esig€¦ · this report was prepared for arizona public service company (aps) by black &...

SOLAR PHOTOVOLTAIC (PV) INTEGRATION COST STUDY B&V PROJECT NO. 174880

PREPARED FOR

Arizona Public Service Company

NOVEMBER 2012

Principal Investigators: Tim Mason, Project Manager Trevor Curry Mon Hong Benson Joe Scott Olson Mary Sprouse Dan Wilson

©Black & Veatch Holding Company 2011. All rights reserved.

Arizona Public Service Company | SOLAR PHOTOVOLTAIC (PV) integration cost Study

BLACK & VEATCH CORPORATION | Assumptions and Limitations Disclaimer i

Assumptions and Limitations Disclaimer

This report was prepared for Arizona Public Service Company (APS) by Black & Veatch Corporation (Black & Veatch) and is based on information not within the control of Black & Veatch. Black & Veatch has assumed that the information both verbal and written, provided by others is complete and correct; however, Black & Veatch does not guarantee the accuracy of the information, data, or opinions contained herein.

Any information shared with the Company prior to the release of the report is superseded by the Report.

Black & Veatch owes no duty of care to any third party and none is created by this report. Use of this report, or any information contained therein, by a third party shall be at the risk of such party and constitutes a waiver and release of Black & Veatch its directors, officers, partners, employees and agents by such third party from and against all claims and liability, including, but not limited to, claims for breach of contract, breach of warranty, strict liability, negligence, negligent misrepresentation, and/or otherwise, and liability for special, incidental, indirect, or consequential damages, in connection with such use.


BLACK & VEATCH CORPORATION | Table of Contents TC-1

Table of Contents Assumptions and Limitations Disclaimer ............................................................................................... i

Glossary of Terms ........................................................................................................................................... 1

Executive Summary ................................................................................................................................. ES-1

1 Introduction ........................................................................................................................................ 1-1

1.1 Arizona Renewable Energy Standards ......................................................................................................1-1

1.2 Variable Resource Integration Requirements ........................................................................................1-2

1.2.1 Generation Shape and Forecasting ............................................................................................1-2

1.2.2 Integration by Time Horizon ........................................................................................................1-2

1.3 Methodology for Variable Integration requirements ..........................................................................1-4

1.4 Quantification of Integration Costs .............................................................................................................1-5

1.5 Sensitivity Analysis ............................................................................................................................................1-6

1.6 Report Organization ..........................................................................................................................................1-7

2 Solar Resource Quantification and Load Estimation ............................................................ 2-1

2.1 Solar Resource in APS IRP ...............................................................................................................................2-1

2.2 Solar PV Project Selection ...............................................................................................................................2-3

2.3 PV Data, 10-minute Regulation Reserve Analysis .................................................................................2-4

2.3.1 Solar Resource Variability .............................................................................................................2-4

2.3.2 Solar Forecast Data ..........................................................................................................................2-9

2.4 Load Forecast and Errors ............................................................................................................................. 2-11

2.4.1 Load Forecast .................................................................................................................................. 2-11

2.4.2 Load Shape ........................................................................................................................................ 2-12

2.4.3 Load Forecast Error ...................................................................................................................... 2-12

3 Operating Reserves Modeling Methodology ............................................................................ 3-1

3.1 Reserve Requirements .....................................................................................................................................3-1

3.2 WECC System Interconnect Frequency .....................................................................................................3-1

3.2.1 CPS2 Requirements and Area Control Error .........................................................................3-4

4 Reserve Requirement Determination and Cost ...................................................................... 4-1

4.1 Reserve Calculation Methodology ...............................................................................................................4-1

4.2 Reserve Requirements .....................................................................................................................................4-2

4.2.1 Year 2020 Reserve Requirements .............................................................................................4-3

4.2.2 Year 2030 Reserve Requirements .............................................................................................4-5

4.3 Reserve Cost Methodlogy ................................................................................................................................4-7

4.3.1 Reserve Incremental Capacity Costs .........................................................................................4-7

4.3.2 Reserve Energy Costs ......................................................................................................................4-7

4.3.3 Spinning Reserve Costs ..................................................................................................................4-8

4.4 Cost Summary ......................................................................................................................................................4-8



4.4.1 Base Case ..............................................................................................................................................4-8

4.4.2 High Variability Scenario ...............................................................................................................4-9

4.4.3 Gas Price Scenario ............................................................................................................................4-9

5 Summary of Results and Recommendations ........................................................................... 5-1

5.1 Results ................................................................................................................................................................ .....5-1

5.2 Recommendations ..............................................................................................................................................5-3

5.2.1 Need for Actual, Time-Synchronized Data .............................................................................5-3

5.2.2 Forecasting Accuracy ......................................................................................................................5-3



LIST OF TABLES Table ES-1 Daylight Hour 10-Minute Reserve Requirement ........................................................... ES-4Table ES-2 Summary of Reserve Costs ..................................................................................................... ES-5Table 2-1 APS IRP Solar Project Forecast, 2020 and 2030 ................................................................. 2-1Table 2-2 Solar Design Assumptions, PVsyst ............................................................................................ 2-4Table 2-3 APS Load, 2020 and 2030 .......................................................................................................... 2-11Table 3-1 APS Operating Reserve Requirements ................................................................................... 3-3Table 4-1 2020 Monthly 10-minute PV Reserves (+/- MW) ............................................................. 4-4Table 4-2 2030 Monthly 10-minute PV Reserves (+/- MW) ............................................................. 4-6Table 4-3 10-minute PV Reserve Cost, Base Variability ..................................................................... 4-9Table 4-4 10-minute PV Reserve Cost, High Variability ...................................................................... 4-9Table 4-5 30% Higher Gas Prices & Base Solar Variability .............................................................. 4-10Table 4-6 30% Higher Gas Prices & High Solar Variability .............................................................. 4-10Table 5-1 Daylight Hour 10-Minute Reserve Requirement ................................................................ 5-1Table 5-2 Summary of Reserve Costs ........................................................................................................... 5-2

LIST OF FIGURES Figure ES-1 APS Installed Solar PV (MW) ............................................................................................... ES-1Figure ES-2 Daylight 10-minute Monthly Reserves, 2020 (+/-MW) ............................................ ES-3Figure ES-3 PV Variability for Different Arizona Locations ............................................................. ES-6Figure 1-1: Variable Energy Integration Requirements ...................................................................... 1-3Figure 2-1 Projected 2030 APS Solar Resources by Location ............................................................ 2-2Figure 2-2 Impact of Modeled Variability on 10 Minute Output – March Day ............................ 2-6Figure 2-3 Ten Minute Solar PV Variation Comparison ....................................................................... 2-7Figure 2-4 Ten Minute Solar PV Variation Comparison, Phoenix Area .......................................... 2-8Figure 2-5 Illustrative Solar Forecast Error (September 2030 Typical Day) ........................... 2-10Figure 2-6 Hour Ahead Load Forecast Error by Month ..................................................................... 2-12Figure 3-1 System Frequency Ranges .......................................................................................................... 3-2Figure 4-1 Incremental Regulating Reserves and ACE ......................................................................... 4-3Figure 4-2 10-minute Reserve Requirement by Month in Year 2020 (+/-MW) ........................ 4-4Figure 4-3 10-minute Reserve Requirement by Month in Year 2030 (+/-MW) ........................ 4-6


BLACK & VEATCH CORPORATION | Glossary of TermsGlossary of Terms 1

Glossary of Terms TERM DEFINITION

10-Minute Reserves For the purposes of this report, estimated regulating reserves calculated from the proxy CPS2 calculation of averaged 10-minute data, which is less granular than calculating reserves based on 1-minute data, but adequate where 1-minute data does not exist.

1-Minute Reserves For the purposes of this report, regulating reserves available to respond to actual 1-minute variability data of load and solar PV.

Area Control Error (ACE) The instantaneous difference between scheduled and actual interchange, taking into account frequency bias and correction for meter error.

Automatic Generation Control (AGC)

Controls on a generator that maintain system ACE by adjusting output through signals sent in sub-minute timeframes by the balancing authority. AGC is used to meet regulating reserve requirements and decrease short term variability of load and generation.

Balancing Authority (BA) The responsible entity that maintains scheduled and forecasted ACE within a specific region comprised of generation, transmission, and load.

Capacity The fixed AC rating of the plant, measured in Watts.

Contingency Reserves Excess operating reserves required to meet DCS by NERC due to an unplanned outage of a generator, transmission line, or piece of electrical equipment. These operating reserves must use Spinning Reserve and the remaining Non-spinning Reserve (at least half must be Spinning), sufficient to meet the NERC Disturbance Control Standard BAL-002-0, equal to the greater of: • The loss of generating capacity due to forced outages of

generation or transmission equipment that would result from the most severe single contingency;

(or) • The sum of five percent of the load responsibility served by hydro

generation and seven percent of the load responsibility served by thermal generation.

Control Performance Standard (CPS)

NERC Reliability standard that sets the limits of a Balancing Authority’s ACE over a specified time period.

CPS2 NERC CPS bounding ACE 10-minute averages within a specified range (L10) to limit variability over a 10-minute period. NERC compliance is achieved if the monthly average of 10-minute ACE averages is within the L10 value greater than or equal to 90% of the time or more.

CPS2 Model A model developed by Black & Veatch that calculates the difference


BLACK & VEATCH CORPORATION | Glossary of TermsGlossary of Terms 2

TERM DEFINITION

between the predicted and actual output of the PV energy on the APS system on a 10-minute basis, and compares it to the L10 operating dead band for 10-minute average ACE over an entire year. CPS2 is calculated every month.

Distributed Energy (DE) For the purposes of this report, small solar PV rooftop projects.

Disturbance Control Standard (DCS)

NERC Reliability standard that sets the time limit following a contingency on the system (generator, grid, or load) within which a Balancing Authority must return its ACE to a specified range.

Energy The actual AC output of a plant over a specified amount of time, measured in Watt-hours.

Global Horizontal Irradiance (GHI)

A measure of solar insolation measured in W/m2, correlating to the sum of direct sunlight, ground-reflected sunlight, and diffused sunlight. This measure is directly correlated with solar photovoltaic energy output.

Load Following The ability to meet variability in demand over a 10-15 minute time period over the course of a day. Load following plants are capable of coming online quickly (within minutes) to provide operating reserve, and can be spinning or non-spinning. This is a market mechanism, not a reliability standard.

N-1 Operation Principle “N-1” is a NERC operation principle that requires that an electric system be capable of withstanding the loss of any individual component without experiencing unacceptable system conditions.

Non-Spinning Reserves Excess grid flexibility consisting of two components: 1. Generation not connected to the system, but can be grid-

connected and online within a specified timeframe; 2. Load that can be interrupted within a specified timeframe.

North American Electric Reliability Corporation (NERC)

The regulating body that creates and enforces reliability standards for the North American bulk power system

Operating Reserve That capability above firm system demand required to provide for regulation, load forecasting error, equipment forced and scheduled outages and local area protection. It consists of spinning and non-spinning reserve.

Operating Reserve – Spinning

The portion of Operating Reserve consisting of: • Generation synchronized to the system and fully available to

serve load within the Disturbance Recovery Period following the contingency event; or

• Load fully removable from the system within the Disturbance Recovery Period following the contingency event.

Operating Reserve – Supplemental

The portion of Operating Reserve consisting of: • Generation (synchronized or capable of being synchronized

to the system) that is fully available to serve load within the


BLACK & VEATCH CORPORATION | 3

TERM DEFINITION

Disturbance Recovery Period following the contingency event; or

• Load fully removable from the system within the Disturbance Recovery Period following the contingency event.

Regulating Reserves An amount of reserve responsive to Automatic Generation Control, which is sufficient to provide normal regulating margin.

Regulation Up / Regulation Down

The ability of generation on AGC control to meet variability between generation and load over a short time frame (seconds to minutes). Regulation services are a market mechanism, not a reliability standard.

Renewable Energy Standard (RES)

For purposes of this report for APS, the standard established in 2007 by the Arizona Corporation Commission which identifies the percentage of load that must be served by renewable energy by a specified calendar year – currently, 15% by 2025.

Spinning Reserves Unloaded generation that is synchronized and ready to serve additional demand.

Utility Scale For the purposes of this report, ground mounted solar PV projects interconnected to the APS grid.

Western Electricity Coordinating Council (WECC)

Regional reliability organization that promotes reliability through standards for Western Canada and the Western United States.


BLACK & VEATCH CORPORATION | Executive SummaryExecutive Summary ES-1

Executive Summary Black & Veatch Corporation (Black & Veatch) is pleased to provide Arizona Public Service Company (APS) with this Solar Photovoltaic (PV) Integration Cost Study report. This report provides an estimate for the incremental operating reserves necessary to integrate projected PV development in the APS service territory in the years 2020 and 2030. This report also quantifies the anticipated incremental cost to provide the reserve capacity and energy services.

Project Overview

Arizona has adopted a Renewable Energy Standard (RES) requiring retail electricity providers to serve customers with 15 percent renewable energy by 2025, with 30 percent of this energy to be derived from distributed energy resources. To achieve this portfolio mix APS anticipates increasing PV resources on its system from roughly 100 MW(ac) currently to 1,038 MW(ac) by 2020 and 1,669 MW(ac) by 2030. To integrate the PV, which is inherently variable generation, into the electric system, additional operating reserve generation will be required by APS to maintain its system operations at its historic reliability level.

Figure ES-1 APS Installed Solar PV (MW)

The goal of this study is to quantify and value the operating reserves required to integrate this level of PV into the APS system during the 10-minute operating time-frame. Black & Veatch, using APS actual PV generation information from existing plant operation and forecast information for projected PV resources, developed a methodology and model to estimate the projected PV generation and its anticipated variability in 10-minute time steps for Years 2020 and 2030. To identify the reserves required to integrate the PV generation, Black & Veatch conducted an analysis to determine the expected incremental Area Control Error (ACE) violations, and then calculated the operating reserves necessary to minimize the violations to both meet North American Electric Reliability Corporation (NERC) Control Performance Standards (CPS2) and to APS current operating levels. Finally, once the expected operating reserves were determined, Black & Veatch quantified the cost of these reserves under a range of compliance and gas price scenarios.

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Summary of Findings To maintain APS’ current level of ACE, average annual operating reserves will need to increase by approximately 106 MW in 2020 and 168 MW in 2030, an amount equivalent to ten percent of the anticipated installed PV capacity. Incremental reserve requirements are lowest in the summer months and highest during the shoulder months during spring and fall, a somewhat counterintuitive result as it is the inverse of the PV energy generation profile. There are two primary factors likely accounting for this. First, the APS system is required to carry more reserves during the summer in the absence of PV due to higher load variability during this time, and the reserve levels used to balance load deviations are also used to balance the variability in solar generation. Second, reserve requirements are highly correlated with output variability, and there is low variability during summer months due to a greater number of clear days based on meteorological data, resulting in lower reserve requirements.

The cost of providing additional regulating reserves for expected PV generation on the APS system is estimated to be in the $2.00/MWh range in 2020 and $3.00/MWh range in 2030. This cost is a result of several factors. First, the marginal cost to provide reserve energy is highest during summer months but incremental requirements are low during this period. The cost of energy is lower during shoulder months when there is greater need for reserves for PV integration. Further, the marginal generation resource providing the reserves in this analysis is typically an efficient natural gas-fired combustion turbine. Gas costs are currently very low and are projected to remain at low levels in the future, thus the marginal cost of reserves energy is low. Finally, this analysis only considered the energy cost portion of the reserves requirements. Discussed in further detail in the report, the cost of the reserve capacity is accounted for in APS revenue requirements and is not included in this analysis.

Reserve Requirements

The need for and characteristics of operating reserves may change substantially in the future depending on numerous factors, including the mix of generating units in the system, the capabilities of intermittent generating technologies to minimize variability, the ability to share and trade reserves regionally, and changes in applicable planning standards in the future, amongst others. To assess future 10-minute reserve requirements for this analysis, APS and Black & Veatch chose to use a conservative approach that mimics existing reliability standards. Specifically, the analysis mimics the NERC Control Performance Standard 2 (CPS2) approach to calculating incremental regulation requirements1

Black & Veatch developed a spreadsheet model (“CPS2 model”) to calculate the variability of load and PV generation on a 10-minute time step throughout the year. Load variability was developed by taking the APS projected load forecast for 2020 and 2030 and then applying historic load variability to this to forecast the 10-minute changes in load. A similar process was used to develop the solar variability. First, hourly production estimates of PV output were developed for areas where APS envisions likely solar development in 2020 and 2030. A mix of PV generation

.

1 NERC CPS bounding ACE 10-minute averages within a specified range (L10) to limit variability over a 10-minute period. NERC compliance is achieved if the monthly average of 10-minute ACE averages is within the L10 value greater than or equal to 90% of the time.



technologies was modeled within these areas to reflect likely resource diversity. Where available, Black & Veatch used output and variability at existing Arizona solar facilities to develop a deterministically-derived quantity of PV variability. For areas without actual data, estimates of output was applied using solar production models and statistical variability modeling. Persistence forecasts adjusted for the sun’s position allowed Black & Veatch to develop a forecast of the difference between the predicted and actual output of the PV energy on the APS system in 2020 and 2030. The quantity of reserves for the “load only” case was determined, and then the reserves required for “load and solar” were calculated. The difference between the reserves required for the load only case and the load with solar case represents the incremental reserve requirements necessary needed for solar PV integration. Only reserve required for daylight hours when PV units operate were used to calculate the monthly averages.

To ensure operating requirements are maintained in 2020, when approximately 1,038 MW of PV is anticipated to be in operation in the APS system, incremental 10-minute reserves during daylight hours will increase by an annual average of 74 to 106 MW, depending on the compliance level of NERC CPS achieved. As noted above, this quantity decreases substantially during summer months as modeled solar PV output is less variable and as reserves are increased due to higher load. Figure ES-2 depicts the monthly 10-minute incremental reserve requirements during daylight hours for various levels of NERC control standard compliance.

Figure ES-2 Daylight 10-minute Monthly Reserves, 2020 (+/-MW)

By 2030, when an additional 631 MW of PV is installed, daylight hour 10-minute reserves are anticipated to be between 135 and 168 MW, again with lower monthly requirements during summer months. Table ES-1 depicts level of 10-minute regulating reserves required for 2020 and 2030, both on an annual basis and during summer periods only.

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90% Compliance

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Table ES-1 Daylight Hour 10-Minute Reserve Requirement

CPS2 COMPLIANCE LEVEL (PERCENT)

YEAR 2020 YEAR 2030

Annual Summer Annual Summer

90 74 15 135 49

95 81 33 146 73

99 106 64 168 109

Cost of Reserves for PV Integration

As PV output changes due to variations in insolation or cloud movements, the system operator must maintain sufficient resources to provide both for upward regulation (in the case of PV output decline) and downward regulation (in case of PV output spiking). The cost to maintain and provide the operating reserves includes a capacity value and an energy cost that reflects the actual change in system operating costs. The capacity value for reserves includes the cost of procuring resources capable of providing the reserves or alternatively, the value of withholding generation capacity being used for reserves that may otherwise be used by the utility or system operator for other purposes. The capacity value may be determined in a variety of ways, and should reflect the operating characteristics of the transmission Control Area, local energy product markets and the resource and market opportunities available to the utility. The Federal Energy Regulatory Commission (FERC) Open Access Transmission Tariff (OATT) Schedule 3, Rate Schedule 3 (Regulation and Frequently Response Service) determines capacity values in a cost-based approach, where the reserve value is a pro-rata portion of the total system capacity resource cost (based on the percentage of resources providing regulation and frequency response). A competitive market approach, such as with the California Independent System Operator (CAISO), will value the capacity at the price to procure these services in a transparent, bid-based market. A third approach is to value a specific identified incremental resource that will provide the regulation service (or opportunity cost in the event the user is withholding potential capacity from the market).

For this analysis, Black & Veatch and APS assumed that the resource providing the incremental reserve capacity is a GE LMS100 aero-derivative gas-fired peaking generator. APS anticipates adding many of these GE LMS100 units within its planning horizon to accommodate future load growth and to provide firming-up capacity to compensate for the intermittency of renewable energy generation. These units have the operating characteristics to allow them to be used for reserve requirements. Since they are planned as future additions in the current APS loads and resources plan, their capacity costs have already been included in the system revenue requirements. Accordingly, this analysis assumed no additional capacity costs to provide the necessary regulation capacity in integrating solar energy generation into the APS system.



The energy cost can be derived from the system energy costs, reflecting movements in the system energy output to accommodate the PV, and spinning reserve costs, which account for the increased cost to commit resources to ensure that there are sufficient resources on line to meet dispatch requirements. The value of this is captured in the production cost simulation. The integration cost from spinning reserves is based on higher reserves than what is required in the base case.

Energy costs were developed by APS Planning staff using the ProMod production cost modeling software. The system energy differential cost is simply the difference between system costs to move the system up netted out by the system cost savings of moving the system down using the new defined level of regulation reserves. The net costs to APS under all scenarios are detailed on Table ES-2.

Table ES-2 Summary of Reserve Costs

2020 2030

Solar MW 1,038 1,669

Solar GWh 2,293 3,602

CPS2 Compliance % 90% 95% 99% 90% 95% 99%

Incremental Spinning Reserves ($/MWh)

1.43 1.51 1.89 2.23 2.40 2.75

Incremental Energy Costs ($/MWh)

0.10 0.11 0.19 0.20 0.26 0.29

Solar Integration Costs ($/MWh)

1.53 1.62 2.08 2.43 2.67 3.04

Other Study Findings In addition to developing estimates of reserve requirements and costs, Black & Veatch noted several additional findings that are relevant to the operation of the APS system with substantial amounts of PV capacity on its system.

Distribution of PV Resources System-Wide Dampens Output Variability - While PV resources may ramp very quickly, the distribution of PV resource across the APS service territory will modulate the impact of this on the APS system. The impacts of clouds and monsoons on PV output will vary from one location to another within the service territory, but the aggregate of this volatility will likely reduce the overall impact on the system. Figure ES-3 below depicts an example of intra-day variability of three PV plants as a percentage of output, variability from the aggregate portfolio of these plants, and the cumulative solar output on the entire APS system in 2020. The cumulative portfolio variability is substantially less volatile than any of the individual resources; this is even more pronounced with the output of all plants is shown. Locations which have high concentrations of PV over a small geographic area will not see as much smoothing of their local net output profile; this will be the case for the local grids in the Phoenix, Gila Bend, and Yuma areas based on the projected locations for future solar PV development.



Figure ES-3 PV Variability for Different Arizona Locations

PV Production Forecasting – This analysis used persistence forecasting for PV to develop hourly forecasts of expected generation. Persistence forecasting is currently considered “best practice” in the industry though we observed that this method consistently overestimates generation during the hour of peak PV generation. Future forecast methods that improve upon a persistence forecast should be able to cut this level of reserves, and hence, integration costs.

Little Change in Costs in High Variability Solar Sensitivity Case - The “High Variability” case did not lead to a significantly greater amount of CPS2 violations. This is likely due to three main reasons. First, geographic diversity greatly smoothes out variation from solar PV projects. Figure ES-3 shows that the average change from nameplate capacity over a 10 minute period is 2 to 3 percent. If the level of variation included in the dataset increases by 50 percent the maximum impact is modest (an increase in roughly 1 percent of nameplate, or 10 MW in 2020). However, this maximum impact assumes that the variation in each solar project is in the same direction, which is not the case. This leads to the second point, that although individual project variation is now greater, the geographic diversity leads to some projects varying in the up direction while others vary downwards, reducing the net impact. Finally, in modeling the High Variability solar datasets, additional time periods were included where variability is present. This does not necessarily lead to more CPS2 violations than the Base Case, since it is the level of variation, not the number of time periods where variation is present that is most important when calculating CPS2 violations.

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Prescott (13 MW)Yuma (30 MW)Phoenix (15 MW)Cumulative (Three Projects)Cumulative (All Projects)



Little Absolute Change in Costs in Due to Gas Prices - The marginal generation resource providing the reserves in this analysis is typically a natural gas-fired combustion turbine. This causes the results to be sensitive to changes in natural gas prices, which are historically very volatile. The gas price modeled by APS in the base case (Year 2020)_production simulation was $5.82/MMBTU. To test the impact of changes in gas prices on the marginal energy and reserve costs, an sensitivity analysis was completed that assumed a 30% increase in the price of natural gas, to $7.56/MMBTU. The impact of gas prices on the marginal cost of integrating solar is less than the 30 percent increase in gas costs, with an approximate increase of 22 percent in 2020 and 15 percent in 2030. This equates to increases of roughly $0.25 to $0.50/MWh, depending on the time period and level of CPS2 compliance.


BLACK & VEATCH CORPORATION | Introduction 1-1

1 Introduction

To comply with the Arizona RES requirements APS anticipates developing or procuring substantial quantities of PV resources in the coming years, expanding from approximately 100 MW in 2012 to an estimated 1,038 MW in 2020 and 1,669 MW in 2030. The impact that this level of solar PV penetration will have on APS operation is important to understand so that appropriate steps can be taken to assure that grid stability is not compromised. To date, APS has been able to accommodate this variable generation on its system using existing resources, much as it has historically managed load variability. As the quantity of PV increases on the APS system in the coming years, new activities may need to be undertaken to manage this increasing level of variability.

Black & Veatch was retained by APS to quantify the variability of PV generation from existing and planned PV facilities, and to estimate the quantity and value of 10-minute operating reserves necessary to maintain reliable system operation. The objective of this study is to isolate the cost of integrating solar on to the system focusing on how the variability of solar output caused by fluctuations in solar irradiation, cloud movement, and monsoon weather conditions will impact the ability of system operators to balance real time load and generation. This Solar Photovoltaic (PV) Integration Cost Study details the anticipated incremental operating reserves required for APS to integrate solar PV resources on its system in 2020 and 2030. This section outlines the project background and approach used to assess the operating reserve requirements.

Black & Veatch developed and utilized an analytical methodology and models designed to simulate achievement of current NERC system reliability operating requirements. A kickoff meeting was held in January 2012 with a broad set of APS personnel to review the methodology and data requirements. Black & Veatch worked with different APS functional groups to gather data, apply APS specific production cost models, and develop load and solar forecasts. A project update meeting was held in April 2012. This report represents the final results of the effort.

1.1 ARIZONA RENEWABLE ENERGY STANDARDS

In November 2006, the Arizona Corporation Commission (ACC) adopted rules to expand the state's RES. Investor-owned utilities and electric power cooperatives serving retail customers in Arizona, with the exception of distribution companies with more than half of their customers outside Arizona, are subject to the standard.

Utilities subject to the RES must obtain Renewable Energy Credits (RECs) from eligible renewable resources to meet 15 percent of their retail electric load by 2025 and thereafter. Of this percentage, 30 percent (i.e. 4.5 percent of total retail sales in 2025) must come from distributed resources (DR) by 2012 and thereafter. One-half of the DR energy requirements must come from residential applications and the remaining one-half from nonresidential, non-utility applications.

APS will achieve the RES requirements by adding a number of wind and solar resources to its generation mix, which are detailed in Chapter 2. Because so much new solar PV, representing at times roughly 20 percent of the peak load for APS, may be coming on-line in the next 18 years, it is



important to consider what is required to assure that grid stability is maintained when such a large amount of intermittent resource is brought on-line and identify the costs associated with this.

1.2 VARIABLE RESOURCE INTEGRATION REQUIREMENTS

The integration of PV and other variable generation resources such as wind in utility portfolios has become an urgent topic in the power industry. These “as-available” resources have historically been a small enough portion of the electricity generation mix that they could be managed along with the variability of electricity load. However, as these resources increase to become a substantial portion of the generation mix of a load serving entity and transmission Control Area, the potential for variability is increasing to the point where changes to the electric industry operating and planning practices are required.

There is no common definition for the “integration” of variable resources, including PV. This is due in part to the fact that integration requires a variety of activities across different time horizons. Integration of variable generation requires careful coordination of generation and transmission operations and planning activities across time intervals ranging from seconds to years. Further, the extent to which “as-available” resource variability becomes an issue will depend on numerous system-specific factors including system size and the proportion of generation that is variable, the potential output of the resource, and the ability to forecast that output.

1.2.1 Generation Shape and Forecasting

Integration requirements will depend on the type of variable resources, the location of these resources, and the forecast accuracy of the generation. Different resources have different operating characteristics that must be considered. For instance, wind resources are variable throughout the entire day, and their variability must be considered operationally during high- and low-load periods, while solar resources are only available during the day and generally corresponds with higher electric loads.

Geographic dispersion of resources is also a consideration in determining integration requirements. Weather patterns are typically very localized, so if the variable resources are concentrated in one area the variability will be greater than if the resources are dispersed across a larger area such as a state or service territory. Chapter 2 illustrates how geographic diversity reduces variability in the use of intermittent resources.

The ability to accurately forecast variable generation will also influence integration requirements. There is a growing industry designed to better forecast variable generation in order to anticipate the resource and operating requirements, especially for wind. These efforts for solar PV are generally nascent but developing.

1.2.2 Integration by Time Horizon

Variable resource integration requirements vary by timeframe and function, which generally can be categorized into Transmission Operations in the instant (“real-time”), timeframe, Generation Operations in the near-term timeframe and Transmission/Resource Planning in the long-term



timeframe. Figure 1-1 depicts these activities by timeframe, and the primary integration requirements for each of these functions are described below.

Figure 1-1: Variable Energy Integration Requirements

Real-time Transmission Operations

PV resource output can change instantaneously, so real-time operation of the system is extremely important to integrating variable generation. Regulating reserves have historically been used by operations to address the small amount of variability caused by changes load and conventional generation; however, as higher penetrations of variable generation are grid-connected this variability increases significantly. Transmission operations maintains system frequency by balancing generation and load on short duration time periods (1-second to 10-minute timeframes) using regulating reserves to provide frequency and voltage regulation in as little as four seconds with the aid of AGC.

Generation Operations

Generation operations plays a vital role in PV integration by supporting a broad range of activities, including ensuring real-time operations has sufficient regulating resources, as well as committing generating units as much as a week ahead of the operating day to meet requirements. Generator operations must schedule resources to meet the shortfalls due to variability over longer timeframes (i.e. 10-minute to day-ahead timeframe). While there is some overlap (e.g. scheduling plants with adequate regulation reserve), Generator Operations is tasked separately to schedule units into the ancillary service market for load following, which occurs on an intra-hourly basis, as well as commit units into the hour-ahead and day-ahead markets. They must also make sure there is adequate Spinning (online and grid connected units) and Non-spinning (fast ramping peaking units or load shedding programs) Reserves to meet NERC Operating Reserve Requirements, including Contingency Reserves.



Generator Operators are also tasked with forecasting plant output, which is becoming increasingly more important as variable generation is beginning to be held accountable to meet projected forecasts or face penalties similar to that of conventional generation.

Transmission /Generation Planning

The large-scale development of PV impacts the types of generating resources that APS will need in future years, as well as the transmission requirements of the system. To integrate PV and other variable generation in the long term, the system will require flexible generation that can readily ramp and cycle to meet changes in demand. This will drive the types of resources available to APS and influence the cost of energy to the system.

Large–scale PV development will also impact transmission planning. Whereas conventional generation can generally be sited to maximize system benefit (i.e. utilize the existing transmission system and minimize transmission congestion), large-scale PV facilities are typically sited in isolated areas due to the quality of the solar resource, land characteristics, and cost, requiring new transmission development. The heavy concentration of future distributed PV in one location (Phoenix) may impact transmission planning and integration costs due to limited geographic diversity for PV generation, especially in 2030.

1.3 METHODOLOGY FOR VARIABLE INTEGRATION REQUIREMENTS

This study focused solely on the operating reserves required to integrate PV in dispatch operations. Specifically, this study considered the Operating Reserves – Spinning, Non-spinning, and Contingency - that can respond to changes in system ACE in a 10-minute time frame to ensure reliable system operation.

Just as there is no common definition for integration of variable generation, there is no industry standard for assessing integration costs. A variety of integration costs studies have been conducted to examine the cost of reserves and as part of this effort, Black & Veatch reviewed a number of these to assess whether these methodologies would be appropriate for APS. While several of these studies were similar to this analysis, a distinct difference is that this effort seeks to isolate the variability associated only with PV, and identify the reserve requirements specifically for PV. None of the other studies reviewed had a similar scope.

To assess 10-minute regulating reserves Black & Veatch chose to use an approach that mimics existing reliability standards. Specifically, the analysis used a statistical CPS2 analysis approach to calculate the incremental regulation requirements. The CPS2 criterion is a statistical measure of the ACE measured in 10 minute clock intervals. Black & Veatch developed a model (“CPS2 model”) to calculate the difference between the predicted and actual output of the PV energy on the APS system in 2020 and 2030. The increase in 10-minute regulating reserves required to keep the number of future CPS2 errors within a specified range was used as the basis to estimate the integration costs. Section 3 discusses the methodology and calculations in detail. The approach undertaken by Black & Veatch represents a conservative methodology, using 10-minute operations



data and established reliability requirements, which incorporate predictive capacity that could be employed for intermittent resources in the future.

The impact of variation in load was also taken into account in the CPS2 model. Ten minute and hourly load forecasts provided by APS were used to estimate the number of CPS2 violations due just to variations in load. The difference between the reserves required just due to load and those estimated when solar profiles were added represents the incremental reserves needed for solar PV integration only.

Note that throughout this analysis, it was assumed that there was no variation in forecasted wind. This allowed the solar PV impact to be isolated and quantified. The presence of wind in the APS system will impact the amount of reserves needed, but its impact cannot be simply added to the integration costs for solar. A full variable energy resource study would be required to estimate the overall impact of both wind and solar on the APS system.

A number of assumptions were made in developing the CPS2 model regarding the location of future projects, technology type, level of variability in the output profile, and forecast approach. The model is sensitive to changes in many of these inputs, impacting accuracy when estimating integration costs. As actual projects are developed in the future and specific algorithms are used for estimating solar output, the model should be revised. This will greatly increase the level of accuracy and confidence in integration costs when considering appropriate tariffs.

1.4 QUANTIFICATION OF INTEGRATION COSTS

As PV output changes due to variations in insolation or cloud movements, the system operator must maintain sufficient resources to provide both for upward regulation (in the case of PV output decline) and downward regulation (in case of PV output spiking). The cost to maintaining the capacity reserves is identified as the capacity value, while the energy cost reflects the actual change in system energy costs as a result of movements in the dispatch to accommodate the PV output.

Capacity Value

The capacity value for reserves includes the cost of procuring resources capable of providing the reserves or alternatively, the value of withholding generation capacity being used for reserves that may otherwise be used by the utility or system operator for other purposes. The capacity value may be determined in a variety of ways, and should reflect the operating characteristics of the transmission Control Area, local energy product markets and the resource and market opportunities available to the utility. The Federal Energy Regulatory Commission (FERC) Open Access Transmission Tariff (OATT) Schedule 3, Rate Schedule 3 (Regulation and Frequently Response Service) determines capacity values in a cost-based approach, where the reserve value is a pro-rata portion of the total system capacity resource cost (based on the percentage of resources providing regulation and frequency response). A competitive market approach, such as with the CAISO, will value capacity at the price to procure these services in a transparent, bid-based market. A third approach is to value a specific identified incremental resource that will provide the



regulation service (or opportunity cost in the event the user is withholding potential capacity from the market).

For this analysis, Black & Veatch and APS assumed that the resource providing the incremental reserve capacity is a GE LMS100 aero-derivative gas-fired peaking generator. APS anticipates adding many of these GE LMS100 units within its planning horizon to accommodate future load growth and to provide firming-up capacity to compensate for the intermittency of renewable energy generation. These units have the operating characteristics to allow them to be used for reserve requirements. Since they are planned as future additions in the current APS loads and resources plan, their capacity costs have already been included in the system revenue requirements. Accordingly, this analysis assumed no additional capacity costs to provide the necessary regulation capacity in integrating solar energy generation into the APS system.

Energy Value

Energy costs can be parsed into the dispatch costs, reflecting movements in the system energy output to accommodate the PV, and spinning reserve costs, which account for the increased cost to commit resources to ensure that there are sufficient resources on line to meet dispatch requirements. The value of this is captured in the production cost simulation, independent of the capacity costs.

Energy costs were developed by APS Planning staff using the ProMod production cost modeling software. The system energy differential cost is simply the difference between system costs to move the system up netted out by the system cost savings of moving the system down using the new defined level of regulation reserves.

1.5 SENSITIVITY ANALYSIS The 10-minute reserve requirements were calculated for an expected amount of variability based on historic variability of small-scale PV interconnected to the APS system and studies of variability seen in large systems throughout the U.S. (“Base Case”). In addition, a sensitivity analysis was completed to represent a potential “High Variability Case”, where the solar output had additional variability added by estimating additional periods with clouds and greater variability when clouds are present.

In addition to calculating the impact of additional variability, a sensitivity analysis was completed to assess the impact of higher gas prices on the integration costs. The marginal generation resource providing the reserves in this analysis is typically a natural gas-fired combustion turbine. This causes the results to be very sensitive to changes in natural gas prices, which are historically very volatile. The gas price modeled by APS in the base case production simulation for 2020 was $5.82/MMbtu. To test the impact of changes in gas prices on the marginal energy and reserve costs, a sensitivity analysis was completed that assumed a 30 percent increase in the price of natural gas.



1.6 REPORT ORGANIZATION The remainder of this report details the methodology and data used in the analysis, as well as the expected incremental resource requirements and costs for the base case and sensitivity analyses. Following this introduction, the sections are:

Section 2 – Solar Resource Quantification and Load Estimation

Section 3 – Operating Reserve Modeling Methodology

Section 4 - Reserve Requirement Determination and Cost

Section 5 – Summary of Results and Recommendations


BLACK & VEATCH CORPORATION | Solar Resource Quantification and Load Estimation 2-1

2 Solar Resource Quantification and Load Estimation

In order to estimate the impact of PV generation on overall APS system reliability, assumptions are required regarding the location, size, and performance of new PV facilities. The basis for these assumptions is APS 2012 IRP and information provided by APS for existing solar facilities. This section addresses the development of the solar resource profiles used in the analysis.

2.1 SOLAR RESOURCE IN APS IRP The 2012 APS IRP identifies 1,038 MW(ac) of solar PV resources to be installed by 2020 and 1,669 MW(ac) to be installed by 2030. Projects are to be located throughout Arizona, with the majority of them likely to be located in the Phoenix metro area. These resources are listed on Table 2-1 and depicted on Figure 2-1.

Table 2-1 APS IRP Solar Project Forecast, 2020 and 2030

LOCATION TYPE MW (AC) 2020

GWH 2020

MW (AC) 2030

GWH 2030

Modeled Data (PVsyst)

Prescott SAT 13 36 13 36

Gila Bend 1 Fixed Thin Film 16 37 16 37

Gila Bend 2 SAT 69 195 69 195

Ajo SAT 5 14 5 14

Hyder 1 SAT 43 122 43 122

Hyder 2 Fixed Thin Film 5 12 5 12

Chino Valley SAT 18 50 18 50

Yuma 1 Fixed Thin Film 33 77 33 77

Yuma Foothill SAT 40 113 40 113

Yuma 3 Fixed Si Rooftop 50 104 78 162

Palo Verde SAT 56 157 111 311

Phoenix 1 Fixed Si Rooftop 150 309 256 537



Actual Data Used (Scaled)

Prescott Fixed Si Rooftop 50 82 78 128

Flagstaff Fixed Si Rooftop 50 73 78 114

Scottsdale SAT 40 78 95 185

Yuma SAT 50 102 105 214

Prescott SAT 25 50 80 159

Total Solar Resources 1,038 2,287 1,669 3,602



Figure 2-1 Projected 2030 APS Solar Resources by Location



Since many of the future locations for new solar projects are undefined at this point Black & Veatch developed assumptions for likely development areas with input provided by APS. For utility scale projects, any known project locations were used to develop the resource profile for the potential facilities. Undefined projects were distributed amongst the following locations: Phoenix, Yuma, Prescott, Gila Bend, and Palo Verde. These locations were selected based on their solar resource potential, transmission availability, likelihood for future development, and proximity to load.

For DE rooftop projects, 80 percent of future DE PV was sited in the Phoenix metro area, with the remaining 20 percent evenly split between Yuma, Flagstaff, and Prescott. This assumption was agreed to by APS. The heavy concentration of future DE PV in one location (Phoenix) may impact integration costs due to limited geographic diversity for PV generation, especially in 2030. Black & Veatch did not explore the impact that this level of PV will have on individual circuits operating in the Phoenix area; as higher penetration of DE PV is seen on the APS system, this should be explored.

2.2 SOLAR PV PROJECT SELECTION

After establishing the locations, sizes and technologies to be modeled for each solar project, output profiles were developed to estimate the performance of each project. This was performed in one of two ways. First, APS provided Black & Veatch with 10 minute data from operating solar PV pilot projects throughout the APS service territory. This dataset was reviewed, with projects operating in locations with appropriate technology types selected. Five resource profiles, in Phoenix (Scottsdale), Yuma, Flagstaff, and Prescott (both rooftop and utility scale tracking) were chosen from the actual operating data provided by APS. Since all of these project output profiles were small (none larger than 228 kW(dc)), each was scaled to the size modeled in the IRP. The variability expected in output from a small solar project will be different than that for a large project; how this was treated is addressed in Section 3. Also, the actual data provided by APS reflects the capacity factor for facilities installed at the time, which will likely be lower and have a different output profile than future facilities. However, having this actual data provides diversity in the profiles and output relative to proxy data discussed next. The goal in the solar model was to develop a net amount of solar capacity and energy on the APS system in 2020 and 2030 close to the IRP estimates with diversity in the output profiles.

To develop hourly output profiles for all other projects, satellite data provided by National Renewable Energy Laboratory’s (NREL) Solar Power Prospector was used in conjunction with the PVsyst modeling software. To model each project in PVsyst, Black & Veatch developed production estimates and output profiles reflecting typical solar PV projects. Modules widely used in the industry were assumed to be utilized, with losses in the average range expected for new facilities. APS specific guidance for the DC to AC conversion factors was applied for both the modeled and actual solar performance data. Major design assumptions for the systems modeled in PVsyst are identified on Table 2-2.



Table 2-2 Solar Design Assumptions, PVsyst

PROJECT FIXED TILT THIN FILM

FIXED TILT CRYSTALLINE

SINGLE AXIS TRACKING

CRYSTALLINE

Panel Manufacturer First Solar Suntech Suntech

Panel Size (DC) 85 W 280 W 280 W

DC/AC Conversion 1.32 1.40 1.49

Modules/String 11 15 15

Inverter Mftr./Size SMA 500 kW SMA 500 kW SMA 500 kW

Inverter Loading Ratio 1.28 1.20 1.37

The full set of output profiles developed through this approach was compared to those forecast in the APS IRP. Due to different assumptions in the solar resource and performance between Black & Veatch and APS, the total energy differed slightly when the same net capacity was modeled. Since APS will be required to meet specific energy targets to be compliant with Arizona RES, capacity was adjusted in the Black & Veatch profiles to meet the energy estimates made in the IRP.

2.3 PV DATA, 10-MINUTE REGULATION RESERVE ANALYSIS

After establishing a basis for the PV resource data to be used, adjustments needed to be made to develop datasets appropriate for the 10-minute model regulating reserve calculations. Adjustments made to the datasets are described in this section.

2.3.1 Solar Resource Variability

For each solar profile, a 10 minute output profile was developed for use in the CPS2 model. As stated earlier, the data used to develop the generation profiles was from either actual project data or satellite data from locations close to the expected project location. While the actual data was for 10 minute output, scrutiny had to be applied to determine if the variation seen on these small datasets scaled to utility scale projects were appropriate. For the satellite data, only hourly profiles are available, meaning that adjustments had to be applied to reflect the likely variation that will be seen on a 10 minute basis.

For the projects modeled in PVsyst, an Excel model was developed to produce 10 minute data points for each location by interpolating the hourly data-points. Variability was then added to these curves through a two-step process based on approaches and research performed by the California



Independent System Operator (CAISO)2 and Lawrence Berkeley National Laboratory (LBNL)3

. The steps utilized were as follows:

Determine which hours should have variability:

CAISO analysis showed that only hours where clouds are likely to exist should have variability incorporated. Hourly solar output that is more than 10 percentage points lower than the maximum output for that time period in a given month is assumed to have clouds present. This led to 18 to 40 percent of daylight hours (depending on the location profile) having some variation, with the weighted average being 24 percent. These numbers are likely conservative given the sunny climate present at most project locations. A sensitivity (“High Variability”) where clouds were assumed in any hour more than 7 percentage points lower than the maximum monthly output for that hour (leading to 36 percent of periods having variation) was also run.

Estimate deviation:

Applying this variability led to resource curves that are more representative of actual solar project performance when compared to smooth curves interpolated between hourly data points. As an example, the difference between a smooth curve (blue line) and curve with variation added (red line) for one day with variability in March forecasted for a Phoenix project can be seen below.

Based on LBNL analysis and Black & Veatch verification of solar irradiance data, it was estimated that the deviation due to clouds could be represented by a normal distribution curve with a standard deviation of 10 percent. For each data point with variation, a random number was chosen on a distribution curve with that shape to reflect the changes from smooth interpolation. The High Variability sensitivity case used a 15 percent standard deviation.

2 Methodology is utilized from the CAISO Renewable Integration Studies regarding when variability should be applied. Information is available in the technical appendix at http://www.caiso.com/282d/282d85c9391b0.pdf. 3 Mills, A., and Wiser, R., “Implications of Wide-Area Geographic Diversity for Short-Term Variability of Solar Power”, September 2010, LBNL 3884-E, available at http://eetd.lbl.gov/EA/EMP. The information presented in this paper regarding the statistical variability of solar power systems over a 10 minute period was used in the model, along with validation being performed on actual solar irradiance data and personal communications with Andrew Mills of LBNL.

http://www.caiso.com/282d/282d85c9391b0.pdf�

http://eetd.lbl.gov/EA/EMP�



Figure 2-2 Impact of Modeled Variability on 10 Minute Output – March Day

Solar profiles generated in 2020 and 2030 reflect variability based upon large multiple arrays throughout Arizona which exhibit less variability than a single array, single location configuration. Figure 2-3 shows the magnitude of 10 minute output changes as a percentage of nameplate for single projects on one day in January for Prescott, Yuma, and Phoenix, along with both the cumulative output for these three projects and the cumulative solar output of all solar projects in 2020. Percentage of installed capacity is used as the comparison basis instead of MW due to the differences in size for the projects represented by each line.

As can be seen in the figure, the cumulative change as a percentage of installed capacity is much lower when compared to the changes that could occur in individual projects on days with cloudy skies. This effect is magnified when the overall cumulative output of all projects on the APS system is taken into consideration. The cumulative line for all projects represents over 1,000 MW of solar capacity; even significant changes in one small project (such as 13 MW in Prescott) may not create great changes in the overall cumulative line.

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Figure 2-3 Ten Minute Solar PV Variation Comparison

Locations which have high concentrations of PV over a small geographic area will not see as much smoothing of their local net output profile. Figure 2-4 shows the change from nameplate over one day in January for three solar PV profiles in the Phoenix area, along with the cumulative Phoenix only variation to be expected on that day from these profiles. While there is still some benefit to be gained in smoothing profiles from larger arrays and geographic diversity here, the benefit will not be as pronounced when compared to a much larger area. This will be the case for the local grids in the Phoenix, Gila Bend, and Yuma areas based on the projected locations for future development. The impact of this level of local variation, such as operation of distribution feeders in these municipalities, was not evaluated as part of this study.

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Figure 2-4 Ten Minute Solar PV Variation Comparison, Phoenix Area

Extreme ramps in PV output can occur quickly on partially cloudy days. The solar data developed suggest that such events can occur but will be infrequent and can be mitigated by diversity in solar installation sites. The impact of extreme ramps in a short timeframe is an operational issue that cannot be addressed in a 10 minute analysis

For the actual 10 minute datasets that were used, no estimated variability needed to be added. On the contrary, there was concern that the datasets will overstate variability once they are scaled from their actual size (<250 kW) to the sizes used in the CPS2 model (up to 80 MW). To determine the amount of 10 minute variability present, the standard deviation of 10 minute step changes relative to nameplate for daylight periods was calculated. For the data provided by APS, the standard deviation was 6 to 8 percent, which is on the high end of the range expected from research performed by LBNL4

4 Personal communications with Andrew Mills of LBNL, August 2011. LBNL estimates that 10 minute daytime step changes relative to nameplate should be around 7 percent, with the 99.7th percentile of change around 35 percent. Since this was in reference to projects in southern California, an assumption could be made that variability in southern Arizona would be lower based on the percentage of sunny periods in Phoenix (85) to Los Angeles (73) and the projected average AC capacity factor for fixed tilt crystalline systems in southern Arizona versus southern California (roughly 23 percent v. 21 percent). The 99.7th percentile of changes in the APS data was also on the high end.

. In order to be conservative in the calculation approach, this same variation was carried over when scaling the dataset for large projects. In the future, the variation could be estimated more accurately by using a rolling average to reflect actual variation in operational APS projects when large-scale 10 minute data is available.

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As sensitivity to the base case analysis, a High Variability case was performed where the data derived from PVsyst had additional variability added by estimating additional periods with clouds and greater variability when clouds are present. This raised the standard deviation of 10 minute changes for all periods by roughly 2 percentage points to nearly 6 percent. The actual APS data used was not modified in this sensitivity case, since these profiles were already deemed to have significant variability and the fact that they represent only 20 percent of the overall capacity modeled.

2.3.2 Solar Forecast Data After completing estimates for the PV output, a forecast methodology was established to determine the amount of error likely to be encountered. Although this model is reflecting performance of the APS system in 2020 and 2030, the forecast approach developed was intended to be simple, utilizing basic mathematical approach that could be implemented today. More sophisticated estimating methods could reduce the forecast error, and subsequently, the overall regulation costs. This will be especially important as the total MW of solar on the APS grid grows. Ramps in 2030, when 1,669 MW of nameplate capacity is anticipated for installation, would likely exceed 400 MW/hour under normal circumstances. A persistence model was used for solar forecasting. A persistence forecast simply assumes that what happened in the recent past is the best approximation for what will likely happen in the future. In this model, the output in the period 70 minutes prior to the forecast hour was used after being adjusted to reflect the expected movement in the sun. 70 minutes prior was found to produce better results than 60 minutes prior since the forecast is attempting to represent the average output over an entire hour. The sun’s movement was calculated as a percentage difference using the NOAA Clear Sky model for a location in downtown Phoenix, AZ. Projected solar irradiance from this model was placed into a model developed by Black & Veatch to estimate power output at all time periods under sunny conditions. For early morning time periods where the persistence model would forecast zero output when it is known that the sun will have risen, the output for that hour from the previous day was used. For solar forecasting, no research currently predicts better forecasting for short-term timeframes than what can be accomplished with persistence adjusted for sun position. However, some organizations are working on improved models that use technologies such as a Sky Imager to predict the impact of clouds. This technology is deployed at a specific site, providing near-term output forecasts using actual weather data. Solar forecasting is not widely practiced today given the small impact of solar on most utility balancing authorities, but may become more important in the future as the penetration of solar energy increases. When there is a large amount of solar energy integrated in the system, such as is forecasted in 2030, using one estimate for an entire hour will be inherently inaccurate even on sunny days. With 1,669 MW on the grid in 2030, sunny day ramps in the morning and afternoon are roughly 400 MW/hour. If the average for this hour is used in the forecast, the beginning and end of the hour will be off by 200 MW, which could very likely lead to CPS2 violations even if the net output of solar is perfectly understood. Changes in operations may be required when this much solar is on the system to maintain reliability. Examples of the type of forecast errors seen for representative days in the model for solar can be seen below. Figure 2-5 below illustrate the predictive ability of the persistence forecasting method for a selected day in September 2030; overall APS system solar output is in red, while predicted



hourly performance using a persistence forecast is in blue. Note how much the output increases in the morning and afternoon due the sheer volume of solar PV on the system.

Hour of Day

Figure 2-5 Illustrative Solar Forecast Error (September 2030 Typical Day)

In reviewing the forecast results versus the predicted performance, many times the forecast methodology over predicts output at and near the peak. While the exact reasons are not clear, this may be partially due to how solar facilities are being designed today. Inexpensive panels are making it more attractive for developers to design a system where the inverter maximum is hit more often, leading to a longer, flatter peak period in exchange for losing some performance (inverter clipping) at the peak. The persistence forecast is based on production following the sun’s position; if solar irradiance is increasing, the persistence model will predict that power output will also be increasing. This is an example of logic that could be easily changed if this is causing significant errors in the forecasting approach in actual projects in the future.

The mathematical based approach of using a persistence forecast also assumes that there will be no material bias towards under-forecasting or over-forecasting output. This forecasting method leads to an equivalent amount of regulation up and down capacity required to balance the system caused by forecasting errors. More information on how the reserve requirements were calculated can be found in Section 3.

This analysis did not consider improvements to the solar forecasting method outlined above, hence this is a conservative estimate for the level of reserves required. Future forecast methods that improve upon a persistence forecast may be able to cut this level of reserves and hence, integration costs. The models established in this study can estimate what the cost benefit is to different levels of forecast improvement.

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2.4 LOAD FORECAST AND ERRORS APS today operates its system with approximately 30 to 80 MW of regulating reserves, equivalent to about 1.5 percent of real time load even without large penetration levels of solar generation. The integration of solar resources must be coordinated with the scheduling and forecasting of hourly loads. The load in 2020 in the APS service territory will average 4,000 MW, with an expected summer peak demand of 8,198 MW. The variability of solar resources is only an issue during daylight hours whereas the variability of load is an issue in all hours.

2.4.1 Load Forecast Utilities typically forecast load in several time-frames, including multi-year, annual, monthly, day ahead, and hour ahead periods. The use of each forecast is dependent on the requirements for the forecast period; for instance, if the utility is determining when to procure new generation it will require a multi-year peak forecast, but for estimating revenues one would more likely use an annual forecast. The hour ahead load forecast is important because it generally used by system operators to dispatch generating resources. For reserve planning, an hourly forecast is required since within the hour the variations in load and generation occur, which are typically met with contingency, or operating, reserves.

Table 2-3 was derived from the hourly load profiles for 2020 and 2030 provided by APS, and provides the monthly peak demand and energy forecast for2020 and 2030.

Table 2-3 APS Load, 2020 and 2030

APS LOAD 2020 2030

Month Peak (MW)

Energy (GWh)

Peak (MW)

Energy (GWh)

Jan 4,951 2,664 6,453 3,470

Feb 4,681 2,291 6,202 3,023

Mar 4,172 2,392 5,500 3,127

Apr 4,536 2,285 5,750 2,936

May 6,331 2,962 8,208 3,834

Jun 7,100 3,477 9,223 4,525

Jul 8,198 3,990 10,862 5,234

Aug 8,198 3,838 10,862 5,033

Sep 7,252 3,411 9,467 4,455

Oct 5,503 2,634 7,139 3,414

Nov 4,520 2,348 5,839 3,021

Dec 4,868 2,734 6,286 3,532

Annual 8,198 35,028 10,862 45,602



The introduction of variable energy resources, such as solar and wind, to the electric system will increase the intra-hour variability that operators must manage. Additional reserves must be deployed to bring the system back into balance with the increased variability. Since CPS2 violations can occur due to both variation in intermittent resource output and errors in load forecasting, the analysis first identified the number of violations due to load only, thereby isolating and quantifying the reliability issues due to solar.

2.4.2 Load Shape APS provided to Black & Veatch its 10 minute load forecast for 2020 and 2030, which was developed using the 2020 and 2030 hourly load forecast data and the actual ten minute load variation from 2009. Therefore, the variability in the 2020 and 2030 dataset between hourly predicted loads and ten minute intervals through the year will match the historical load variability exhibited in 2009.

2.4.3 Load Forecast Error Imperative to this analysis is isolating the system variability caused by solar PV generation from variability caused by other factors, namely changes in load and other variable generation on the system. To isolate this Black & Veatch determined the expected variability of load in 2020 and 2030 based on historical load variability.5

To calculate the load forecast error within a given hour, Black & Veatch compared actual measured 10-minute loads to the hour-ahead forecast, taking the difference as the load forecast error. On average, the absolute value of the load forecasting error on an hour ahead basis is approximately 1.1 percent for the entire year based on APS data. This average error is used for both the 2020 and 2030 CPS2 calculations. A 1.1 percent error for the average load of 4,000 MW in 2020 translates to a 44 MW increase in the system ACE, while it translates to a 90 MW increase in the system ACE for the peak load (8,198 MW). Since this is only the annual average, there are many hours in which the hour ahead load forecasting error is more or less than 1.1 percent. Figure 2-5 depicts the average monthly hour-ahead load forecast error.

Figure 2-6 Hour Ahead Load Forecast Error by Month

5 The APS system historical load includes small quantities of PV and wind generation. As the variations for these resources were not identified, this analysis assumes the variations from those resources are included in the load forecast error.

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BLACK & VEATCH CORPORATION | Operating Reserves Modeling Methodology 3-1

3 Operating Reserves Modeling Methodology

3.1 RESERVE REQUIREMENTS

As the operator of a balancing authority within the Western Energy Coordination Council (WECC), APS must comply with an established set of reliability measures developed by the North American Electric Reliability Council (NERC). Balancing authorities throughout the WECC must work in coordination to minimize system disturbances and to avoid inadvertent power interchange between them.

As more variable energy resources are developed in the WECC, the challenges to maintain the NERC requirements increase due to the inherent variability of these resources. The NERC is currently investigating and testing different operating schemes that will assist Balancing Areas to maintain appropriate grid reliability, but for purposes of this analysis we assume that APS, as a balancing authority, will be required to comply with existing NERC requirements in both 2020 and 2030. 6

3.2 WECC SYSTEM INTERCONNECT FREQUENCY

A key component of reliability is maintaining system frequency. The frequency of the entire WECC interconnect is 60Hz when instantaneous load matches the generation on the system. The variability of generating output from solar is expected to cause more imbalances between load and generation, thereby impacting the system frequency. Maintaining the system wide frequency at 60Hz requires coordination within all balancing authorizes in the WECC. Balancing authorities that under or over generate relative to their scheduled load and generation will cause inadvertent interchange of power that will flow to other interconnected control areas. During the course of normal power system operations there will be very few instances when the amount of load will exactly match the amount of generation on the system. Many times the frequency of the grid will be in a tolerable dead band range above or below 60Hz and will not cause significant damage to the system. The system frequency increases when generation is greater than load and conversely the system frequency decreases when generation is less than load. This is not a problem as long as the appropriate amount of operating reserves is available to correct for over or under generation.

Figure 3-1 below shows the dead band range of the system frequency7

6 APS is currently enrolled of the NERC field trial of the Reliability Based Control (RBC) standard, which is a potential replacement for CPS2. Enrollment in the NERC field trial of RBC exempts APS from CPS2 performance penalties, although APS is still required to submit monthly CPS2 reports to NERC.

. APS system operators primarily use regulating reserves that are responsive to AGC to match loads and resources every four seconds.

7 Information from Kirby, et.al., “Frequency Control Concerns in the North American Power System”, Oak Ridge National Laboratory, publication ORNL/TM-2003/41, December 2002.



Figure 3-1 System Frequency Ranges

The electricity grid was designed to handle large drops in frequency. For example, when a large baseload generation plant suddenly trips offline, existing safeguards such as AGC, governor controls, and operator action can usually mitigate the problem and bring the system back into equilibrium quickly. However, there is growing concern within the industry that increasing levels of solar in the future will create a reliability issue with system operations. The most problematic integration issue is the inability to accurately forecast the output of solar, particularly fast ramps of solar during cloudy days. This variability on the forecasted amount of energy coming from solar makes matching up instantaneous loads with generation even more difficult unless additional regulating reserves are available to make up the difference. The variability of solar resources creates an additional cost to the system because other resources have to be made available to ramp system resources up or down to maintain the system frequency at an acceptable dead band range around 60 Hz. The generation regulation costs being calculated in this study is directly attributed to the accuracy of the output forecast for solar resources.

Operating reserves are composed of two main types of reserves: Regulating and Contingency Reserves. Governor controls at non-renewable generating units have speed settings that regulate the plant generating speed in response the system frequency. The 5 percent droop setting on the governor establishes a dead band range which restricts the movement of a unit, except in the event of large system frequency excursion (i.e. loss of unit or loss of transmission line). Regulating reserves are controlled by AGC and allows generating units to increase or decrease power output marginally in response to smaller scale system energy imbalances. Contingency reserves on the other hand are used to correct for larger scale system imbalance cause usually by a loss of generating unit or transmission line.

One of the primary reliability protocols for transmission Balancing Authorities is the NERC Minimum Operative Reserve Criteria (MORC) as it pertains to operating reserve as defined in



NERC-STD-BAL-002-08

Regulating reserve - sufficient 1-minute regulating reserve, immediately responsive to AGC to provide sufficient regulating margin to allow the Balancing Authority to meet NERC's Control Performance Criteria (see BAL-001-0).

, which requires that each Balancing Authority shall maintain a minimum Operating Reserve, which is the sum of the following:

Contingency reserve - an amount of 1-minute Spinning Reserve and Non-spinning Reserve (at least half of which must be Spinning Reserve), sufficient to meet the NERC Disturbance Control Standard BAL-002-0, equal to the greater of:

• The loss of generating capacity due to forced outages of generation or transmission equipment that would result from the most severe single contingency; or • The sum of five percent of the load responsibility served by hydro generation and seven

percent of the load responsibility served by thermal generation.

Table 3-1 lists the type of operating reserve requirements and the time frame in which they respond to.

Table 3-1 APS Operating Reserve Requirements

The regulating and contingency reserve requirements will vary by time of day and season based upon the real time load on the system. System operators at APS indicated to Black & Veatch that the current regulating reserve requirement is approximately 1.5 percent of the load at any given time. For example, if system load is 5,000 MW, then APS will carry 75 MW of upward and downward regulation capacity for that hour. In order to adhere to the NERC MORC APS, must spin for 7 percent of the thermal resources online. Therefore if there is 5,000 MW of generation online for an hour APS must carry 350 MW of contingency reserve with no more than 50 percent of the reserves (175 MW) being quick start units for that hour.

APS is a participant in the Southwest Reserve Sharing Group (SRSG), which administers requirements related to regional compliance with BAL-002, the Disturbance Control Standard, EOP-001, and EOP-002. SRSG Participants share contingency reserves in order to maximize generator 8 http://www.nerc.com/files/BAL-STD-002-0.pdf

CURRENT APS OPERATING RESERVE REQUIREMENTS REQUIREMENT RESPONSE TIME

Operating Reserves

Regulating Reserves

Regulation Up ~1.5 percent of Load Seconds

Regulation Down ~1.5 percent of Load Seconds

Contingency Reserve

Spinning Reserves The greater of N-1 or 7 percent of system thermal

generation and 5 percent of hydro generation. At least

50 percent must be spinning reserve

Minutes to Hours

Non-Spinning Reserve

Minutes to Hours



dispatch efficiency and decrease costs of compliance with the Disturbance Control Standard (DCS), as well as contributing to the reliability of the Western Interconnection.

3.2.1 CPS2 Requirements and Area Control Error

To estimate the reserves needed to handle variations in 10 minute output, an analysis was performed using NERC CPS2 as the metric to determine the impact of solar on the APS balancing system. The NERC CPS2 reliability criterion stipulates that the system Area Control Error (ACE) must be within a tolerable deviation range (defined as the L10) for 90 percent of the 10 minute clock intervals for each month. The system ACE is essentially the difference in the scheduled generation and load versus the actual generation and load for each 10 minute clock interval when no intermittent energy is on the system.

The ACE is the difference between scheduled and actual electrical generation within a control area on the electric power system taking the frequency bias into account. Under-forecasting the load can be offset by under forecasting of the wind or solar. Conversely, over-forecasting of the load will be exacerbated by under-forecasting of the wind or solar and cause a higher frequency bias, unless AGC or the system operator can take corrective action.

The formula for calculating the ACE is:

ACE = (NIA - NIS) - 10b (FA - FS) Tob + IME Where:

NIA represents actual net interchange (MWs). NIS represents scheduled net interchange (MWs).

b represents the control area's frequency bias setting (MW/0.1 Hz). FA represents actual system frequency (Hz).

FS represents scheduled system frequency (60.0 Hz in North America). Tob represents scheduled interchange energy used to correct inadvertent accumulations (MWs). IME represents a manually entered amount to compensate for known equipment error (MWs).

The current L10 for the year 2012 is 46 MW. Based on load requirements Black & Veatch estimated the L10 for 2020 at ± 51 MW and for 2030 at ±59 MW. To calculate the L10 for years 2020 and 2030 Black & Veatch escalated the current L10 value by 1.4 percent, the forecasted annual average load growth for the WECC. As the size and the magnitude of the WECC grows in the future the L10 for APS will increase proportionately. This means that the net difference between scheduled generation and actual load must be inside the range of ± 51 MW for 2020 and ± 59 MW for 2030 after operating reserves are deployed to rebalance load and generation.


BLACK & VEATCH CORPORATION | Reserve Requirement Determination and Cost 4-1

4 Reserve Requirement Determination and Cost

This analysis seeks to quantify and value the incremental operating reserves needed in order to integrate the anticipated PV capacity on the APS system in 2020 and 2030. As discussed in Section 3, Black & Veatch used the current NERC CPS2 standard as a proxy to measure the reserves required and as the basis to estimate the costs of future reserves. Specifically, Black & Veatch considered the requirements and cost to achieve CPS2 compliance at the 90 percent, 95 percent, and 99 percent monthly levels.

The methodology to calculate the quantity and costs for solar PV reserves was completed in a two step process. The first step was to calculate the amount of incremental upward and downward regulating reserves required to maintain a specified level of monthly CPS2 performance caused by forecasting errors from the solar PV penetration levels in each case. The second step took the amount of incremental regulating reserves calculated in the first step and modeled the cost impact to the system using an electric system production simulation cost model9

to capture the system energy cost differential of providing the regulating energy margin.

4.1 RESERVE CALCULATION METHODOLOGY

The incremental amount of reserve capacity required to maintain CPS2 compliance was calculated as a difference from the reserve capacity required due to loads only and that due to the combination of loads and solar. For the base case, it was assumed that APS would maintain a 99 percent CPS2 compliance (the standard that APS has historically achieved). Sensitivity cases were also generated to reflect different levels of CPS2 compliance (90 and 95 percent) and using solar profiles with greater variability.

To calculate reserve requirements for any given level of NERC CPS2 compliance, Black & Veatch developed a spreadsheet model using Microsoft Excel. The spreadsheet requires forecasted hourly loads and 10-minute expected generation inputs an entire year (the inputs used in the CPS2 model are discussed in Section 2). After the load and solar data are entered into the model the number of CPS2 violations is calculated. Violations due to load only were first calculated, assuming perfect solar forecasting. After the solar forecast variability was added, the incremental reserves required beyond what was needed for load only during daylight hours was calculated. The net difference between the actual load and solar generation and the forecasted load and solar generation is the forecast error.

As discussed in Section 2, the approach taken to estimate the solar output profiles was conservative, reflected in both the number of cloudy time periods forecast during daylight hours and the level of variability seen in the actual solar output data used. While it is possible that the level of variability (and hence CPS2 violations) could be lower, for planning purposes it is appropriate to take a conservative approach. A sensitivity case was developed that includes additional solar variability (more cloudy time periods and greater variation when there are clouds) to assess the potential impact on the quantity of CPS2 violations. After completion of this revised

9 ABB/Ventyx ProMod production cost model was used in this study



dataset, the profiles were placed into the same CPS2 models as the base case, with the new level of 10-minute reserves calculated.

A number of assumptions were made in developing the CPS2 model for location of future projects, technology type, level of variability in the output profile, and forecast approach. The model is sensitive to changes in many of these inputs, impacting accuracy when estimating integration costs. As actual projects are developed in the future and specific algorithms are used for estimating solar output, the model should be revised. This will greatly increase the level of accuracy and confidence in integration costs.

4.2 RESERVE REQUIREMENTS

APS’ L10 in 2012 is 46 MW. In 2020 and 2030 the L10 is estimated to be 51 MW and 59 MW respectively. Figure 4-1 depicts the calculated ACE for all the ten minute periods in a single day in 2020. Since the L10 is 51 MW for the year 2020, the ACE can stay at +/- 51 MW without a CPS2 violation for a ten minute period without having to deploy any additional resources. System operators would require 10-minute reserves to bring the system back into tolerable L10 range to avoid negatively impacting the system frequency. In instances where the system ACE experiences a large deviation it is possible for system operators to use 1-minute regulating reserves to bring the system back into balance.

To maintain a 95 percent CPS2 compliance monthly average for the year in 2020 APS would need to carry and deploy, on average, 81 MW of incremental regulation up and 81 MW of incremental regulation down reserves during hours when the solar is potentially operating.

Figure 4-1 below depicts the interdependence of the load and solar forecasting error to the open loop10

ACE. In certain time periods the load and solar forecast error offset and keep the ACE low. In other periods the ACE is high because the load and solar forecast errors are both moving in directions that make the ACE worse.

10 Open loop ACE is the Area Control Error of the system before AGC dispatch signals are deployed to correct for ACE. Closed loop ACE is the Area Control Error after AGC dispatch signals have deployed to correct for ACE.



Figure 4-1 Incremental Regulating Reserves and ACE

4.2.1 Year 2020 Reserve Requirements

APS applied the methodology used by Black & Veatch to develop the incremental monthly reserve requirements necessary to maintain the CPS2 standards at 90 percent, 95 percent and 99 percent. Table 4-1 provides the monthly reserve requirements for different levels of CPS2 compliance during daylight hours, while Figure 4-2 depicts the inter-temporal reserve requirements for 2020. The incremental 10 minute reserve requirement applies only to hours when solar generating output is available.

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Table 4-1 2020 Monthly 10-minute PV Reserves (+/- MW)

YEAR 2020 BASE SOLAR VARIABILITY CASE

HIGH SOLAR VARIABILITY CASE

Month 90% 95% 99% 90% 95% 99%

Jan 117 136 133 119 133 133

Feb 95 94 103 97 92 94

Mar 137 140 139 141 141 144

Apr 126 122 221 133 145 221

May 50 57 96 55 62 102

Jun 31 52 75 35 62 70

Jul -4 15 53 -3 20 53

Aug 7 24 61 10 25 61

Sep 25 40 68 28 41 73

Oct 74 88 140 76 85 145

Nov 128 111 95 133 116 96

Dec 107 91 90 107 89 90

Monthly Avg

74 81 106 78 84 107

June-Sept Avg

15 33 64 18 37 64

Figure 4-2 10-minute Reserve Requirement by Month in Year 2020 (+/-MW) The amount of incremental regulating reserves required to integrate solar is less in the summer compared to other seasons during the year. This is the result of two factors. First, in the absence of incremental PV APS requires more reserves during the summer because loads are highest during this time and have greater variability. The higher amounts of reserves used to balance out ACE deviation caused by load forecasting errors also aids in solar integration. Second, there is less cloud coverage during summer months11

11 May through September averages 20 clear days in Phoenix, while the rest of the year averages 16. See http://www.public.asu.edu/~aunjs/ClimateofPhoenix/wxpart4.htm#sun3

which leads to less solar variability in the model, reducing

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the need for reserves. The algorithm used to estimate variability in the solar datasets used the presence of clouds as the trigger for whether or not to add variability for a given 10 minute period. The net solar dataset shows the highest standard deviation between 10 minute periods from January to April, with relatively low variability from May to August. Review of actual 10 minute datasets provided by APS show mixed variability levels depending on location; since the model algorithm did not distinguish between partially cloudy and full cloudy days, this leads to months with full clear days (summer) having the least variation. The assumptions made in the model are conservative, likely producing more variation in the non-summer months than may actually exist. As can be seen from Table 4-1, the “High Variability” case did not lead to a significantly greater amount of CPS2 violations in any of the cases. While the reasons are not entirely clear, this is likely due to three factors. First, the anticipated geographic diversity of the PV resources greatly smoothes out the aggregate generation profile. Referring back to Figure 2-3, this shows that for the aggregated solar profile in 2020 in one of the most volatile months (January), the average change from nameplate capacity over a 10 minute period is 2 to 3 percent. If the level of variation included in the dataset increases by 50 percent (as was assumed in the High Variability case), the maximum impact is modest (an increase in roughly 1 percent of nameplate, or 10 MW). However, this maximum impact assumes that the variation in each solar project is in the same direction, which is not the case. This leads to the second factor, that although individual project variation is now greater, the geographic diversity leads to some projects varying in the up direction while others vary downwards, eliminating some of the net impact. Finally, in modeling the High Variability solar datasets, additional time periods were included where clouds, and hence variability, is present. This does not necessarily lead to more CPS2 violations than the Base Case, since it is the level of variation, not the number of time periods where variation is present that is most important when calculating CPS2 violations. The level of reserves calculated in the Base Case may be sufficient to handle the majority of new variation created in High Variability time periods where there originally were none.

4.2.2 Year 2030 Reserve Requirements

The reserve requirements for 2030 are approximately 60 percent greater than the 2020 requirements at 99 percent CPS2 on an annual basis, consistent with the increase in solar PV. The summer requirements do increase at a greater rate, but still represent a small portion of the total installed PV capacity. Table 4-2 provides the monthly reserve requirements during daylight hours for different levels of CPS2 compliance, while Figure 4-3 depicts the inter-temporal reserve requirements throughout the year.



Table 4-2 2030 Monthly 10-minute PV Reserves (+/- MW)

YEAR 2030 BASE SOLAR VARIABILITY CASE HIGH SOLAR VARIABILITY CASE

Month 90% 95% 99% 90% 95% 99%

1 190 212 200 192 214 203

2 167 162 186 162 164 186

3 222 228 246 224 228 241

4 214 229 300 223 256 297

5 108 116 153 111 120 164

6 73 99 115 83 120 121

7 22 47 96 23 52 93

8 41 57 101 39 60 104

9 61 89 124 66 91 128

10 129 161 191 130 154 192

11 214 193 177 216 195 182

12 176 162 125 176 162 127

Monthly Avg 135 146 168 137 151 170

June-Sept Avg 49 73 109 53 81 111

Figure 4-3 10-minute Reserve Requirement by Month in Year 2030 (+/-MW)

As in the 2020 model runs, the “High Variability” case did not lead to a significantly greater amount of CPS2 violations in any of the cases for the same reasons listed in Section 4.2.1.

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4.3 RESERVE COST METHODLOGY

As PV output changes due to variations in insolation or cloud movements, the system operator must maintain sufficient resources to provide both for upward regulation (in the case of PV output decline) and downward regulation (in case of PV output spiking). The cost to maintain and provide the operating reserves includes capacity and energy costs that reflects the actual change in system operating costs.

4.3.1 Reserve Incremental Capacity Costs

The capacity value for reserves includes the cost of procuring resources capable of providing the reserves or alternatively, the value of withholding generation capacity being used for reserves that may otherwise be used by the utility or system operator for other purposes. The capacity value may be determined in a variety of ways, and should reflect the operating characteristics of the Balancing Authority, local energy product markets and the resource and market opportunities available to the utility. The FERC OATT Schedule 3, Rate Schedule 3 (Regulation and Frequently Response Service) determines capacity values in a cost-based approach, where the reserve value is a pro-rata portion of the total system capacity resource cost (based on the percentage of resources providing regulation and frequency response). A competitive market approach, such as with the CAISO, will value the capacity at the price to procure these services in a transparent, bid-based market. A third approach is to value a specific identified incremental resource that will provide the regulation service (or opportunity cost in the event the user is withholding capacity from the market).

For this analysis, Black & Veatch and APS assumed that the resource providing the incremental reserve capacity is a GE LMS100 aero-derivative gas-fired peaking generator. APS anticipates developing many of these units within its planning horizon to accommodate load growth and to provide firming capacity for solar variability since these units have the operating characteristics to allow them to be used for reserve requirements. Since these resources are already planned for development, APS anticipates no incremental cost to it in order to provide the necessary regulation capacity. Further, APS does not foresee opportunities to sell this capacity in the market, since it is providing replacement capacity reserves for the system in addition to providing regulating reserves. Accordingly, this analysis assumed no additional capacity cost to integrate solar PV into the APS system.

4.3.2 Reserve Energy Costs

In addition to the cost of reserve capacity there are also associated energy costs related to providing the reserves. Units that can provide 10-minute reserve service will increase and decrease generating output through AGC in order to match real time generation with the real time load. APS has a fleet of generators that can provide 10-minute reserve service. In order to minimize costs to the utility, the generators are dispatched in the most economic way to meet energy and ancillary service requirements. To capture the energy cost component of the incremental reserves three production costs model runs were constructed for each scenario.

Run Base Case with no incremental 10-minute reserve requirement

Run Change Case #1: Increase load in every hour when the PV is available by the calculated regulating reserve up amount.



Run Change Case #2: Decrease load in every hour when the PV is available by the calculated regulating reserve down amount.

The objective of these three production cost runs is to estimate the system energy cost differential using all units that are available to 10-minute reserves. The production cost runs were performed by APS utilizing the 2012 IRP database with assistance from Black & Veatch. A large penetration of PV will require both carrying the incremental 10-minute reserves and deployment of these resources to correct for ACE deviations outside of the L10. The production cost runs with modified loads are designed to capture the incremental energy required for ramping up and ramping down the system. Under normal load and operating conditions, the cost of ramping up on the system is higher than ramping down on the system because ramping up moves the supply curve into more expensive units (such as peaking simple cycle turbines). Change Case #1 and #2 assume that the amount of energy regulation up and down will be exactly the same because the magnitude of the forecast error is the same in both directions.

4.3.3 Spinning Reserve Costs NERC requires that at least 50 percent of regulating reserves are spinning reserves, resources that are operating and synchronized to the grid but not providing energy. The cost to maintain this capacity is called the spinning reserve cost for this analysis. This may be considered as part of the energy cost but is calculated independently in the production cost model. The integration cost reflected in the results from spinning reserve is based on different reserves than what is required in the base case.

4.4 COST SUMMARY As discussed above, the cost of energy required to integrate the anticipated solar was developed for the base case and the sensitivity cases, including the load variability and gas price sensitivities. Following are the costs for each of these cases.

4.4.1 Base Case

The results for the Base Case are shown in Table 4-3. The cost of providing additional regulating reserves for expected PV generation on the APS system is substantially lower than what other studies of solar integration costs conducted for different balancing authorities and utilities have suggested, with estimated costs in the $2.00/MWh range in 2020 and $3.00/MWh range in 203012

12 Cost include in this report are all in nominal dollars.

. This lower cost is a result of several factors. First, the marginal cost to provide reserve energy is highest during summer months but incremental requirements are low during this period. The cost of energy is lower during shoulder months when there is greater need for reserves for PV integration. Further, the marginal generation resource providing the reserves in this analysis is typically an efficient natural gas-fired combustion turbine. Gas costs are currently very low and are projected to remain at low levels in the future, thus the marginal cost of reserves energy is low. Finally, this analysis only considered the energy cost portion of the reserves requirements. The cost of the reserve capacity is accounted for in APS revenue requirements and is not included in this analysis.



Table 4-3 10-minute PV Reserve Cost, Base Variability

YEAR 2020 2030

Solar Capacity, MW 1,038 1,669

Solar Energy, GWh/Year 2,293 3,603

CPS2 Compliance 90% 95% 99% 90% 95% 99%

Incremental Spinning Reserves, $000 3,280 3,461 4,339 8,047 8,664 9,893

Incremental Regulation Energy, $000 233 262 441 704 949 1,054

Total Integration Costs, $000 3,513 3,723 4,781 8,751 9,613 10,947

Incremental Spinning Reserves, $/MWh 1.43 1.51 1.89 2.23 2.40 2.75

Incremental Regulation Energy, $/MWh 0.10 0.11 0.19 0.20 0.26 0.29

Total Integration Costs, $/MWh 1.53 1.62 2.08 2.43 2.67 3.04

4.4.2 High Variability Scenario The costs for the High Variability case do not vary greatly from the Base Case estimates. This is due to the small difference between the number of CPS2 violations seen in each case, as discussed in Section 4.2.1. Table 4-4 details the costs for the High Variability case in 2020 and 2030.

Table 4-4 10-minute PV Reserve Cost, High Variability

YEAR 2020 2030



CPS2 Compliance 90% 95% 99% 90% 95% 99%


Incremental Regulation Energy, $000 241 283 443 835 1,004 1,049





4.4.3 Gas Price Scenario As discussed above, the marginal generation resource providing the reserves in this analysis is typically a natural gas-fired combustion turbine causing the integration costs to be sensitive to changes in natural gas prices. The gas price modeled by APS in the Year 2020 base case production simulation was $5.82/MMbtu. To test the impact of changes in gas prices on the marginal energy and reserve costs, a sensitivity analysis was completed that assumed a 30 percent increase in the



price of natural gas, to $7.56/MMbtu. Tables 4-5and 4-6 detail the impact of the high gas prices both on the base case and high volatility scenario. The marginal cost of integrating solar is less than the 30 percent increase in gas costs, with an approximate increase of 22 percent in 2020 and 15 percent in 2030 at 99 percent CSP2. This equates to increases of roughly $0.25 to $0.50/MWh, depending on the time period and level of CPS2 compliance.

Table 4-5 30% Higher Gas Prices & Base Solar Variability

Table 4-6 30% Higher Gas Prices & High Solar Variability

YEAR 2020 2030



CPS2 Compliance 90% 95% 99% 90% 95% 99%


Incremental Regulation Energy, $000 287 338 532 1,004 1,206 1,268





YEAR 2020 2030



CPS2 Compliance 90% 95% 99% 90% 95% 99%


Incremental Regulation Energy, $000 275 311 529 847 1,139 1,274






BLACK & VEATCH CORPORATION |Summary of Results and Recommendations 5-1

5 Summary of Results and Recommendations

5.1 RESULTS

The addition of PV resources will increase APS system 10-minute operating reserve requirements, but this is not expected to substantially increase costs to APS for three reasons. First, the geographic diversity of many different PV projects located throughout Arizona will modulate the variability from solar PV generation when compared to the output of a single facility, limiting the total regulation requirements. Second, APS requires more reserves during the summer because loads are highest during this time and have greater variability. The higher amounts of reserves used to balance out deviations caused by load forecasting errors also aids in solar forecasting errors. Finally, the model has low variability in PV output solar output during summer months due to a greater number of clear days based on meteorological data, although future analysis should use more actual data to refine the predictions.

By 2030, when 1,669 MW of PV is installed, daylight hour 10-minute reserves are anticipated to be between 135 and 168 MW, with lower monthly requirements during summer months. As a summary, Table 5-1 depicts level of 10-minute regulating reserves required for 2020 and 2030, both on an annual basis and during summer periods only. Lower summer reserves likely leads to lower cost relative to studies that do not distinguish reserves by month.

Table 5-1 Daylight Hour 10-Minute Reserve Requirement

CPS2 COMPLIANCE LEVEL (PERCENT)

YEAR 2020 YEAR 2030

Annual Summer Annual Summer

90 74 15 135 49

95 81 33 146 73

99 106 64 168 109

The energy cost can be parsed into the dispatch costs, reflecting movements in the system energy output to accommodate the PV, and spinning reserve costs, which account for the increased cost to commit resources to ensure that there are sufficient resources on line to meet dispatch requirements. The value of this is captured in the production cost simulation, independent of the energy costs. The net costs to APS under all scenarios are detailed on Table 5-2.



Table 5-2 Summary of Reserve Costs

2020 2030

Solar MW 1,038 1,669

Solar GWh 2,293 3,602

CPS2 Compliance % 90% 95% 99% 90% 95% 99%

Incremental Spinning Reserves ($/MWh) 1.43 1.51 1.89 2.23 2.40 2.75

Incremental Energy Costs ($/MWh) 0.10 0.11 0.19 0.20 0.26 0.29

Solar Integration Costs ($/MWh) 1.53 1.62 2.08 2.43 2.67 3.04

Other Study Findings In addition to developing estimates of 10-minute reserve requirements and costs, Black & Veatch noted several additional findings that are relevant to the operation of the APS system with substantial amounts of PV capacity in place.

• Distribution of PV Resources System-Wide Dampens Output Variability - While PV resources may ramp very quickly, the distribution of PV resource across the APS service territory will modulate the impact of this on the APS system. The impacts of clouds and monsoons on PV output will vary from one location to another within the service territory, but the aggregate of this volatility will likely reduce the overall impact on the system. Locations which have high concentrations of PV over a small geographic area will not see as much smoothing of their local net output profile; this will be the case for the local grids in the Phoenix, Gila Bend, and Yuma areas based on the projected locations for future solar PV development.

• Resource Scheduling for PV Ramping in High Penetration Cases – An issue identified during the process is that there will be extraordinary ramps of PV generation in 2030, particularly on clear days. For instance, with 1,669 MW on the grid in 2030, a ramp-up in the mid-morning and ramp-down in mid-afternoon may exceed 400 MW/hour. Intra-hour scheduling for other resources may be required to accommodate these ramps to avoid CPS2 violations.

• PV Production Forecasting – This analysis used persistence forecasting for PV to develop hourly forecasts of expected generation. Persistence forecasting is currently considered “best practice” in the industry though we observed that this method consistently overestimates generation during the hour of peak PV generation. Future forecast methods that improve upon a persistence forecast should be able to cut this level of reserves, and hence, integration costs.

• Little Change in Costs in High Variability Solar Sensitivity Case: The “High Variability” case did not lead to a significantly greater amount of CPS2 violations. This is likely due to three main reasons. First, the large amount of geographic diversity greatly smoothes out variation from solar PV projects. Second the geographic diversity leads to some projects varying in the up direction while others vary downwards, eliminating some of the net impact. Finally, in modeling the High Variability solar datasets, additional time periods were included where variability is present. This does not necessarily lead to more CPS2 violations than the Base Case, since it is the



level of variation, not the number of time periods where variation is present, that is most important when calculating CPS2 violations.

• Little Absolute Change in Costs in Due to Gas Prices - The marginal generation resource providing the reserves in this analysis is typically a natural gas-fired combustion turbine. This causes the results to be very sensitive to changes in natural gas prices, which are historically very volatile. The gas price modeled by APS in the base case production simulation for 2020 was $5.82/MMBTU. To test the impact of changes in gas prices on the marginal energy and reserve costs, an sensitivity analysis was completed that assumed a 30 percent increase in the price of natural gas, to $7.56/MMBTU. The impact of gas prices on the marginal cost of integrating solar is less than the 30 percent increase in gas costs, with an approximate increase of 22 percent in 2020 and 15 percent in 2030. This equates to increases of roughly $0.25 to $0.50/MWh, depending on the time period and level of CPS2 compliance.

5.2 RECOMMENDATIONS

5.2.1 Need for Actual, Time-Synchronized Data This analysis relied on a mix of resource variability data from small operating PV facilities of various vintages and modeled projections of resource output using typical year insolation conditions. As larger PV facilities are installed and generating in the APS service territory (such as Hyder and Cotton Center), this analysis should be updated with actual operation information from those facilities. This time-synchronized output from a variety of PV resources will provide an enhanced understanding of the impact of geographic diversity on operating reserves. Further, the data from current-generation PV technologies will refine the understanding of the variability from individual large-scale facilities.

5.2.2 Forecasting Accuracy This analysis did not look at improvements over the simple solar forecasting method outlined in Section 2. Future forecast methods that improve upon a persistence forecast should be able to cut this level of reserves and hence, integration costs. For example, the forecast methodology often over predicts output at and near the peak likely due to the difference between the sun’s ramp and the profiles typically seen in new solar projects built today. Modifications in the forecasting to reflect this is an example of logic that could be easily changed if this is causing significant errors to improve forecast accuracy and reduce integration costs.

When there is a large amount of solar energy integrated in the system, such as is forecasted in 2030, using one estimate for an entire hour will be inherently inaccurate even on sunny days. With 1,669 MW on the grid in 2030, sunny day ramps in the morning and afternoon are roughly 400 MW/hour. If the average for this hour is used in the forecast, the beginning and end of the hour will be off by 200 MW, which could very likely lead to CPS2 violations even if the net output of solar is perfectly understood. Changes in operations may be required when this much solar is on the system to maintain reliability.

esig€¦ · this report was prepared for arizona public service company (aps) by black &...

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