distribution system. Non-residential distribution customers may be supplied and/or may generate three-phase balanced power or two-phase power.

Residential customers in the United States are supplied single-phase power at the point of electrical connection and thus they may generate single-phase power if a solar plant is connected to their location (e.g., home, business, or farm).

All customer types are considered to produce and/or consume only the number of phases provided at their respective node. The level of control and visibility of the solar generators in the power system decreases as the voltage decreases from the transmission to distribution systems. This is similar to the number of phases which decreases along the power system. In the next section of this chapter, a review of the impacts from the 2015 European solar eclipse study are provided.

A Review of 2015 European Solar Eclipse The European Network of Transmission System Operators for Electricity (ENTSO-E) community produced an analysis6 to assess the operational impacts from the 2015 total solar eclipse and developed a plan to mitigate these impacts. This analysis included data estimations and required coordinated communication for all of continental Europe. While solar eclipses are typical events, this specific event marked the first occurrence of a near-total solar eclipse to introduce such a wide-area impact by passing over a substantial 90 GW of photovoltaic (PV) generation. The analysis performed required an estimation of the total installed PV capacity by the date of the eclipse, an estimation of the energy from PV that would be produced across the hours of the eclipse, and an estimation for the demand. ENTSO-E estimated the amount of solar energy that would be produced by examining capacity and coincidence factors by country inside and outside of the solar eclipse hours. The total calculated reduction of PV for the entire area, as compared to what is expected during “clear sky” conditions, is shown in Figure 1.2. The anticipated high impact of solar reduction of 34 GW would be observed at 09:41 (UTC) and distributed across the entire footprint.

6 “Solar Eclipse March 2015: The Successful Stress Test of Europe’s Power Grid- More Ahead,” Prepared by European Network of

Transmission System Operators for Electricity, July 15, 2015, Retrieved February 6, 2017 from http://www.entsoe.eu/Documents/Publications/ENTSO-E%20general%20publications/entsoe_spe_pp_solar_eclipse_2015_web.pdf


Chapter 1: Background


Figure 1.2: Comparison of expected infeed from solar on March 20 during clear sky conditions with and without solar eclipse7, 8

The report proposed multiple recommendations that are grouped as steps to be taking as individual transmission system operators (TSO) or by the whole of continental Europe for synchronous area coordination. Specific considerations were to be made for each of the TSOs due to their variances in PV capacity, associated regulatory bodies, and the applicability of different measures for preparation. These preparatory measures are outlined in more detail within the report. The weather conditions in part of the footprint on the day of the 2015 event were cloudier than was originally forecasted which led to a less severe reduction in PV generation while other areas had clear skies and experienced a high impact. However, with the outlined preparatory measures taken and ample coordinated planning performed, there were no observed issues caused by the 2015 near-total solar eclipse in continental Europe. The main lessons learned for system operations are summarized as follows:5

Develop a plan to disconnect part of the installed utility-scale PV generation in advance of the eclipse.

Determine the exact amount of PV to be switched off (disconnected).

Coordinate and communicate the time for disconnecting and reconnecting to the grid.

Detail the steps to be taken to reconnect PV systems to the grid.

Acquire backup generation and/or transfers to fulfill load in absence of PV generation.

A clear description of the installed PV capacity and their capabilities is needed for the accuracy of forecast studies (e.g., technical data, retrofitting campaign, disconnection/reconnection settings and logics, etc.).

Real-time measurement of the dispersed PV generation is the key for adapting the operational strategy in real-time.

In the next chapter the problem formulation for the North American August 21, 2017 total solar eclipse study is

presented.

7 “Solar Eclipse 2015 Impact Analysis,” Prepared by Regional Group Continental Europe and Synchronous Area Great Britain, February 19, 2015, (2017, February 6) Retrieved from https://www.entsoe.eu/Documents/Publications/SOC/150219_Solar_Eclipse_Impact_Analysis_Final.pdf 8 “Solar Eclipse March 2015: The Successful Stress Test of Europe’s Power Grid- More Ahead,” Prepared by European Network of Transmission System Operators for Electricity, July 15, 2015, Retrieved February 6, 2017 from http://www.entsoe.eu/Documents/Publications/ENTSO-E%20general%20publications/entsoe_spe_pp_solar_eclipse_2015_web.pdf

https://www.entsoe.eu/Documents/Publications/SOC/150219_Solar_Eclipse_Impact_Analysis_Final.pdf



2:Chapter 2: Problem Formulation

The North America electric power system consists of many individual network components and their respective physical constraints. In order to limit the size of the problem and to examine the system as a whole, the problem was reduced to the basic conservation of power equality constraint. In this white paper, the conservation of power law is employed to monitor the change in generation and load of the North America wide-area power system. The North America wide-area power system is composed of the lower forty-eight continental states of the United States; the eight provinces of Canada; and Baja California, Mexico. In the analysis performed for the white paper, the transmission losses are neglected, and the operating limits of individual generating units are not explicitly stated. The search space for the problem is limited to the set of real numbers. The problem for each area under study is stated in Equation 1:

𝑷𝒈𝒆𝒏 = 𝑷𝒍𝒐𝒂𝒅 − 𝑷𝒔𝒐𝒍𝒂𝒓 Equation 1

where, alphabetically: 𝑃𝑔𝑒𝑛 ∶ total real power (MW) generated in a study area 𝑃𝑙𝑜𝑎𝑑: total real power (MW) demand/load in a study area 𝑃𝑠𝑜𝑙𝑎𝑟: total real power(MW) generated by photovoltaic generators in a study area The primary goal of this white paper is to identify the obscuration effects of the eclipse on the amount of solar generation required to serve the forecasted hourly load. Screening tools that address coordination, ramping, and on on-line reserves for eclipse scenarios have yet to be created by industry. Therefore, this analysis provides a screening method (tool) to identify states and provinces with potential issues due to the eclipse and the result of the sudden loss of solar generation in an area. The current version of the tool does not provide plans for coordination, ramping, or on-line reserve sharing. However, the method may be enhanced in the future to create a more sophisticated tool that could be applied to the April 8, 2024 total eclipse of North and Central America. This analysis will be used to highlight states and provinces that would require further investigation of the eclipse on their respective areas and possible coordination with their neighboring transmission and distribution companies. This white paper also aims to inform policy makers, government, and industry on the key effects of the eclipse on the electric power system for the August 2017 solar eclipse. In the next chapter the assumptions for the problem set-up are provided.


3:Chapter 3: Study Methods and Assumptions

This white paper focuses on examining a large loss of solar power generation on the BPS that will occur during the upcoming total solar eclipse on August 21, 2017. The load and photovoltaic generation for North America was separated into their respective states and provincial boundaries. Engineering reasoning was applied generally to restrict the amount of data collected and selected for study. The following subsections present the methods used and assumptions applied in order to create the extreme case scenario for the eclipse study.

Data Collection

Hourly Load Selection

Photovoltaic Generators

Eclipse Magnitude Bands In all subsections of this white paper, the transmission system considerations are provided before any applicable distribution system considerations. In the next section, data collection and assumptions are addressed.

Data Collection In this section, the data collection sources and data assumptions applied to the problem formulation are presented. NERC relied on two sources for transmission and distribution data collection. The primary source for transmission system data retrieval was ABB’s Velocity Suite database9. ABB’s database provided provincial and state specific hourly load estimates and generating unit capacity. The secondary data source was used to obtain 2017 forecasted distribution system photovoltaic data. The source is SEIA & GTM Research’s US Solar Market Insight Q310 2016 report, which provided U.S. state total distribution system photovoltaic (PV) installation capacity. State-specific load and generator data was captured by querying ABB’s Velocity Suite database. ABB’s lists FERC Form 714 Part III Schedule 2, EIA 861, and ABB research as the source for the estimated state hourly load data. Specifically, the estimated hourly load by state from ABB was determined by, “… splitting out the ABB hourly load by Transmission Zone to states based on retails sales data from EIA Form 861 and ABB primary research.” Furthermore, the ABB Velocity Suite database was also mined to acquire the utility-scale generating unit capacity for photovoltaic and non-photovoltaic generators. ABB amalgamated the existing and future generation dataset through multiple sources (e.g., EIA 860, NERC ES&D, CFE, StatsCanada, CEMS, U.S. Federal and State Agencies, ISOs, Unit Owner and/or Operator Websites, and ABB Primary Research). Therefore, the data in this white paper includes updates for utility generator data estimates which may differ from the data provided in the 2016 LTRA.11 Lastly, the data collected from the GTM report pertains only to U.S. state specific distribution system installations and projected forecasts. The annual cumulative distributed generation installations by state table for residential photovoltaic (PV) installations and non-residential PV installations10 was utilized to determine the 2017 total state projected direct-current capacity installations. All individual distribution system photovoltaic systems were assumed to have a total capacity of less than 10 MW. In order to convert the direct-current MW values to alternating current MW values, a derate factor was applied. The total state reported direct-current MW values was multiplied by 0.87 to obtain the alternating-current MW value. The source for this 0.87 MW derate factor is

9 Velocity Suite. (2017). Retrieved February 06, 2017, from http://new.abb.com/enterprise-software/energy-portfolio-management/market-intelligence-services/velocity-suite 10 GTM US Solar Market Insight Report Q3 2016 11 NERC 2016 LTRA Report

http://new.abb.com/enterprise-software/energy-portfolio-management/market-intelligence-services/velocity-suite


http://www.seia.org/research-resources/solar-market-insight-report-2016-q3

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2016%20Long-Term%20Reliability%20Assessment.pdf

Chapter 3: Study Methods and Assumptions


GTM research.12 A derate factor specific to each state and unit could be determined and applied, but since the focus of the study is on wide-area analysis, individual derate factors were deemed outside the scope of work for the project. Once the data was collected, the main assumptions for the study were constructed as follows:

All hourly load data is expected to be the maximum load occurring during the hour on the transmission system. Distribution system load is believed to be already incorporated into the data.

With the exception of the Ontario province, the Canadian distribution system photovoltaic system data was not considered for the study because the eclipse path will predominantly affect the United States.

An August 1, 2017, in-service date was selected as a cut-off for all generating unit data. Therefore, any unit sited to be in-service prior to this date was counted toward the state’s total generating capacity. Any generating unit status listed as canceled, retired, mothballed, or postponed was eliminated from the generating data.

As previously stated in the Chapter 2: Problem Formulation formulation section of this white paper, the system under study is assumed to be lossless. In addition to ignoring transmission line losses, power transfers between Balancing Authorities were ignored, and available resources within each state are perceived to be adequate to balance the loss of photovoltaic generation due to the eclipse.

Using the assumptions above, an extreme case scenario for all study areas was created. It is noted that given other future eclipse scenarios, the assumptions and data collection sources presented in this white paper may not be applicable; a new study will need to be conducted. However, for the current situation, the presented strategy provides a macroscopic view of the upcoming astronomical event. In the next section of this white paper, the method used to select the hourly load on August 21, 2017 is presented.

Hourly Load Selection Load follows the pattern of human behavior, and its shape is dependent on the season of the year, the day of the week, and the hour of the day. The solar eclipse will occur on Monday, August 17, 2017. The total eclipse will begin to be first observed in totality in the state of Oregon at approximately 10:15 a.m. (Pacific) and 1:15 p.m. (Eastern) and will be last observed in totality in South Carolina at roughly 2:45 p.m. (Eastern). Attention to the month and day of the week of the eclipse are important aspects for estimating the extreme case hourly load. Therefore, hourly reported load for Monday’s in August over the years 2013 to 2015 were collected as the set of possible hourly data points. From the set of collected data, the maximum reported hourly load was selected as the worst case load setting to be studied. Furthermore, the time of day when the eclipse occurs significantly impacts the amount of solar generation that will become unavailable in the system. The upcoming total eclipse does not occur during a peak load period for any of the study areas. Regardless of the actual eclipse time, the hours 9:00 a.m. to 5:00 p.m. were chosen for this study in order to evaluate the impact of the time of the eclipse throughout a typical work day. In the next section, of this white paper considerations applied to photovoltaic (PV) generation for the assessment that was performed are discussed.

Photovoltaic Generation Utility scale photovoltaic generation installed on the transmission system differs from distribution system photovoltaic systems not only by magnitude/size but also by the type of technology employed (e.g. panels, inverters, maximum power point tracking). In this white paper, photovoltaic technology is limited to facilities that solely convert sunlight into electricity; thermal systems and supplemental or backup energy sources (e.g., storage technology) are not included in this analysis.

12 U.S. Solar Market Insight. “Q3 2016 U.S. SOLAR MARKET INSIGHT” (2017). Retrieved February 06, 2017, from http://www.seia.org/research-resources/us-solar-market-insight

http://www.seia.org/research-resources/us-solar-market-insight



Additionally, it is important to recognize that variation exists in hardware components due to technological advancements. These improvements have resulted in an increase in efficiency pertaining to the total real power output of a system. Generating units that were installed in the past five to ten years are more efficient than those installed ten to fifteen years ago. In this white paper, historical and estimated generating data based on total facility size was procured without further refining an individual unit’s specific technology and respective efficiency. Although technological diversity is present in the grid is known, it is assumed that the overall aggregated capacity of a facility is the most significant factor for the study. The central goal of this whitepaper is to develop an extreme case wide-area scenario in order to examine the consequences of losing a large amount of photovoltaic generation at any given hour during the day. In order to create the extrema all photovoltaic power generation facilities are scaled uniformly within the assessment area. The percentage to scale the generation is approximated by looking at August 2015 hourly daily generation curves reported by CA ISO.13 From the reported data, three hourly generation percentages were chosen to describe the general shape of MW production. The three percentages are shown in Figure 3.1 as follows, 40 percent for morning hours 9:00 a.m. through 11:00 a.m., 80 percent for noon. through 3:00 p.m. and 50 percent for 4:00 p.m. through and 5:00 p.m. Other hourly generation curves could be applied when necessary. Additional granularity could be achieved by scaling each individual hour to a specific percentage of MW production oy by scaling based on a shorter time frame.

Figure 3.1: Photovoltaic (PV) generator production hourly percentage scale

The amount of generation in a network that is required to maintain reliability is guided by the amount of load drawn by the system. The amount of photovoltaic generation that is produced is dependent on weather conditions and is independent of the amount of load that is drawn from a network. As areas in the BPS become more diverse and the amount of renewables increase, it is important to include evaluations that assess the risks associated with levels of dependency on intermittent resources. The next section of the white paper focuses on eclipse bands, which maps the geographic location of the PV facilities to the eclipse obscuration magnitudes.

Eclipse Bands As the Earth rotates during the day, it also revolves around the Sun. Likewise, the observed magnitude of the solar eclipse path is a function of the season, geographic location, and time of day. The path of totality is the trajectory

13 CA ISO - Today's Outlook. (2017). Retrieved February 06, 2017, from http://www.caiso.com/outlook/outlook.html

http://www.caiso.com/outlook/outlook.html



of the Moon’s shadow across the Earth, and those geographic locations along the path will experience complete obscuration of the Sun. Locations north and south of the path of totality will observe partial phases of the eclipse, and the level of obscuration of the Sun can be mapped to specific geographical latitudes and longitudes. In this white paper, obscuration eclipse bands (eclipse levels) were created by mapping 5 percent increments of eclipse obscurations to Google Earth maps of North America. A layer containing the eclipse bands was created in Velocity Suite14 so that photovoltaic generators’ physical locations could be assigned to their corresponding eclipse band. Figure 3.2 is a map of United States with direct normal irradiance shaded. The eclipse band layers are marked on the map by a series of parallel lines, and the locations of utility scale photovoltaic systems are shown by white, gray, and black circles.

Figure 3.2: United States map showing direct normal irradiance, eclipse bands and

locations of transmission photovoltaic generators

In Figure 3.2, the observed gap between the eclipse bands is the path of totality band of the eclipse, which includes the center line and the northern and southern limits of total obscuration. To avoid confusion, the center line of the eclipse is not explicitly drawn within the band. In the map, the two states with visible concentrations of utility solar were identified as California and North Carolina. The data in this whitepaper includes updates from EIA-860 generator owner forms which includes updated generation estimates and may differ from the data provided in the 2016 LTRA. Both California and North Carolina will be explicitly discussed in the Results and Observations section of this white paper.

14 Velocity Suite. (2017). Retrieved February 06, 2017, from http://new.abb.com/enterprise-software/energy-portfolio-management/market-intelligence-services/velocity-suite





The eclipse bands provide an obscuration range from which the photovoltaic units are mapped and scaled. Once the photovoltaic units are mapped, the eclipse band’s highest and lowest value in the range are assigned to the unit for scaling. The unit’s generating output will be decreased by the upper and lower band values during the eclipse. Further refinement of scaling could be made by reducing the obscuration increment step size. However, since the goal is to monitor the aggregated loss of solar generation on the BPS, high and low scalar values were selected for this study. State specific total installed distribution system photovoltaic generator capacity was provided in the GTM report for the United States.15 However, the location of these individual photovoltaic systems was not provided. Since location data was not available, two additional assumptions were applied to all distribution level photovoltaic data:

Distribution system PV data from the GTM report was categorized as either residential customers or non-residential customers.

The total PV was obtained from aggregating the residential MW and non-residential MW. The assumptions used to create an extreme case which results in a high distribution load are as follows:

The highest fraction of obscuration established in each state, by the eclipse bands, was used to reduce the total capacity generated by the distribution photovoltaic systems.

For states that did not have transmission system photovoltaic generators, the total distribution system capacity was decreased to zero MW.

Distribution System Photovoltaic Generation State-specific total installed distribution system photovoltaic generator (PV) capacity was provided in the GTM report for the United States.14 The distribution system total PV was reduced by using the highest fraction of eclipse coverage determined by the obscuration bands for each state. In Appendix B: Distribution Photovoltaic Generation Eclipse Map, a shaded map is provided to illustrate the linear degree of homogeneity reduction that was applied to each state’s distribution PV data. States with a degree of one will experience total obscuration at some locations within the state, and since the locations of the distribution PV are not explicitly provided, the highest fraction is applied to all DER in the state. For future analysis, the transmission to distribution system coupling must be refined to include distributed energy resources at the minimum electrical distance to a transmission substation to enhance planning, operations, and coordination. Figure 3.3 shows the top five states with distribution PV. The chart provides the total amount of distribution PV, the total residential sector (roof-top solar systems with nameplate capacity less than 10 kW) MW, and the anticipated total amount of distribution PV reduced by the total solar eclipse. From Figure 3.3, California visibly has the largest amount of distribution PV in comparison to other states. California’s residential sector accounts for 62 percent of the 7,250 MW in its distribution system. Of the top five states shown, Arizona and New York have the greatest contributions to the total distribution PV produced from their residential sector (rooftop PV). Their residential sectors account for 70 percent and 68 percent of the total distribution PV MW, respectively. It is noted that the residential sector PV capacity is not controlled by the distribution system or a transmission system; it is currently viewed as a load modifier. For most systems, the distribution residential rooftop systems are not observable; this will pose problems in the future if the level of renewable generation continues to grow because it will become increasingly difficult to model, plan, and operate the system without explicit knowledge of the

15 U.S. Solar Market Insight. “Q3 2016 U.S. SOLAR MARKET INSIGHT” (2017). Retrieved February 06, 2017, from http://www.seia.org/research-resources/us-solar-market-insight

http://www.seia.org/research-resources/us-solar-market-insight



rooftop solar locations and hourly MW outputs. For the purpose of this study, the distribution solar systems are not at a level of penetration (in comparison to transmission solar) where the effects from the eclipse will greatly affect transmission system operations or transmission system ramping. Hence, the assumptions used in the report are approximations to include distribution solar and create a comprehensive view of the transmission system. Distribution system operations should be studied separately in preparation for the eclipse. Appendix C: State and Province Specific Forecasted Data contains a detailed list of states and their respective distribution system total nameplate capacity.

Figure 3.3: Top five states with distribution system Photovoltaic (PV) generation—Total

nameplate capacity MW, total residential sector PV MW and total MW distribution PV reduced by eclipse shown

The next section of the report focuses on the simulation that was performed to study the substantial loss of photovoltaic generation due to the eclipse, detailed results are provided in the section.


4:Chapter 4: Simulation Results

In this chapter, the simulation results for the photovoltaic generation analysis using the law of conservation of power is presented. In the following sections, the, test cases, and results and observations are given. Subsequently, the impacts of the eclipse on the bulk power system (BPS) and potential reliability implications are discussed. A description of the test case components used for the simulation results is presented in the next section.

Test Cases The data collected for generation and load across North America was used to create extreme test cases. For each state or province, load and photovoltaic generator data from both transmission and distribution systems for nine hours were examined. A total of 522 scenarios were created for the study. Each individual scenario was composed of the following components:

Forecasted hour ending August 21, 2017, load in MW

Area specific total transmission system nameplate capacity of photovoltaic generation reduced by the time of day and by both high and low eclipse bands (two MW values)

When applicable, the area specific total distribution system nameplate capacity of photovoltaic generation decreased by the time of day and highest fraction of obscuration due to the eclipse

All results are compared to the respective area’s hourly base case data. The base case scenario is the solution to the power equality constraint, excluding the effects of the eclipse. The base case is considered the business-as-usual scenario: a typical sunny day without weather concerns such as cloud coverage, extreme temperatures, or precipitation. The base case for each hour is created by subtracting the total photovoltaic generation MW from the forecasted load in MW. The total photovoltaic generation is the sum of the total distribution system nameplate capacity and the total transmission system nameplate capacity; then that sum is multiplied by the hourly generation time-of-day percentage scale. For example, the forecasted hourly load at 9:00 a.m. is 1,000 megawatts for an area. The area’s total transmission photovoltaic nameplate capacity is 100 megawatts, and the area’s total distribution photovoltaic nameplate capacity is 10 megawatts. The photovoltaic generation scale percentage at 9:00 a.m. is 40 percent. Thus, the total photovoltaic generation is (100+10)*0.4 = 44 MW. Therefore, the base case data for the area is 1,000 MW – 44MW = 956 MW. The eclipse scenarios (low and high band) for 9:00 a.m. would then be compared to this base case data point. With the test cases set-up, the conservation of power problem is readily solved and the findings are provided in the next section of this chapter.

Results and Observations Ramping issues on the BPS due to changes in renewable energy output are most problematic during peak load conditions. Therefore, the results in this whitepaper focus on the forecasted non-coincident peak hourly load for every state or province. Localized effects should be studied in detail by transmission system and distribution system planners and operators. In Appendix C: State and Province Specific Forecasted Data, there is a table of hourly forecasted loads provided for every state and province under study. In the next subsection, data and results for 10 states with the greatest amount of total PV nameplate capacities are provided.

Chapter 4: Simulation Results


Top 10 Areas with the Greatest Amount of Total System Photovoltaic (PV) Nameplate Capacity In order to identify states or provinces that could experience reliability issues (e.g., change to resource mix in short period of time, ramping concerns, additional imports to serve load), the top 10 states or provinces with the greatest amount of total system PV nameplate capacities were plotted as a screening tool. Figure 4.1 is a comparison between total system solar generator nameplate capacities in the noted states to the forecasted non-coincident peak load of the states. The total PV is defined as the transmission system (utility) PV plus distribution system PV. The term total PV is defined as the total nameplate capacity in the system. The total PV reported in Figure 4.1 does not have the PV generator production hourly percentage applied.

Figure 4.1: Top ten states and provinces with photovoltaic nameplate capacities versus the

respective August 21, 2017, forecasted hour ending non-coincident peak load (Hourly time-of-day production scaling is not applied to PV generation)

Figure 4.1 makes it apparent that the top four states that may require advanced system coordination for operations prior to the eclipse, during the eclipse, and after the eclipse are Utah, California, Nevada, and North Carolina. In some parts of each of these states, a 0.9 obscuration will be observed (Utah has a maximum obscuration of 0.95, Nevada and North Carolina will experience a total eclipse). At a glance, the proportion of the nameplate PV to the forecasted peak load indicates that Utah has 39 percent of its generation resource mix by PV generation (capacity). Comparable statements about Figure 4.1 can be made with respect to Nevada, which has PV generation as 24 percent of its generation resource mix (capacity). Looking



only at nameplate capacity to modify (reduce) the load would indicate a great vulnerability to the eclipse and ramping concerns. Using the nameplate capacity and peak load conditions simulates a straightforward screening scenario that could be used in future eclipse analysis for a specific hour and load of a day to identify areas with possible ramp concerns. In the assessment for the white paper, the peak load is forecasted for Utah at the ending hour of 3:00 p.m. (Mountain) and Nevada at 5:00 p.m. (Pacific). The peak load for Utah may appear to be a data anomaly; however, the data was verified and the peak was selected from the maximum reported hourly load based on three years of historic load data for Monday’s in August. Provided that the eclipse occurred at the projected peak period, then the ramp rate for a five-minute interval would be difficult to control without standby energy storage systems. If no energy storage devices exist in a network, then the recommendation would be similar to lessons learned from Germany’s operation of the 2015 eclipse16 in which utility photovoltaic generators were disconnected from the grid in advance of the eclipse event. However, the ramping and balancing issue is not a concern because the actual time of the eclipse in Utah is 10:13 a.m. (Mountain) and Nevada is 10:09 a.m. (Pacific). At this time in the morning, the PV systems would not be at maximum power output, and the customer load is not at its peak. Utah’s peak load in comparison to the magnitude of the projected peak load for California differs by 166.2 percent. The amount of load to be served during the projected peak hour is a major factor for system operators during the eclipse; it is more difficult to make changes to the amount and type of generation online in order to compensate for ramping and to balance the network when it is heavily loaded. Similar statements and comparisons about the load size can be stated for North Carolina which experiences total obscuration (100 percent) in some parts of the state. Therefore, California and North Carolina are identified as states that could require advanced planning and operating coordination for the August 21, 2017 eclipse; a more in-depth analysis will be provided in later sections regarding these two states. Next, the photovoltaic generation time-of-day hourly scale was applied to the data to create the base case. The hourly scale was also applied to create the low and high band eclipse scenarios which are performed on the non-coincident forecasted peak load.

Forecasted Peak Load Scenario Results for Top 10 Areas with Greatest PV Nameplate Capacities In order to analyze the effect of the eclipse on the change in the forecasted peak load the base case, low-band PV generation and high-band PV generation scenarios were performed and post-processed to produce the results in Figure 4.2. It is a fact that typical peak performance of photovoltaic generators occurs before peak load conditions; however, the analysis in this white paper is aligned to coincide with the loss of PV generation during the peak load time period which is the primary time period of concern for BPS planning and operations. Therefore, the next stage of the analysis is to further investigate the states identified by the screening tool.

16 “Solar Eclipse March 2015: The Successful Stress Test of Europe’s Power Grid- More Ahead,” Prepared by European Network of Transmission System Operators for Electricity, July 15, 2015, Retrieved February 6, 2017 from http://www.entsoe.eu/Documents/Publications/ENTSO-E%20general%20publications/entsoe_spe_pp_solar_eclipse_2015_web.pdf




Figure 4.2: MW difference in non-coincident peak load using the total PV generated for the

base case, the low band PV and the high band PV generation scenarios for the top ten states and provinces with photovoltaic nameplate capacities

Figure 4.2 shows the MW change (difference) in the non-coincident peak load of the base case and the MW change in peak load for the low and high eclipse bands. The forecasted non-coincident peak demand is plotted on the secondary axis. The base case difference for each state’s peak hour is created by subtracting the total photovoltaic generation MW from the forecasted peak load in MW. Likewise, the same procedure was followed for the low and high eclipse band scenarios. The results are displayed in decreasing order of peak demand. A legend table below the chart provides the reported difference in load and the total forecasted load. In Figure 4.2, the expected MW change in the amount of photovoltaic generation reduced may appear to be severe. The top four states with photovoltaic nameplate capacities (i.e., Utah, Nevada, North Carolina, and California) are anticipated to produce photovoltaic generation that coincides with their respective forecasted peak load. For these states, the percent change in load was calculated. The percent change (%Δ) in peak load was calculated by taking the provided difference and dividing by the forecasted peak load and then multiplying by 100 percent. Utah is the state identified by the screening tool with the greatest total photovoltaic (PV) nameplate capacity in comparison to its projected load. Utah’s projected total nameplate PV capacity is 1,847 MW In Figure 4.2, Utah’s nameplate percent change in load appears to be the most severe; the photovoltaic generation modifies the total load in Utah by 27.92 percent. When the eclipse occurs, the state load is expected to increase by approximately 24 percent for the high band eclipse scenario and 22 percent for the low band eclipse scenario. The portion of the 4,727 megawatt load projected for the peak would then need to be served by other resources. This is not a major concern as there are sufficient synchronous resources in Utah to serve its respective load. Specifically, it is expected that Utah will have 8,787 megawatts (nameplate) of non-photovoltaic resources installed and operational by August 1, 2017. Therefore, the modification in load due to the eclipse is not significant enough to warrant further analysis from a system-wide perspective and, comparable statements about Nevada could be



provided. Instead, the in-depth focus is on California and North Carolina and each state’s respective hour ending results will be provided in the next section. The time for the results has been shifted to military time.



California

Figure 4.3: California projected PV

generation for high band PV and low band PV scenarios in comparison to the total

installed nameplate capacity

Figure 4.4: California remaining ending

hour load (MW) to be supplied by Non-PV resources for the base case, low band PV

and high band PV scenarios

Table 4.1: California forecasted ending hour load and the remaining ending hour load (MW) to be supplied by Non-PV resources for the base case, low band PV, and high band

PV scenarios

Hour Ending 9 10 11 12 13 14 15 16* 17

Forecasted Load (MW) 39,293 41,003 43,148 45,064 46,933 48,816 50,346 51,233 50,969

Base case (MW) (Load – Total PV)

34,426 36,135 38,280 35,329 37,198 39,081 40,611 45,148 44,885

Low Band PV Scenario (MW) (Load – Low Band Eclipse PV)

37,349 39,058 41,203 41,175 43,044 44,927 46,457 48,802 48,539

High Band PV Scenario (MW) (Load – High Band Eclipse

PV) 37,572 39,282 41,427 41,622 43,491 45,374 46,904 49,082 48,818

Note: Time has been converted to military time and the forecasted peak ending hour has an asterisk (*) next to the time of day when the peak’s occurs. Figure 4.3 shows California’s total photovoltaic nameplate capacity in MW and the forecasted reduced photovoltaic capacity for the high band and low band eclipse scenarios. These MW values exclude additional hourly generation production scaling. The low band eclipse scenario yields 64 percent reduction in transmission system nameplate capacity, and the high band eclipse scenario gives a 69 percent reduction in transmission nameplate capacity. For both scenarios, there is a 90 percent reduction in distribution system nameplate capacity. In Figure 4.4, the results from applying the production hourly generation scale to the PV generator scenarios and base case is provided. The chart shows the remaining ending hour load in MW that must be supplied by non-photovoltaic resources. Table 4.1 lists the forecasted ending hour load and the remaining MW load to be served by non-photovoltaic resources for each scenario and the base case. The peak forecasted load is 51,233 MW and occurs at hour 16 (4 p.m.). At the peak hour, the PV generated for the base case decreased (modified) the load by 19 percent, and when the eclipse occurs the PV generated will be reduced and increase the load in the range of 7 to 8 percent. The difference in the load will need to be compensated by additional resources. Other hours of

11,444

4,136

3,577

7,250

725

725

Nameplate PV

Low Band PV

High Band PV

0 5,000 10,000 15,000 20,000

Total Photovoltaic Capacity or Generation in MW

Transmission PV Distribution PV

30,000

35,000

40,000

45,000

50,000

55,000

9 10 11 12 13 14 15 16 17

Rem

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End

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High Band PV Applied to Load

Basecase PV Applied to Load

Low Band PV Applied to Load



concern include hour 12 through hour 14; these hours are typically peak PV production hours and usually modify the load at their respective hour by 22, 21, and 20 percent. During the eclipse, the production of PV will be obscured and the load will appear to be 13 to 14 percent higher than normal. Although this increase in load is not extreme, advanced coordination to mitigate ramping and balancing issues may be needed. It is recommended that utilities in California perform detailed studies and retain necessary resources to meet the increased and varying load.



North Carolina

Figure 4.5: North Carolina projected PV

generation for high band PV and low band PV scenarios in comparison to the total

installed nameplate capacity

Figure 4.6: North Carolina remaining

ending hour load (MW) to be supplied by Non-PV resources for the base case, low

band PV and high band PV scenarios

Table 4.2: North Carolina forecasted ending hour load and the remaining ending hour load (MW) to be supplied by Non-PV resources for the base case, low band PV, and high

band PV scenarios

Hour Ending 9 10 11 12 13 14 15 16 17*

Forecasted Load (MW) 23,881 18,715 24,231 20,459 24,346 21,949 24,487 23,158 24,539

Base case (MW) (Load – Total PV)

22,143 16,977 22,493 16,983 20,870 18,473 21,011 20,985 22,367

Low Band PV Scenario (MW) (Load – Low Band Eclipse PV)

23,664 18,498 24,014 20,024 23,911 21,514 24,052 22,886 24,267

High Band PV Scenario (MW) (Load – High Band Eclipse

PV) 23,749 18,583 24,099 20,195 24,082 21,685 24,223 22,993 24,374

Note: Time has been converted to military time and the forecasted peak ending hour has an asterisk (*) next to the time of day when the peak’s occurs. The data in this white paper includes updates from EIA-860 generator owner forms that may differ from the data provided in the 2016 LTRA.17 In Figure 4.5, the chart shows North Carolina’s total photovoltaic nameplate capacity in MW and the forecasted reduced photovoltaic capacity for the high-band and-low band eclipse scenarios. These MW values exclude additional hourly generation production scaling. The low-band eclipse scenario yields 87 percent reduction in transmission system nameplate capacity, and the high-band eclipse scenario gives a 92 percent reduction in transmission nameplate capacity. For both scenarios, there is a 100 percent reduction in distribution system nameplate capacity because North Carolina is in the path of total obscuration of the Sun. In Figure 4.6, the results from applying the production hourly generation scale to the PV generator scenarios and base case is provided. The plot shows the remaining ending hour load in MW which must be supplied by non-photovoltaic resources. Table 4.2 lists the forecasted ending hour load and the remaining MW load to be served by non-photovoltaic resources for each scenario and the base case. The peak forecasted load is 24,539 MW and

17 NERC 2016 LTRA Report

4,346

544

330

186

Nameplate PV

Low Band PV

High Band PV

0 1,000 2,000 3,000 4,000 5,000

Total Photovoltaic Capacity or Generation in MW

Transmission PV Distribution PV15,000

16,000

17,000

18,000

19,000

20,000

21,000

22,000

23,000

24,000

25,000

9 10 11 12 13 14 15 16 17

Rem

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End

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Time in Hours

Basecase PV Applied to Load

Low Band PVApplied to Load

High Band PV Applied to Load

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2016%20Long-Term%20Reliability%20Assessment.pdf



occurs at hour 17. At the peak hour, the PV generated for the base case decreased (modified) the load by nine percent, and when the eclipse occurs, the PV generated will be reduced and increase the load by only one percent. This is expected because North Carolina is in the path of total obscuration. The difference in the load will need to be compensated by additional resources. Other hours of concern include hour 12 through hour 14; these hours are typically peak PV production hours and usually modify the load at their respective hour by 17, 14, and 16 percent. During the eclipse, the production of PV will be totally obscured, and the load will appear to be 14 to 17 percent higher than normal. Although this increase in load is not extreme, advanced coordination to mitigate ramping and balancing issues may be needed. It is recommended that utilities in North Carolina perform detailed studies and retain necessary resources to meet the increased and varying load.


5:Chapter 5: Summary and Recommendations

A total solar eclipse is a predictable event that impacts solar generation over a short time period. As the number of variable energy resources on the power system increases, there is a greater reliability risk created by solar eclipses. As a result, there is an emerging concern of how to maintain a reliable and operable system during off-normal astronomical events (i.e., solar eclipses and geomagnetic storms). Distributed energy resources (DER) offer both emerging challenges and benefits to the grid as a means of serving load through dynamic operations and planning for future capacity. A growing reliance on these resources requires a larger knowledge base of how increasing resources on the distribution side both impact the BPS in the immediate and future timeframes. Significant changes towards renewable and distributed resources are being observed in all of North America due to government endorsed policy changes and through the overall mindset of the public. As a result of the expectation that DERs will increase, a path for industry to collaborate and to create regulatory requirements to integrate DERs is warranted. Controllable system resources that help to balance the electrical characteristics are necessary for the BPS. The analysis performed in this study showed no reliability impacts to BPS operations. Advanced coordination to address ramp issues and secure non-photovoltaic resources for August 21, 2017, can be readily obtained. Specific states (i.e., California and North Carolina) will experience the greatest impact to photovoltaic resources and system operations. It is recommended that utilities in all states perform detailed studies and retain necessary resources to meet the increased and varying load. There is an increasing need to coordinate DERs for system protection and quantify BPS electrical characteristics. Enhanced data, modeling, and system awareness will need to be driven forward through collaborative efforts within the industry. Understanding both localized fluctuations to power quality and varying levels of footprints (or jurisdiction) are challenges to developing solutions; this requires increased coordination and parallel initiatives. Seven years after August 21, 2017, North America will again experience a total solar eclipse on April 8, 2024. As industry continues to advance and modify the power system to meet customers’ needs by adding DER, the effect of eclipses on the BPS will become more relevant. Future detailed studies for off-normal astronomical events and advanced BPS coordination may be needed to ensure that the loss of generation produced from PV facilities during astronomical events does not adversely effect the reliability of wide-area BPS facilities


6:NERC Staff Contributors

Name Entity

Nicole Segal, Ph.D. North American Electric Reliability Corporation David Calderon North American Electric Reliability Corporation Elliott Nethercutt North American Electric Reliability Corporation Levetra Pitts North American Electric Reliability Corporation Alex Carlson North American Electric Reliability Corporation Terry Campbell North American Electric Reliability Corporation Thomas Coleman North American Electric Reliability Corporation John Moura North American Electric Reliability Corporation


A.Appendix A: Simulation Set-Up

The simulations were conducted on a PC with a Windows 7 Enterprise, 64-bit operating system. The computer contained a 7th generation 5600U Intel Core processer with a rated clock speed of 2.6 GHz. The PC contained 16 gigabytes of random access memory. The code was written and run on MATLAB version 9.0.0.341360 (R2016a) for Windows 64-bit machines.


B.Appendix B: Distribution Photovoltaic Generation Eclipse Map

In Figure B.1, a shaded map is provided to illustrate the linear degree of homogeneity reduction that was applied to each states’ distribution PV data.

Figure B.1: North America Map colored by the expected maximum occult fraction in the respective state that is applied homogenously to the distribution system photovoltaic

generators


C.Appendix C: State and Province Specific Forecasted Data

In Table C.1 below, the ending hourly forecasted loads is provided for every state and province under study.

Table C.1: State and province specific forecasted ending hourly loads (MW)

State or Province Forecasted Hour Ending Load (MW)

9 10 11 12 13 14 15 16 17

Alabama 13,084 14,120 14,939 15,579 16,055 16,462 16,720 16,729 16,445

Alberta 9,513 9,766 9,925 9,983 10,045 10,142 10,152 10,151 10,110

Arizona 12,462 13,174 13,980 14,662 15,231 15,899 16,508 16,974 17,254

Arkansas 6,825 7,340 7,799 8,288 8,650 8,848 9,043 9,065 8,900

British Columbia 7,425 7,612 7,724 7,913 8,013 8,104 8,126 8,154 8,186

California 39,293 41,003 43,148 45,064 46,933 48,816 50,346 51,233 50,969

Colorado 7,989 8,258 8,662 8,987 9,349 9,668 9,936 10,014 10,114

Connecticut 4,759 5,162 5,508 5,764 5,983 6,067 6,089 6,097 6,070

Delaware 1,687 1,804 1,909 2,028 2,124 2,197 2,222 2,247 2,251

District of Columbia 1,553 1,634 1,726 1,822 1,896 1,936 1,966 1,982 1,980

Florida 40,950 31,308 42,518 34,272 42,312 37,120 42,308 39,186 41,979

Georgia 21,451 23,109 24,414 25,500 26,168 26,593 26,755 26,685 26,296

Idaho 2,392 2,921 2,499 3,000 2,613 3,029 2,699 3,023 2,816

Illinois 25,182 29,397 26,806 29,909 27,828 30,007 27,787 29,668 28,590

Indiana 10,286 11,925 10,796 11,878 11,242 11,794 11,581 11,634 11,826

Iowa 7,087 7,495 7,844 8,108 8,312 8,452 8,528 8,592 8,487

Kansas 6,880 7,399 7,839 8,246 8,603 8,854 8,957 8,992 8,890

Kentucky 10,233 8,180 10,464 8,772 10,657 9,310 10,778 9,832 10,779

Louisiana 13,385 14,470 15,479 16,431 17,115 17,461 17,795 17,800 17,473

Maine 1,778 1,687 1,752 1,737 1,730 1,760 1,728 1,771 1,725

Manitoba 3,082 3,164 3,198 3,211 3,223 3,205 3,202 3,178 3,139

Maryland 9,050 9,822 10,550 11,188 11,696 12,041 12,200 12,276 12,140

Massachusetts 9,052 9,645 10,113 10,459 10,765 10,934 11,046 11,140 11,128

Michigan 19,869 15,648 20,207 16,934 20,069 18,150 19,857 19,124 19,424

Minnesota 12,376 12,953 13,457 13,715 14,018 14,105 14,144 14,117 13,998

Mississippi 7,336 7,811 8,223 8,599 8,871 9,012 9,098 9,076 8,919

Missouri 12,697 13,638 14,496 15,263 15,957 16,275 16,527 16,628 16,448

Montana 139 1,525 377 138 1,553 380 141 1,549 380

Nebraska 5,624 6,037 6,350 6,567 6,610 6,621 6,725 6,782 6,804

Nevada 5,005 5,439 5,893 6,296 6,737 7,081 7,432 7,665 7,695

New Brunswick 1,395 1,465 1,505 1,531 1,522 1,522 1,524 1,530 1,547

New Hampshire 1,842 1,962 2,046 2,099 2,148 2,171 2,178 2,181 2,178

New Jersey 12,326 13,497 14,670 15,688 16,460 16,869 17,111 17,281 17,176

New Mexico 3,043 2,540 3,142 2,687 3,177 2,831 3,158 2,957 3,126

New York 24,967 26,555 27,874 28,941 29,758 30,171 30,382 30,543 30,408

North Carolina 23,881 18,715 24,231 20,459 24,346 21,949 24,487 23,158 24,539

North Dakota 1,798 1,897 1,958 1,997 2,030 2,043 2,051 2,046 2,025

Nova Scotia 1,279 1,328 1,381 1,388 1,415 1,410 1,411 1,397 1,387


Table C.1: State and province specific forecasted ending hourly loads (MW)

Ohio 22,050 23,664 25,018 25,955 26,413 26,406 26,824 27,084 26,938

Oklahoma 10,447 11,376 12,263 13,064 13,860 14,197 14,521 14,673 14,495

Ontario 19,335 20,234 20,996 21,441 21,655 21,891 21,998 22,229 22,396

Oregon 8,050 6,770 8,150 7,137 8,187 7,491 8,161 7,812 8,058

Pennsylvania 21,647 23,278 24,773 25,823 26,588 26,975 26,995 27,015 26,897

Prince Edward Island 127 130 133 133 135 134 134 134 133

Quebec 19,188 19,736 20,108 19,978 19,978 20,007 20,093 20,342 20,122

Rhode Island 1,304 1,398 1,474 1,527 1,571 1,595 1,604 1,610 1,596

Saskatchewan 2,991 3,047 3,078 3,138 3,212 3,155 3,171 3,105 2,952

South Carolina 12,509 13,676 14,710 15,534 15,985 16,280 16,383 16,350 16,079

South Dakota 1,437 1,681 1,545 1,687 1,598 1,680 1,637 1,672 1,666

Tennessee 14,740 19,181 15,603 19,494 16,737 19,477 17,762 19,248 18,633

Texas 56,189 77,376 61,056 78,990 66,070 79,510 70,572 79,121 74,474

Utah 4,001 4,167 4,341 4,468 4,569 4,673 4,727 4,665 4,567

Vermont 791 827 854 869 887 895 898 905 916

Virginia 19,780 15,405 20,207 16,638 20,304 17,822 20,443 18,927 20,327

Washington 11,489 11,960 12,343 12,752 13,102 13,285 13,485 13,544 13,505

West Virginia 4,762 5,114 5,403 5,687 5,867 5,940 5,925 5,941 5,905

Wisconsin 10,694 11,311 11,832 12,249 12,577 12,712 12,737 12,684 12,473

Wyoming 2,464 2,317 2,483 2,374 2,457 2,412 2,432 2,433 2,413


Table C.2 contains state and province specific distribution system total nameplate capacity.

Table C.2: State and province specific distribution system total nameplate capacity (MW)

State or Province Forecasted Total Distribution PV Nameplate Capacity (MW)

Alabama 6.05

Arizona 1,059.36

Arkansas 3.92

California 7,249.89

Colorado 440.26

Connecticut 368.93

Delaware 95.84

Florida 217.30

Georgia 37.97

Idaho 8.92

Illinois 45.33

Indiana 32.29

Iowa 45.46

Kansas 0.00

Kentucky 0.00

Louisiana 84.94

Maryland 652.01

Massachusetts 1,642.01

Michigan 34.80

Minnesota 165.63

Mississippi 2.43

Missouri 136.32

Montana 7.63

Nevada 230.47

New Hampshire 71.52

New Jersey 1,549.47

New Mexico 127.17

New York 1,143.45

North Carolina 185.59

North Dakota 0.00

Ohio 98.49

Ontario 3,096.70

Oregon 137.93

Pennsylvania 273.49

Rhode Island 0.00

South Carolina 55.14

Tennessee 89.53

Texas 287.48

Utah 197.94

Vermont 124.82

Virginia 45.37


Table C.2: State and province specific distribution system total nameplate capacity (MW)

Washington 123.62

District of Columbia 39.54

Wisconsin 41.41