[Federal Register Volume 87, Number 107 (Friday, June 3, 2022)]
[Notices]
[Pages 33775-33786]
[FR Doc No: 2022-11965]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Docket No. AD10-12-013]

Increasing Market and Planning Efficiency Through Improved
Software; Supplemental Notice of Technical Conference on Increasing
Real-Time and Day-Ahead Market and Planning Efficiency Through Improved
Software

    As first announced in the Notice of Technical Conference issued in
this proceeding on February 24, 2022, Commission staff will convene a
technical conference on June 21, 22, and 23, 2022 to discuss
opportunities for increasing real-time and day-ahead market and
planning efficiency of the bulk power system through improved software.
Attached to this Supplemental Notice is a final agenda for the
technical conference and speakers' summaries of their presentations.
    While the intent of the technical conference is not to focus on any
specific matters before the Commission, some conference discussions
might include topics at issue in proceedings that are currently pending
before the Commission, including topics related to capacity valuation
methodologies for renewable, hybrid, or storage resources. These
proceedings include, but are not limited to:

PJM Interconnection, L.L.C. Docket No. EL21-83-000
California Independent System Operator Corp. Docket No. ER21-2455-000
New York Independent System Operator, Inc. Docket No. ER21-2460-000
ISO New England, Inc. Docket No. ER22-983-000
PJM Interconnection, L.L.C. Docket No. ER22-962-000
Southwest Power Pool, Inc. Docket No. ER22-1697-000
Midcontinent Independent System Operator, Inc. Docket No. ER22-1640-000
ISO New England, Inc. Docket No. EL22-42-000
Southwest Power Pool, Inc. Docket No. ER22-379-000
PJM Interconnection, L.L.C. Docket No. ER22-1200-000

    The conference will take place virtually via WebEx, with remote
participation from both presenters and attendees. Further details on
remote attendance and participation will be released prior to the
conference. Attendees must register through the Commission's website on
or before June 10, 2022.\1\ WebEx connections may not be available to
those who do not register.
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    \1\ The attendee registration form is located at https://www.surveymonkey.com/r/SHFLFKV.
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    The Commission will accept comments following the conference, with
a deadline of July 29, 2022.
    There is an ``eSubscription'' link on the Commission's website that
enables subscribers to receive email notification when a document is
added to a subscribed docket(s). For assistance with any FERC Online
service, please email [email protected], or call (866) 208-
3676 (toll free). For TTY, call<PHONE_NUMBER>.
    FERC conferences are accessible under section 508 of the
Rehabilitation Act of 1973. For accessibility accommodations please
send an email to [email protected] or call toll free (866) 208-
3372 (voice) or<PHONE_NUMBER> (TTY), or send a fax to<PHONE_NUMBER>
with the required accommodations.
    For further information about these conferences, please contact:

Sarah McKinley (Logistical Information), Office of External Affairs,
(202) 502-8004, [email protected].
Alexander Smith (Technical Information), Office of Energy Policy and
Innovation,<PHONE_NUMBER>, [email protected].

    Dated: May 27, 2022.
Kimberly D. Bose,
Secretary.
[GRAPHIC] [TIFF OMITTED] TN03JN22.020

Technical Conference: Increasing Real-Time and Day-Ahead Market
Efficiency Through Improved Software

Agenda

AD10-12-013

June 21-23, 2022

Tuesday, June 21, 2022

10:45 a.m. Introduction
Thomas Dautel, Federal Energy Regulatory Commission (Washington, DC)

Tuesday, June 21, 2022

11:00 a.m. Session T1
Enhancing Energy Assessment for ISO New England
    Jinye Zhao, Principal Analyst, ISO New England (Holyoke, MA)
    Tongxin Zheng, Director, ISO New England (Holyoke, MA)
    Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
    Song Zhang, Lead Analyst, ISO New England (Holyoke, MA)
    Thomas Knowland, Manager, ISO New England (Holyoke, MA)
    Mallory Waldrip, Lead Energy Security Analyst, ISO New England
(Holyoke, MA)
Cascading analysis for bulk power system operations
    Slava Maslennikov, Technical Manager, ISO New England (Holyoke, MA)
    Xiaochuan Luo, Manager, ISO New England (Holyoke, MA)
    Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
    Tongxin Zheng, Director, ISO New England (Holyoke, MA)
Transmission Outage Predictions to Improve Operational Resilience and
Situation Awareness
    Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
    Xiaochuan Luo, Manager, ISO New England (Holyoke, MA)
    Slava Maslennikov, Technical

Manager, ISO New England (Holyoke, MA)
    Tongxin Zheng, Director, ISO New England (Holyoke, MA)
12:30 p.m. Lunch
1:30 p.m. Session T2
Improving uncertainty management through ancillary service products
    Yonghong Chen, Consulting Advisor, MISO (Carmel, IN)
Benefit Evaluation of Multi-period Market Clearing
    Jinye Zhao, Principal Analyst, ISO New England (Holyoke, MA)
    Tongxin Zheng, Director of Advanced Technology Solutions, ISO New
England (Holyoke, MA)
    Jiachun Guo, Principal Analyst, ISO New England (Holyoke, MA)
    Dane Schiro, Lead Analyst, ISO New England (Holyoke, MA)
Flexible Ramping Product Enhancements
    Guillermo Bautista Alderete, Director of Market Analysis and
Forecasting, California ISO (Folsom, CA)
Co-optimization of Reserve Requirements and Scheduling with Energy and
Transmission Security
    Matthew Musto, Technical Specialist, New York ISO and Hitachi
Energy (Rensselaer, NY)
    Edward O. Lo, Consultant, Hitachi Energy (Rensselaer, NY)

Tuesday, June 21, 2022

3:30 p.m. Break
4:00 p.m. Session T3
Jointly-Owned Unit Modeling
    Tomas Tinoco De Rubira, Sr Power Systems Engineer--Development,
California ISO (Folsom, CA)
    Yannick Degeilh, Senior Power Systems Engineer, California ISO
(Folsom, CA)
Better Operating Reserves Modeling to Accommodate Duct Burner-Equipped
Combined Cycle Generators
    John Meyer, Senior Energy Market Engineer, New York ISO
(Rensselaer, NY)
    Iiro Harjunkoski, Researcher, Hitachi Energy (Mannheim, Germany)
Energy Storage Resource Modeling Enhancements in CAISO Markets
    Khaled Abdul-Rahman, Vice President of Power Systems and Market
Technology, California ISO (Folsom, CA)
    Tomas Tinoco De Rubira, Sr Power Systems Engineer--Development,
California ISO (Folsom, CA)
    Gabe Murtaugh, storage Sector Manager, California ISO (Folsom, CA)
Maintain Grid Reliability from Operations Planning to Real-time
    Pengwei Du, Supervisor--Resource Forecasting and Analysis, ERCOT
(Taylor, TX)
6:00 p.m. Adjourn

Wednesday, June 22, 2022

9:45 a.m. Introduction
10:00 a.m. Session W1
Practical challenges with the large penetration of Energy Storage
Resources including SOC optimization, Pricing, Ancillary Services and
Hybrid modeling within Production Costing software
    Brian Thomas, Principal Engineer, PowerGEM LLC (Clifton Park, NY)
    Boris Gisin, President, PowerGEM LLC (Clifton Park, NY)
Impact of Market Bidding and Dispatch Model over Energy Storage
Utilization
    Bolun Xu, Assistant Professor, Columbia University (New York, NY)
    Ningkun Zheng, Research Assistant, Columbia University (New York,
NY)
    Joshua Jaworski, Research Assistant, Columbia University (New York,
NY)
    Gabe Murtaugh, Storage Sector Manager, California ISO (Folsom, CA)
Market design and cost recovery in a simple 100% RES system: Analytical
insights
    Guillaume Tarel, Engineer, Hydro Qu[eacute]bec (Montr[eacute]al,
Canada)
    Audun Botterud, Principal Research Scientist, Massachusetts
Institute of Technology (Cambridge, MA)
    Magnus Korp[aring]s, Professor, Norwegian University of Science and
Technology (Trondheim, Norway)
11:30 p.m. Lunch
12:30 p.m. Session W2
Key concepts to promote operational flexibility: Comparison of
approaches and recommendations
    Erik Ela, Program Manager, Electric Power Research Institute
(Denver, CO)
    Phil de Mello, Senior Technical Leader, Electric Power Research
Institute (Davis, CA)
    Nikita Singhal, Technical Leade, Electric Power Research Institute
(Palo Alto, CA)
    Ben Hobbs, Pofessor, Johns Hopkins University (Baltimore, MD)
    Mahdi Mehrtash, Assistant Research Professor, Johns Hopkins
University (Baltimore, MD)
    James Kim, Energy Policy Project Scientist, Lawrence Berkeley
National Laboratory (Berkeley, CA)
    Miguel Heleno, Research Scientist, Lawrence Berkeley National
Laboratory (Berkeley, CA)
Price Formation in Zero-Carbon Electricity Markets: A Review of
Challenges and Solutions
    Zhi Zhou, Principal Computational Scientist, Argonne National
Laboratory (Lemont, IL)
    Audun Botterud, Principal Energy System Engineer, Argonne National
Laboratory (Lemont, IL)
    Todd Lovin, Team Lead, Argonne National Laboratory (Lemont, IL)
Risk-Aware Wind Bids with Distributed Optimization and Central Dispatch
    Daniel Shen, Graduate Student, Massachusets Institute of Technology
(Cambridge MA)
    Marija Ilic, Senior Research Scientist, Massachusets Institute of
Technology (Cambridge, MA)
Impacts of Multi-Interval Real-Time Dispatch on Generator Investment
Incentives in PJM
    Sushant Varghese, Graduate Research Assistant, Pennsylvania State
University (State College, PA)
    Anthony Giacomoni, Lead Market Strategist, PJM Interconnection LLC
(Audubon, PA)
    Aravind Retna Kumar, Graduate Research Assistant, Pennsylvania
State University (University Park, PA)
    Shailesh Wasti, Graduate Research Assistant, Pennsylvania State
University (University Park, PA)
    Mort Webster, Professor, Pennsylvania State University (University
Park, PA)
Transitioning to Linked Swing-Contract Markets for Net-Zero 2050
    Leigh Tesfatsion, Research Professor of Economics, Courtesy
Research Professor of Electrical & Computer Engineering, Iowa State
University (Ames, IA)
3:00 p.m. Break
3:30 p.m. Session W3
Assessing energy adequacy through scenario development for extreme
events
    Aidan Tuohy, Program Manager, Electric Power Research Institute
(Chicago, IL)
    Eamonn Lannoye, Program Manager, EPRI Europe (Dublin, Ireland)
    Juan Carlos Martin, Senior Engineer, EPRI Europe, (Madrid, Spain)
    Erik Smith, Engineer/Scientist III, Electric Power Research
Institute (Palo Alto, CA)
Improving grid planning by modeling correlated generator failures
    Dr. Sinnott Murphy, Research Engineer, National Renewable

Energy Laboratory (Golden, CO)
Integrated Modeling Framework For Multi-energy Systems' Planning
    Violette Berge, Vice President, Artelys Canda Inc.
(Montr[eacute]al, Canada)
    Tobias Bossmann, Project Director, Artelys Canada Inc.
(Montr[eacute]al, Canada)
5:00 p.m. Adjourn

Thursday, June 23, 2022

9:45 a.m. Introduction
10:00 a.m. Session H1
Real-Time Demand Response Market Co-Optimized with Conventional Energy
Market
    Bala Venkatesh, Professor and Director, Ryerson University
(Toronto, Ontario)
    Jessie Ma, Research Fellow, Centre for Urban Energy, Ryerson
University (Toronto, Ontario)
Electricity retail rate design in a decarbonizing power system: an
analysis of time-of-use pricing
    Tim Schittekatte, Postdoctoral Associate, Massachusetts Institute
of Technology (Cambridge, MA)
    Dharik Mallapragada, Research Scientist, Massachusetts Institute of
Technology (Cambridge, MA)
    Richard Schmalensee, Professor of Economics, Emeritus,
Massachusetts Institute of Technology (Cambridge, MA)
    Paul Joskow, Professor of Economics, Emeritus, Massachusetts
Institute of Technology (Cambridge, MA)
Improving Software to Allow End-users to Drive Impactful Procurement
Decisions
    Bryn Baker, Senior Director, Policy Innovation, Clean Energy Buyers
Association (Washington, DC)
Latent distribution system flexibility offers bulk power system
opportunities
    Philip Court, Product and Company Strategist, Ecogy Energy
(Brooklyn, NY)
12:00 p.m. Lunch
1:00 p.m. Session H2
Using E3's RESERVE Machine Learning Model to Advance the Calculation of
Subhourly Ancillary Services Needs in Deeply Renewable Grids
    Arne Olson, Senior Partner, Energy and Environmental Economics,
Inc. (San Francisco, CA)
    John Stevens, Senior Managing Consultant, Energy and Environmental
Economics, Inc. (San Francisco, CA)
    Jimmy Nelson, Associate Director, Energy and Environmental
Economics, Inc. (San Francisco, CA)
    Yuchi Sun, Senior Consultant, Energy and Environmental Economics,
Inc. (San Francisco, CA)
Synergistic Integration of Machine Learning and Mathematical
Optimization for Unit Commitment
    Jianghua Wu, PhD student, University of Connecticut (Storrs, CT)
    Peter B. Luh, Professor, University of Connecticut (Storrs, CT)
    Yonghong Chen, Senior Engineer, Midcontinent ISO (Carmel, IN)
    Bing Yan, Assistant Professor, Rochester Institute of Technology
(Rochester, NY)
    Mikhail A. Bragin, Research Assistant Professor, University of
Connecticut (Storrs, CT)
Congestion and Overload Mitigation using Optimal Transmission
Reconfigurations--Experience in MISO and SPP
    Pablo A. Ruiz, CEO and CTO, NewGrid, Inc. (Somerville, MA)
    Paola Caro, Principal Engineer, NewGrid, Inc. (Somerville, MA)
    Mitchell Myhre, Manager--Transmission Planning and Regulatory
Relations, Alliant Energy (Madison, WI)
    Rodica Donaldson, Senior Director--Transmission Strategy &
Analytics, EDF Renewables (San Diego, CA)
    Xiaoguang Li, Director of Product, NewGrid, Inc. (Somerville, MA)
Demonstration of Potential Data/Calculation Workflows Under FERC Order
No. 881's Ambient-Adjusted Rating (AAR) Requirements
    Lisa Sosna, Economist, FERC (Washington, DC)
    Tom Dautel, Deputy Director, Division of Economic and Technical
Analysis, FERC (Washington, DC)
    Ken Fenton, Physical Scientist, Global Systems Laboratory, National
Oceanic and Atmospheric Administration (Boulder, CO)
3:00 p.m. Break
3:30 p.m. Session H3
GO Competition Challenge 2: Analysis and Lessons Learned
    Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory` (Baltimore, MD)
    Stephen Elbert, Computational Scientist, Pacific Northwest National
Laboratory (Richland, WA)
    Arun Veeramany, Data Scientist, Pacific Northwest National
Laboratory (Richland, WA)
    Hans Mittelmann, Professor, Arizona State University (Tempe, AZ)
    Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)
GO Competition Challenge 3: Goals and Formulation
    Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)
    Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory (Baltimore, MD)
    Stephen Elbert, Advisor, Pacific Northwest National Laboratory
(Richland, WA)
Solving GO competition ACOPF problems
    Daniel Bienstock, Professor, Columbia University (New York, NY)
    Richard Waltz, Senior Scientist, Artelys, Inc. (Chicago, IL)
A Profit Maximizing Security-Constrained IV-AC Optimal Power Flow &
Global Solution
    Amro M. Farid, Visiting Associate Professor, MIT Mechanical
Engineering (Cambridge, MA)
ABSCoRES, managing risk and uncertainty on electricity systems using
Banking Scoring and Rating methodologies
    Alberto J. Lamadrid L., Associate Professor, Lehigh University
(Bethlehem, PA)
5:30 p.m. Adjourn

Conference Abstracts

Session T1 (Tuesday, June 21, 11:00 a.m., WebEx)

Enhancing Energy Assessment for ISO New England
Dr. Jinye Zhao, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Tongxin Zheng, Director, ISO New England (Holyoke, MA)
Dr. Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Song Zhang, Lead Analyst, ISO New England (Holyoke, MA)
Mr. Thomas Knowland, Manager, ISO New England (Holyoke, MA)
Mrs. Mallory Waldrip, Lead Energy Security Analyst, ISO New England
(Holyoke, MA)

    ISO New England performs a 21-day energy assessment providing an
energy supply outlook given anticipated power system conditions of the
region. The assessment takes into consideration major risk factors such
as fuel supply and inventory, weather forecast and electricity demand.
It was developed to improve situational awareness for the ISO and New
England's market participants about regional energy adequacy. This
presentation focuses on improvements to the modeling, process, and
software of the 21-day energy assessment, enhancing solution efficiency
and performance. Future improvements will also be discussed.

Cascading Analysis for Bulk Power System Operations
Dr. Slava Maslennikov, Technical Manager, ISO New England (Holyoke, MA)
Dr. Xiaochuan Luo, Manager, ISO New England (Holyoke, MA)
Dr. Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Tongxin Zheng, Director, ISO New England (Holyoke, MA)

    Clean energy transition is shifting the bulk power system operating
paradigm from reliability-centered deterministic approaches to risk-
based methods. Cascading analysis is one of the practical ways to
facilitate such transition as it tries to assess the potential load and
generation losses caused by an initiating contingency. Such a system
impact measure is more informative than the traditional thermal and
voltage violations estimated from conventional contingency analysis and
could be more efficiently used for situational awareness and system
risk mitigation. ISO New England has developed both the online and
offline cascading analysis process for real-time system operation and
planning. The online application runs every few minutes and evaluates
system impact of higher order contingencies to supplement Real-Time
Contingency Analysis. The offline application assesses the operational
risk under different scenarios representing the variability and
uncertainty of renewables as well as extreme weather conditions.
Cascading analysis has also a potential to greatly increase the
efficiency of outage coordination. This presentation discusses details
and use cases of the cascading analysis under the operational time
frame.
Transmission Outage Predictions to Improve Operational Resilience and
Situation Awareness
Dr. Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Xiaochuan Luo, Manager, ISO New England (Holyoke, MA)
Dr. Slava Maslennikov, Technical Manager, ISO New England (Holyoke, MA)
Dr. Tongxin Zheng, Director, ISO New England (Holyoke, MA)

    The northeastern U.S. has been frequently visited by harsh wintry
and tropical storms that result in transmission outages due to
precipitation, high winds, lighting and icing. In collaboration with
the University of Connecticut Eversource Energy Center, ISO New England
has been conducting real-time transmission outage prediction studies
using the machine learning (ML) techniques to support situation
awareness in real-time operation. Historic weather, transmission
facility and topography, and transmission outage data are used to train
the jointly-developed ML model. Outcome of the ML algorithms is further
combined with mechanistic simulation results (fragility curves) that
reflect extreme and rare conditions. Our early studies have produced
promising results that will further improve with the availability of
more collected data. The developed algorithms are being implemented in
our Online Weather Look-ahead Study (OWLS) tool. OWLS performs look-
ahead weather monitoring and transmission risk assessment to assist
operational decision against extreme weather events.

Session T2 (Tuesday, June 21, 1:30 p.m., WebEx)

Improving Uncertainty Management Through Ancillary Service Products
Dr. Yonghong Chen, Consulting Advisor, MISO (Carmel, IN)

    This presentation discusses recent work at MISO to improve
ancillary service product design based on quantified uncertainties
under different timeframe. Up ramp capability product requirement and
demand curve are derived based on risks under normal and contingency
conditions. The seasonal and hourly short term reserve requirements are
derived with machine learning clustering algorithm based on aggregated
real time uncertainties and real time commitment distributions. Similar
approach is applied to derive seasonal and hourly sub-regional
uncertainty events. It'll also give a brief introduction of on-going
and future work on quantifying and predicting risks across operational
timeframe with existing and upcoming resource mixes.
Benefit Evaluation of Multi-period Market Clearing
Dr. Jinye Zhao, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Tongxin Zheng, Director of Advanced Technology Solutions, ISO New
England (Holyoke, MA)
Dr. Jiachun Guo, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Dane Schiro, Lead Analyst, ISO New England (Holyoke, MA)

    Intertemporal constraints are inherent to almost all the resources
participating in electricity markets. Currently, many electricity
markets employ a sequential single-period market clearing process which
does not fully recognize the intertemporal linkages among different
market intervals. An efficient multi-period market clearing approach
has been drawing attention recently due to its capability of
simultaneously scheduling and pricing a market with multiple time
intervals while respecting market coupling. This presentation discusses
the differences between the two market clearing methods and presents a
quantitative analysis of the multi-period approach on the ISO New
England markets. An in-house market simulator was used to perform such
analysis by using 2019 market data. The results demonstrate the
benefits of the multi-period approach in terms of system reliability
improvement, social surplus gain and uplift payment reduction.
Flexible Ramping Product Enhancements
Dr. Guillermo Bautista Alderete, Director of Market Analysis and
Forecasting, California ISO (Folsom, CA)

    The integration of renewable resources in the CAISO system requires
market mechanisms to deal with the inherent uncertainty arising from
the variability of load as well as wind and solar resources. The
flexible ramping product is a market product that procures the ramp
capability to address this uncertainty. This requires the CAISO to
estimate uncertainty in both the upward and downward directions.
Currently, CAISO utilizes a statistical methodology with historical
uncertainty to assess procurement requirements. In this presentation,
CAISO introduces an enhanced methodology, using a quantile calculation,
to estimate uncertainty based on both historical uncertainty and
forecasts of load as well as wind and solar output.
Co-Optimization of Reserve Requirements and Scheduling With Energy and
Transmission Security
Mr. Matthew Musto, Technical Specialist, NYISO and Hitachi Energy
(Rensselaer, NY)
Mr. Edward O. Lo, Consultant, Hitachi Energy (Rensselaer, NY)

    With increasing variable resources in the generation mix, the need
for more economic responsiveness and flexibility is growing. The NYISO
and Hitachi Energy have been working on advanced design and
optimization techniques for dynamically calculating reserve
requirements based upon generation and transmission contingencies; as
part of the overall system production minimization cost objective. This
presentation will discuss initial design criteria as well as forward
looking design and prototype efforts to ensure

grid reliability. Topics include, use cases in the New York grid where
dynamic reserves procurement can be applied as well as highlighting the
complexities in formulation required to efficiently co-optimize reserve
requirements with load/gen and transmission security.

Session T3 (Tuesday, June 21, 4:00 p.m., WebEx)

Jointly-Owned Unit Modeling
Dr. Tomas Tinoco De Rubira, Sr Power Systems Engineer--Development,
California ISO (Folsom, CA)
Dr. Yannick Degeilh, Senior Power Systems Engineer, California ISO
(Folsom, CA)

    Efficient electricity markets require mathematical models that
capture the physical and economic characteristics of resources. One
important type of resource is a jointly-owned unit. It represents a
physical generator that is owned and shared between multiple parties.
At CAISO, as part of a pilot project, we have developed a mathematical
model for representing such units and implemented the necessary market
extensions for integrating and utilizing these effectively in the
Energy Imbalance Market. This market software enhancement allows the
scheduling coordinators that manage the different ownership shares to
participate in the real-time financial markets independently, while
automatically ensuring the physical capabilities of the underlying unit
are not only respected but fully utilized. In this presentation, we
describe the model implemented and highlight the challenges, lessons
learned, and results of the pilot project.
Better Operating Reserves Modeling To Accommodate Duct Burner-Equipped
Combined Cycle Generators
Mr. John Meyer, Senior Energy Market Engineer, NYISO (Rensselaer, NY)
Dr. Iiro Harjunkoski, Researcher, Hitachi Energy (Mannheim, Germany)

    The New York Independent System Operator (NYISO), in conjunction
with Hitachi Energy, have been working on improvements to the
scheduling and conversion of Operating Reserves products as applied to
combined cycle generators equipped with Heat Recovery Steam Generator
(HRSG) supplemental firing systems. These generator configurations have
unique operating characteristics to consider in Energy and Operating
Reserves optimization that present some modeling challenges. This
presentation will discuss the challenges, review the approach to better
model the true physical capabilities of these units, and elaborate on
potential operational benefits identified during the concept
development.
Energy Storage Resource Modeling Enhancements in CAISO Markets
Dr. Khaled Abdul-Rahman, Vice President of Power Systems and Market
Technology, California ISO (Folsom, CA)
Dr. Tomas Tinoco De Rubira, Sr Power Systems Engineer--Development,
California ISO (Folsom, CA)
Mr. Gabe Murtaugh, Storage Sector Manager, California ISO (Folsom, CA)

    Organized electricity markets allow resource schedulers to bid a
price that varies over the operating range of the resource. These
operating ranges span from the minimum amount of power (MW) to the
maximum amount of power that the resource is physically able or rated
to generate at any point in time. Today, storage resources are becoming
more prevalent within organized electricity markets and have additional
physical constraints for operation compared to traditional resources.
Notably, storage has limitations on the amount of energy (MWh) that it
may store or discharge at any point in time. The California ISO is
developing a framework for a new storage model that will allow bidding
a price that varies over the operating range for energy--or state of
charge--rather than power. This will allow storage resources to more
closely convey true marginal costs of operation to the CAISO through
bids, which in turn will allow for a more optimal dispatch and better
resource performance.
Maintain Grid Reliability From Operations Planning to Real-Time
Dr. Pengwei Du, Supervisor--Resource Forecasting and Analysis, ERCOT
(Taylor, TX)

    This talk will present the operational reliability challenges at
ERCOT and recent developments to improve the grid reliability from
operations planning to real-time operaions.

Session W1 (Wednesday, June 22, 10:00 a.m., WebEx)

Practical Challenges With the Large Penetration of Energy Storage
Resources Including SOC Optimization, Pricing, Ancillary Services and
Hybrid Modeling Within Production Costing Software
Mr. Brian Thomas, Principal Engineer, PowerGEM LLC (Clifton Park, NY)
Dr. Boris Gisin, President, PowerGEM LLC (Clifton Park, NY)

    With rapid growth of Renewable and Battery Energy Storage System
(BESS) resources it becomes more important to study BESS resources
including hybrids in mid to long range Production Cost Modeling (PCM)
Studies. BESS state of charge (SOC) optimization models vary between
ISOs and PCM studies due to differences in SOC Management and how it is
currently implemented. This presentation describes the challenges with
modeling BESS in PCM environment including full SOC management model,
enforcements of SOC targets, SOC limits and pricing run challenges.
BESS resource can provide Ancillary services which makes it more
important to manage SOC for Energy and Ancillary services in an optimal
fashion and avoid infeasible operating conditions. Here we describe our
experience implementing the BESS SOC model for Energy and different
Ancillary products. BESS resources are essential to meet ramping
requirements in severely ramp constrained regions. However, this
requires pre-ramping algorithms in market clearing products to better
manage ramps. This presentation describes our experience with pre-
ramping modeling and possible solutions. This presentation also
describes the challenges and approaches to model Hybrid Plants (within
PCM studies) which is rapidly increasing in interconnection queues of
many regions.
Impact of Market Bidding and Dispatch Model Over Energy Storage
Utilization
Dr. Bolun Xu, Assistant Professor, Columbia University (New York, NY)
Mr. Ningkun Zheng, Research Assistant, Columbia University (New York,
NY)
Mr. Joshua Jaworski, Research Assistant, Columbia University (New York,
NY)
Mr. Gabe Murtaugh, Storage Sector Manager, California ISO (Folsom, CA)

    This talk analyzes how different dispatch models and bidding
strategies would affect the utilization of storage with various
durations in deregulated power systems. We use a dynamic programming
model to calculate the operation opportunity value of storage from
price predictions, and use the opportunity value result as a base for
designing market bids. We compare two market bidding and dispatch
models in single-period economic dispatch: a power bidding model and a
State of Charge-segment bidding model. We test the two storage dispatch
models, combined with different price predictions and storage
durations, using historical real-time price data from New York
Independent System Operator. We compare the utilization rate with
respect to results from perfect price forecast cases. Our result shows
that modeling storage bids as dependent on State of Charge in single-
period real-time

dispatch will provide around 5-10% of improvement in storage
utilization over all duration cases and bidding strategies, and higher
renewable share will likely improve storage utilization rate due to
higher occurrence of negative prices.
Market Design and Cost Recovery in a Simple 100% RES System: Analytical
Insights
Dr. Guillaume Tarel, Engineer, Hydro Qu[eacute]bec (Montr[eacute]al,
QC)
Dr. Audun Botterud, Principal Research Scientist, Massachusetts
Institute of Technology (Cambridge, MA)
Dr. Magnus Korp[aring]s, Professor, Norwegian University of Science and
Technology (Trondheim, Norway)

    Modern power systems should meet the three criteria of
affordability, security and sustainability. This largely explains why
renewable energy sources (RES), whose costs and performance have
improved dramatically during the last decades, are rapidly expanding.
However, RES generation remains dictated by weather condition because
of their very nature, and systems with very high shares of RES will
have to rely on various sources of flexibility such as demand-response,
interconnections, peakers and storage to balance supply and demand.
Moreover, most RES technologies have zero marginal cost, impacting
price formation in the electricity market. During this presentation, we
will show an analysis of a simplified 100% RES systems based on wind
generation and energy storage only. Using an analytical formulation
based on net load duration curves, we analyze the equilibrium
conditions for RES and storage. This leads to a discussion on how
short-term market prices could be shaped to allow cost minimization for
the system as a whole and cost recovery for market players.

Session W2 (Wednesday, June 22, 12:30 p.m., WebEx)

Key Concepts To Promote Operational Flexibility: Comparison of
Approaches and Recommendations
Dr. Erik Ela, Program Manager, Electric Power Research Institute (Palo
Alto, CA)
Dr. Phil de Mello, Senior Technical Leader, Electric Power Research
Institute (Davis, CA)
Dr. Nikita Singhal, Technical Leader, Electric Power Research Institute
(Palo Alto, CA)
Dr. Ben Hobbs, Professor, Johns Hopkins University (Baltimore, MD)
Dr. Mahdi Mehrtash, Assistant Research Professor, Johns Hopkins
University (Baltimore, MD)
Mr. James Kim, Energy Policy Project Scientist, Lawrence Berkeley
National Laboratory (Berkeley, CA)
Mr. Miguel Heleno, Research Scientist, Lawrence Berkeley National
Laboratory (Berkeley, CA)

    A variety of mechanisms are being proposed for promoting
operational flexibility for bulk power systems. These include dynamic
reserve requirements, flexibility products, extended sloped operating
reserve demand curves, market clearing tool enhancements, and advanced
participation models. The presentation will discuss key concepts for
promoting flexibility to improve reliability and economic efficiency
while aligning price signals with necessary operational decisions. It
will also describe some case studies that compare flexibility products
and operating reserve demand curves to describe their similarities and
how they can be effectively integrated in electricity market design.
Price Formation in Zero-Carbon Electricity Markets: A Review of
Challenges and Solutions
Dr. Zhi Zhou, Principal Computational Scientist, Argonne National
Laboratory (Lemont, IL)
Dr. Audun Botterud, Principal Energy System Engineer, Argonne National
Laboratory (Lemont, IL)
Dr. Todd Lovin, Team Lead, Argonne National Laboratory (Lemont, IL)

    Future power systems dominated by zero-carbon generation resources
may require significant revisions to electricity market designs to
ensure capacity adequacy and market efficiency. In this presentation,
we first conceptually outline key fundamentals underlying electricity
market design and price formation and briefly review current
operational practices in U.S. electricity markets. We then discuss a
set of potential market design challenges in a grid dominated by zero-
carbon resources with marginal cost profiles that differ compared to
traditional thermal resources. Next, we review electricity market
design solutions that have been proposed in the literature to ensure
market efficiency in zero-carbon systems, along with policies and
incentive schemes proposed or implemented to accelerate the transition.
We also briefly discuss ongoing revisions to the seven regional
electricity markets in the United States and review the intended goals
and potential challenges of different market design options. Finally,
taking hydropower resources as an example, we discuss the specific
implications for flexible resources in a future zero-carbon system. In
particular, we summarize the potential advantages and challenges that
hydropower resources may face when participating in a competitive
market framework dominated by resources with zero marginal costs or
zero fuel costs. We conclude by summarizing key observations and
establishing a set of research questions that should be addressed to
improve our understanding of market design, price formation, and market
efficiency in zero-carbon power systems.
Risk-Aware Wind Bids With Distributed Optimization and Central Dispatch
Mr. Daniel Shen, Graduate Student, Massachusetts Institute of
Technology (Cambridge MA)
Dr. Marija Ilic, Senior Research Scientist, Massachusetts Institute of
Technology (Cambridge MA)

    Grid operators must integrate ever increasing amounts of
stochastic, distributed generation in the form of wind and solar power.
On the consumer side, demand response will also become an important
component of grid operation. Unlike conventional fossil generation,
these assets have time- and state- varying capacities, ramp
constraints, and cost curves that add additional computation complexity
to centralized dispatch algorithms. We propose a distributed
optimization approach for dispatch that reduces the computation burden
of the ISO's centralized dispatch algorithm and opens the possibility
of running ACOPF for day-ahead and real-time dispatch. Key to our
approach is that assets bid in a manner that internalizes their own
operating constraints, instead of these constraints being part of the
central optimization problem. We demonstrate this distributed dispatch
on a NYISO 1576-bus system with risk-aware wind bids.
Impacts of Multi-Interval Real-Time Dispatch on Generator Investment
Incentives in PJM
Mr. Sushant Varghese, Graduate Research Assistant, Pennsylvania State
University (State College, PA)
Dr. Anthony Giacomoni, Lead Market Strategist, PJM Interconnection
(Audubon, PA)
Mr. Aravind Retna Kumar, Graduate Research Assistant, Pennsylvania
State University (University Park, PA)
Mr. Shailesh Wasti, Graduate Research Assistant, Pennsylvania State
University (State College, PA)

    Over the last several years, the PJM generation mix has shifted
with some traditional fossil fuel generators being

displaced by renewable resources. Given current state policy goals
within the PJM region, this shift is expected to accelerate over the
next several years. Most new renewable resources being built are wind
and solar generators, which are inherently intermittent in nature.
Absent large-scale deployments of new energy storage resources, one of
the current challenges of integrating large amounts of intermittent
renewable resources into the system is the provision of adequate intra-
hour ramp capability from controllable resources to account for
unexpected changes in their output. Ideally, the real-time market
clearing should both provide sufficient flexibility in its energy and
reserve schedules and revenues should reward the more flexible units
that provide the needed flexibility, thereby guiding future investment
decisions. Currently, PJM uses a single interval optimization that
looks ahead 8-10 minutes to the target time in its real-time security
constrained economic dispatch (RT-SCED). Given the short look-ahead
period, RT-SCED is not able to anticipate potential changes in
generation and load that may occur over subsequent intervals. One
potential solution that has been implemented in other Independent
System Operators (ISOs) is the use of a multi-interval real-time
dispatch with a longer look-ahead period. A multi-interval real-time
dispatch reduces system costs by optimally scheduling ramp capability
on the system by prepositioning controllable generators to handle
forecasted load and generation uncertainties. However, to date, all
ISOs that have implemented a multi-interval real-time dispatch use
single settlement procedures, in which prices are only set for the
first interval from the RT-SCED's time horizon. The prices in later
intervals from each model solution are advisory only. A question
remains about whether the revenues from this approach are biased
towards more or less flexible units. An alternative is a multi-
settlement approach, in which every cleared quantity and price for the
same demand interval from repeated model solutions are saved and all
are used in determining the final settlement. This presentation will
provide an overview of PJM's current dispatch practices in its Real-
Time Energy Market and will compare them to a multi-interval real-time
dispatch using both single- and multi-settlement approaches. Simulation
results using a real-time model of the PJM system with a rolling window
horizon will be presented. Results will compare the relative
differences in net revenues for each generation technology class, as
one indication of relative incentives for investment in more flexible
resources.
Transitioning to Linked Swing-Contract Markets for Net-Zero 2050
Dr. Leigh Tesfatsion, Research Professor of Economics, Courtesy
Research Professor of Electrical & Computer Engineering, Iowa State
University (Ames, IA)

    The need for flexible dependable reserve provision in electric
power systems has dramatically increased in recent years. Growing
reliance on volatile renewable power resources and greater
encouragement of more active demand-side participation has led to
greater uncertainty and volatility of net load. Consequently, system
operators are finding it harder to secure reserve with sufficient
dependability and flexibility to permit the continual balancing of net
load, a basic requirement for power system reliability. In this
presentation I reconsider the design of U.S. RTO/ISO-managed wholesale
power markets in light of these concerns. Four design principles are
stressed: (i) U.S. RTO/ISO-managed wholesale power markets must
necessarily be forward markets due to the speed of real-time
operations; (ii) Only one type of product can effectively be transacted
in U.S. RTO/ISO-managed wholesale power markets: Namely, reserve, an
insurance product offering availability of net-load balancing services
for future real-time operations; (iii) Net-load balancing services
offered into U.S. RTO/ISO-managed wholesale power markets primarily
take the form of RTO/ISO-dispatchable power-paths available for
possible dispatched delivery at designated grid locations during
designated future operating periods; (iv) All dispatchable power
resources should be permitted to compete for the provision of power-
paths in U.S. RTO/ISO-managed wholesale power markets without regard
for irrelevant underlying technological differences. If these four
principles are accepted, current trade and settlement arrangements for
U.S. RTO/ISO-managed wholesale power markets need to be fundamentally
altered. In this presentation I propose the transition to a new linked
swing-contract market design, consistent with principles (i)-(iv), that
could meet the future needs of U.S. RTO/ISO-managed wholesale power
markets better than currently implemented designs.

Session W3 (Wednesday, June 22, 3:30 p.m., WebEx)

Assessing Energy Adequacy Through Scenario Development for Extreme
Events
Dr. Aidan Tuohy, Program Manager, Electric Power Research Institute
(Chicago, IL)
Dr. Eamonn Lannoye, Program Manager, EPRI Europe (Dublin, Ireland)
Mr. Juan Carlos Martin, Senior Engineer, EPRI Europe (Madrid, Spain)
Dr. Erik Smith, Engineer/Scientist III, Electric Power Research
Institute (Palo Alto, CA)

    While power system adequacy studies have traditionally focused on
ensuring sufficient capacity is available to meet demand, recent events
and projected changes to the system have shown that having sufficient
energy as well as capacity is likely to become increasingly relevant.
This can come in the form of gas availability during extreme cold, the
likelihood of long periods of low wind and solar output, or energy
storage availability in batteries and other forms of limited duration
storage. As part of its ``Resource Adequacy for a Decarbonized Future''
initiative, EPRI has been examining how best to include energy adequacy
considerations into the larger set of probabilistic resource adequacy
metrics, such as loss of load expectation or expected unserved energy.
While extreme events are important to consider, they may occur in the
tails of the distribution and as such do not get attention in metrics
that average outage likelihood over long periods of time. EPRI is
currently working with its utility and ISO members on case studies
related to these issues and initial results will be presented here. In
this presentation, we will focus on a new tool, intended to be publicly
available once validated, that is used to develop scenarios for
adequacy studies. We will provide an overview of the modeling
approaches, including how vulnerability models are being developed for
each type of asset on the system, based on expert knowledge and
historical performance. This results in a set of asset risk models,
showing risk under different types of weather conditions. Such
information can then be combined with historical and projected weather
data to understand the periods when the system is most likely to be
energy limited. The outputs of the tool are thus scenarios related to
extreme events, that can then be studied using existing or under
development adequacy assessment tools.

Improving Grid Planning by Modeling Correlated Generator Failures
Dr. Sinnott Murphy, Research Engineer, National Renewable Energy
Laboratory (Golden, CO)

    Recent academic research has identified correlated generator
failures in the United States bulk power system, violating key
assumptions made in system planning. Subsequent work demonstrated
strong statistical relationships between generator outages and extreme
temperatures, with particularly large outages observed during winter
events. These temperature dependencies were then shown to be
consequential for both planning reserve margins and the procurement of
operating reserves. Unfortunately, standard resource adequacy modeling
software tools used by grid planners are incapable of representing
temperature-dependent outage rates and instead assume each generator's
average reliability over a historical period (e.g., five years)
reflects its risk during peak load conditions, when temperatures are
often at their most extreme. As a result, resource adequacy modeling
generally understates the capacity levels needed to achieve a desired
system reliability target. At the National Renewable Energy Laboratory,
grid modelers employ the open-source Probabilistic Resource Adequacy
Suite (PRAS) to perform resource adequacy assessments. Unlike most
tools, PRAS allows users to define time-varying asset outage and
recovery rates for all assets, including generators, storage resources,
and transmission lines. Researchers can thus use PRAS to conduct
adequacy assessments that are significantly more realistic than current
industry practice. This enables more accurate identification of today's
system capacity requirements as well as improved ability to assess and
mitigate reliability risks of future systems. This talk will: 1.
Present the empirical evidence of correlated failures in the U.S.; 2.
Introduce the PRAS model and some of the studies it has supported; 3.
Describe ongoing work to model temperature-outage relationships in the
U.S.; and 4. Describe novel resource adequacy workflows enabled by
PRAS.
Integrated Modeling Framework for Multi-Energy Systems' Planning
Mrs. Violette Berge, Vice President, Artelys Canada Inc.
(Montr[eacute]al, Canada)
Dr. Tobias Bossmann, Project Director, Artelys Canada Inc.
(Montr[eacute]al, Canada)

    For the past 7 years, Artelys has been developing the METIS model
on behalf of the European Commission's Directorate-General for Energy.
METIS is the European model that allows to develop scenarios for the
future of energy systems (electricity, gas, heat, etc). It enables to
address questions like impact assessment of European Union energy
policy proposals, cost benefit assessment of infrastructure projects,
assessment of the potential role for a technology. While the first
phase of the project consisted in developing the power and gas system/
market model, the second phase of the project focused on better
integrating distribution and transmission grids. Artelys developed a
similar integrated modeling framework for the American Northeastern
power grid, including Eastern Canadian provinces, New-York and New-
England grids for strategic studies. In this talk, Artelys will present
the METIS project and the American Northeastern model and discuss the
benefits of using such a modeling framework for energy and climate
policymaking.

Session H1 (Thursday, June 23, 10:00 a.m., WebEx)

Real-Time Demand Response Market Co-Optimized With Conventional Energy
Market
Dr. Bala Venkatesh, Professor and Director, Ryerson University
(Toronto, Ontario) Ms. Jessie Ma, Research Fellow, Centre for Urban
Energy, Ryerson University (Toronto, Ontario)

    In addition to procuring energy, consumers in electricity markets
procure demand response (DR) services. Demand and supply of energy in
the electricity market drives the demand for DR services. Through the
Net Benefits Test (NBT), economic procurement of DR is limited to an
amount that ensures that consumers benefit with the procurement of DR
services. However, the NBT neither (a) recognizes the co-existence of
the DR market with the energy market; nor (b) optimizes social welfare
in the DR market in concert with that of the energy market. This lack
of accounting for DR market surplus results in economic inefficiency.
To address this shortcoming, we advance past works by: (a) Proposing a
real-time DR market where the DR demand curve is a function of
opportunity in the energy market; and (b) co-optimizing energy and DR
markets such that the total social welfare derived from both markets is
maximized simultaneously. We also present an optimal power flow
formulation and process to implement our ideas in real-time electricity
markets. The formulation is tested on a simple test case and a system
based on actual PJM data. For the PJM case, total social welfare is
increased by 1.41% to 3.05% over existing DR procurement strategies,
resulting in $14.5M to $30.9M additional benefits per hour.
Electricity Retail Rate Design in a Decarbonizing Power System: An
Analysis of Time-of-Use Pricing
Dr. Tim Schittekatte, Postdoctoral Associate, Massachusetts Institute
of Technology (Cambridge, MA)
Dr. Dharik Mallapragada, Research Scientist, Massachusetts Institute of
Technology (Cambridge, MA)
Dr. Richard Schmalensee, Professor of Economics, Emeritus,
Massachusetts Institute of Technology (Cambridge, MA)
Dr. Paul Joskow, Professor of Economics, Emeritus, Massachusetts
Institute of Technology (Cambridge, MA)

    Increased electrification of heating and transport on the demand-
side and high rates of intermittent renewable uptake on the supply-side
increase the importance of retail electricity rates. Due to
acceptability issues with the first-best solution, i.e., retail rates
passing-through wholesale prices, alternatives are being proposed. An
important alternative is a time-of-use (TOU) tariff, possibly
reinforced by critical peak pricing (CPP). Trabish (2022) reports that
there were over 150 rate design policy initiatives in 2021 addressing
new time-of-use (TOU) or time-varying rate (TVR) structures in the
United States. TOU rates are predefined, e.g., a year ahead, and vary
according to fixed time blocks calibrated on historical data--see e.g.,
Faruqui and Sergici (2013). Typically, time blocks are differentiated
based on seasons, months, type of day (workdays or weekends), and/or
time of the day (so-called peak, shoulder, or off-peak hours). The idea
behind TOU rates is that consumers are to a certain extent exposed to
the time-varying conditions in wholesale electricity markets while
keeping rates predictable and protecting consumers from unexpected
price shocks. Most academics investigating the TOU tariffs emphasize
that such rates only capture a small fraction of welfare benefits when
compared with prices passing through the wholesale price (Hogan, 2014;
Borenstein, 2015; Jacobson et al., 2020). The metric of interest in
these studies in the correlation between TOU prices and realized
wholesale prices and/or they make the crucial assumption that demand is
modelled as having a constant, rather low, elasticity in each
(independent) hour. In our paper, we use data from different power
systems in the US (ERCOT, CAISO and ISO-NE)

for a period between 2010-2019 and we find indeed that the out-of-
sample correlations between TOU prices and the realized wholesale
prices are low. However, these correlations significantly improve when
leaving out the unpredictable scarcity prices in the train and test
data. More importantly, we argue that a very large fraction of demand
response in the future will come in the form of ``load shifting''
rather than changes in load in independent hours. The major relevant
technologies in that regard are electric vehicles (EVs), heat pumps
(HPs) and air conditioning (ACs). The potential of TOU to induce
(beneficial) load shifting is not well captured by looking at
correlations. To estimate how effective TOU would be to shift load from
one time block to another ``in the right direction'', with the realized
wholesale price as a baseline, we propose to use the rank correlation
metric. We simulate demand shifting and demand reduction under realized
wholesale prices and under TOU prices. We show that show that rank
correlations are an appropriate predictor of beneficial load shifting
under TOU pricing. Conditional upon power system characteristics, TOU
tariffs can lead to a high proportion of the potentially ideal load
shifting volumes. We end the paper by discussing under what power
system conditions TOU tariffs can be a reasonable second best to
passing through wholesale prices and under what conditions this
statement does not hold anymore.
Improving Software to Allow End-Users To Drive Impactful Procurement
Decisions
Ms. Bryn Baker, Senior Director, Policy Innovation, Clean Energy Buyers
Association (Washington, DC)

    Energy customers, like corporates, government agencies, cities and
universities, are becoming increasingly sophisticated and bidirectional
in their interaction with the electricity grid (shifting loads,
providing demand response, making consumption and siting decisions
based on the grid profile) and they interested in driving greater
emissions impact through their procurement and operational decisions.
But these actions are hampered by lack of access to standardized,
transparent and reliable grid and greenhouse gas emission data. One of
the benefits of improving software for increased efficiency and
reliability of the bulk power system is that it can help to collect,
standardize and make available critical information to electricity
customers, among others, including about emissions and delivered
electricity profile. More granular, timely, and accurate grid and
emissions data are needed. Electricity customers utilize data to
perform carbon-optimized load shifting and accurately measure the
decarbonization performance of renewable energy projects and help site
those in the most impactful areas. Additionally, standardization across
regions would make information more widely accessible and comparable.
By improving software to increase market and planning efficiencies, it
will improve critical datasets for a range of end-users seeking
accessible, standardized, and accurate data from the grid.
Latent Distribution System Flexibility Offers Bulk Power System
Opportunities
Mr. Philip Court, Product and Company Stratigest, Ecogy Energy
(Brooklyn, NY)
Mr. John Gorman, Asset Manager, Ecogy Energy (Brooklyn, NY)
Ms. Twiggy Hamilton, Policy Research Analyst, Ecogy Energy (Brooklyn,
NY)
Mr. Joel Santisteban, Director of Platform, Ecogy Energy (Brooklyn, NY)

    The bulk power system exists to serve distribution systems. But
distribution systems are both consumers and service providers to the
bulk power system. It is flexibility in these distribution systems
which lets them behave as service providers. At a high level this
presentation is all about unused technical capability and associated
commercial desires in the distribution system and the opportunities
that these could unlock if they are unleashed and then leveraged. At a
lower level we will look at resources, either existing or proposed,
that are not being fully leveraged. There is opportunity here to
unleash flexibility that will be useful both within distribution
systems and ultimately for the bulk power system. If we can expose this
to date untapped flexibility and present it as a service to the bulk
power system, we can use this service to deliver additional reliability
and economic efficiencies. In this presentation we will define the
nature of the opportunity, roughly quantify the size of it, explore
what technology options can allow this to be achieved and finally what
policy changes may be needed to accelerate this opportunity.

Session H2 (Thursday, June 23, 1:00 p.m., WebEx)

Using E3's RESERVE Machine Learning Model To Advance the Calculation of
Subhourly Ancillary Services Needs in Deeply Renewable Grids
Mr. Arne Olson, Senior Partner, Energy and Environmental Economics,
Inc. (San Francisco, CA)
Dr. John Stevens, Senior Managing Consultant, Energy and Environmental
Economics, Inc. (San Francisco, CA)
Dr. Jimmy Nelson, Associate Director, Energy and Environmental
Economics, Inc. (San Francisco, CA)
Dr. Yuchi Sun, Senior Consultant, Energy and Environmental Economics,
Inc. (San Francisco, CA)

    Accurately forecasting wind and solar power output poses challenges
for deeply decarbonized electricity systems. Grid operators must commit
resources to provide reserves to ensure reliable operations in the face
of forecast errors, a process which can increase fuel consumption and
emissions. To help address these issues, E3 worked with the California
Independent System Operator (CAISO) under a grant from the ARPA-E
PERFORM program to develop E3's open-source RESERVE machine learning
model. This model expands the usefulness of median 15- and 5-minute
market point forecast data currently used by the CAISO to execute the
Western Energy Imbalance Market (EIM) by creating probabilistic
distributions of short-term uncertainty in demand, wind, and solar
forecasts that adapt to prevailing grid conditions. Machine learning-
derived estimates of forecast errors are found to compare favorably to
estimates based on incumbent methods. Reserves derived from machine
learning are usually smaller than values derived using incumbent
methods, which enables fuel savings during most hours. Machine learning
reserves are generally larger than incumbent reserves during times of
higher forecast error, potentially improving system reliability during
extreme events. E3 tested RESERVE's performance using multi-stage
production simulation modeling of the CAISO system. Machine learning
reserves provide production cost and greenhouse gas (GHG) emission
reductions of approximately 0.3% relative to historical 2019
requirements. Savings in the 2030 timeframe are highly dependent on
battery storage capacity. At lower levels of battery capacity, savings
of 0.4% from machine learning reserves are shown. Significant
quantities of battery storage are expected to be added to meet

California's resource adequacy needs and GHG reduction targets.
Addition of these batteries saturates reserve needs and results in
minimal within-hour balancing costs in 2030.
Synergistic Integration of Machine Learning and Mathematical
Optimization for Unit Commitment
Mr. Jianghua Wu, Ph.D. student, University of Connecticut (Storrs, CT)
Dr. Peter B. Luh, Professor, University of Connecticut (Storrs, CT)
Dr. Yonghong Chen, Senior Engineer, Midcontinent ISO (Carmel, IN)
Dr. Bing Yan, Assistant Professor, Rochester Institute of Technology
(Rochester, NY)
Dr. Mikhail A. Bragin, Research Assistant Professor, University of
Connecticut (Storrs, CT)

    Unit Commitment (UC) is important for power system operations. With
increasing challenges, e.g., growing intermittent renewables and intra-
hour net load variability, traditional mathematical optimization such
as branch-and-cut (B&C) could be time-consuming. Machine learning (ML)
is a promising alternative. Recently, multiple ``indirect'' ML methods
for UC problems have been presented, e.g., learning effective branching
strategies for B&C or removing inactive transmission constraints.
``Direct'' methods have also been explored, e.g., using graph neural
networks and reinforcement learning. In view of the combinatorial
nature of UC with an exponentially growing number of possible
solutions, these ML methods have difficulties for large problems in
terms of training data preparation and time required for training. To
this end, synergistic integration of ML and mathematical optimization
is explored by learning subproblems within our recent decomposition and
coordination framework of Surrogate Lagrangian Relaxation (SLR) for
deterministic UC problems. Compared to the original problem, a
subproblem is much easier to learn, and it only requires solutions to
be ``good enough'', i.e., feasible to unit-level constraints and
satisfying a convergence condition. Nevertheless, in view of many types
of constraints, finding ``good enough'' subproblem solutions is still
challenging. For simplicity, only system demand and unit initial
statuses are assumed changing across days. The set of units, unit
characteristics, and capacities of transmission lines are assumed
constant across days. Under these simplifying assumptions, a deep
neural network (DNN) of multilayer perceptron is adopted. For effective
learning, dimensionality reduction is accomplished by aggregating
Lagrangian multipliers and removing unnecessary variables. Moreover, an
innovative specification of multiplier distributions is explored for
effective training in the presence of binary decision variables.
Furthermore, a loss function considering target values and constraint
violations is designed for offline supervised training. After offline
training, DNNs are used to help solve subproblems in daily operations.
When facing patterns not yet learned, ML may not perform well, but
graceful degradation of these cases is achievable by using B&C as a
backup. Finally, to effectively exploit subproblem solutions available
from daily operations, online self-learning is considered as
supplementary learning. For ``positive'' cases which have good-enough
solutions from DNNs as targets, the learning process is similar to that
of offline learning. For ``negative'' cases which have no good-enough
targets, a loss function that considers the satisfaction of SLR's
convergence condition is innovatively developed, and this allows to
obtain gradient to update DNN weights. Offline supervised learning and
online self-learning are unified at the switching of the loss function.
Since ML is used for the first time to learn subproblem solutions, the
focus is to demonstrate the ability of ML to predict good-enough
subproblem solutions, as opposed to demonstrating the ability of SLR+ML
to solve large and practical UC problems. At this early stage, our goal
is not for our method to outperform B&C in terms of solution quality or
computation efficiency on low to medium-complexity problems.
Nevertheless, we are confident that for very complex UC problems, e.g.,
MISO's problem where B&C suffers from poor performance, the advantages
of SLR will be apparent, and the speed advantage of applying ML for
subproblem solving will be prominent. Although testing is limited to
the IEEE 118-bus system, results demonstrate that ML speeds up the
subproblem solving process of SLR while maintaining near-optimality of
the overall solutions. This speedup can be improved through continual
online self-learning. Our method thus opens a direction for integrating
ML and mathematical optimization to solve large and complicated UC and
beyond.
Congestion and Overload Mitigation Using Optimal Transmission
Reconfigurations--Experience in MISO and SPP
Dr. Pablo A. Ruiz, CEO and CTO, NewGrid, Inc. (Somerville, MA)
Ms. Paola Caro, Principal Engineer, NewGrid, Inc. (Somerville, MA)
Mr. Mitchell Myhre, Manager--Transmission Planning and Regulatory
Relations, Alliant Energy (Madison, WI)
Ms. Rodica Donaldson, Senior Director, Transmission Strategy &
Analytics, EDF Renewables (San Diego, CA)
Mr. Xiaoguang Li, Director of Product, NewGrid, Inc. (Somerville, MA)

    While the transmission grid configuration is continuously changing
due to planned and unplanned outages, the transmission flexibility
afforded by the existing circuit breakers is typically not used to
purposely adapt the grid configuration to best meet changing system
needs to mitigate overloads and congestion costs. At the same time,
transmission needs are becoming more variable and are increasing
rapidly to support the power system transition to integrate increasing
levels of variable renewable resources. Topology optimization software
is a grid-enhancing technology that identifies reconfiguration options
to re-route power flow around transmission bottlenecks employing less
utilized facilities and satisfying reliability criteria. These
reconfigurations provide cost savings to power customers and increases
the value of the existing transmission network as well as new
transmission projects, from both reliability and market efficiency
perspectives. This presentation will illustrate the flow relief,
transfer capability and cost saving impacts of using reconfigurations
to mitigate heavily congested constraints in MISO and SPP. A practical
path for the adoption of topology optimization technology will be
discussed.
Demonstration of Potential Data/Calculation Workflows Under FERC Order
No. 881's Ambient-Adjusted Rating (AAR) Requirements
Ms. Lisa Sosna, Economist, Federal Energy Regulatory Commission
(Washington, DC)
Mr. Tom Dautel, Deputy Director--Division of Economic and Technical
Analysis, Federal Energy Regulatory Commission (Washington, DC)
Mr. Ken Fenton, Physical Scientist, Global Systems Laboratory, National
Oceanic and Atmospheric Administration (Boulder, CO)

    FERC Order No. 881, Managing Transmission Line Ratings, requires
(among other things) that transmission providers use ambient-adjusted
transmission line ratings (AARs) that are updated hourly to reflect
ambient air temperature forecasts and the impact of solar heating
during daytime periods. In this presentation, Commission staff will

demonstrate one potential data/calculation workflow for implementing
the AAR requirements of Order No. 881. In the demonstrated approach,
NOAA weather forecasts from the National Blend of Models (NBM) and
calculated daytime solar intensity are used to calculate AAR line
ratings on the RTS-GMLC test system. Hourly ratings are inserted into a
ratings database to comply with the data retention requirements of
Order No. 881.

Session H3 (Thursday, June 23, 11:00 a.m., WebEx)

GO Competition Challenge 2: Analysis and Lessons Learned
Dr. Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory (Baltimore, MD)
Dr. Stephen Elbert, Computational Scientist, Pacific Northwest National
Laboratory (Richland, WA)
Dr. Arun Veeramany, Data Scientist, Pacific Northwest National
Laboratory (Richland, WA)
Dr. Hans Mittelmann, Professor, Arizona State University (Tempe, AZ)
Dr. Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)

    The Grid Optimization (GO) Competition Challenge 2 is nearly
finished. This competition focused on a security constrained AC optimal
power flow problem with fast start unit commitment, transmission
switching, and a detailed post-contingency model. The Final Event trial
finished in September 2021, and the Monarch of the Mountain ongoing
trial will finish in October 2022. This talk reviews the results so far
and presents some lessons learned regarding the impact of solver time
limits, the value and computational difficulty of model features like
transmission switching and flexible load, the challenges of working
with confidential industry data, and other outcomes of the competition.
GO Competition Challenge 3: Goals and Formulation
Dr. Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)
Dr. Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory (Baltimore, MD)
Dr. Stephen Elbert, Advisor, Pacific Northwest National Laboratory
(Richland, WA)

    The Grid Optimization (GO) Competition Challenge 3 has launched.
This talk gives an overview of the model formulation and the questions
we are aiming to address with it. The model includes multi-period unit
commitment; AC bus/branch modeling; scheduling of energy and reserves;
flexible loads; storage; and combined cycle generators. The model can
be configured for use in an ISO/RTO context for applications of real-
time (RT) look ahead, day ahead (DA) market clearing, and week ahead
(WA) advisory. The model combines features that are considered in
isolation in a sequence of models in current electricity industry
practice, for example solving a DA unit commitment model with little
regard for AC considerations, then solving an ACOPF with fixed
commitments closer to RT. With this combined model and the solvers that
competition entrants will develop, we want to ask and answer: Can the
combined model be solved to high accuracy in a reasonable amount of
time on practical instances? What are the incremental benefits to
society of the combined solution, relative to the sequential approach?
How will various industry trends, including increasing capacity of
variable and uncertain generation resources, distributed energy
resources, price sensitive load, and storage, affect the value of
advanced computational tools for grid optimization?
Solving GO Competition ACOPF Problems
Dr. Daniel Bienstock, Professor, Columbia University (New York, NY)
Dr. Richard Waltz, Senior Scientist, Artelys, Inc. (Chicago, IL)

    We describe the approach we deployed in the recent GO competition,
in which we placed #2 overall. The GO competition addressed security-
constrained Alternating Current Optimal Power Flow (ACOPF) problems in
a modern formulation. This formulation included a number of integer
variables used to model switching and transformer and shunt control.
Many of the instances were quite large and involved many scenarios;
additionally a strict time limit was involved. Our approach relied on
the Knitro solver and deployed a number of domain-reduction techniques
based on power engineering perspectives. We will describe our approach
and document some of our experimental outcomes.
A Profit Maximizing Security-Constrained IV-AC Optimal Power Flow &
Global Solution
Dr. Amro M. Farid, Visiting Associate Professor, Massachusetts
Institute of Technology (Cambridge, MA)

    Since its first formulation in 1962, the Alternating Current
Optimal Power Flow (ACOPF) problem has been one of the most important
optimization problems in electric power systems. Its most common
interpretation is a minimization of generation costs subject to network
flows, generator capacity constraints, line capacity constraints, and
bus voltage constraints. The main theoretical barrier to its solution
is that the ACOPF is a non-convex optimization problem that
consequently falls into the as-yet-unsolved space of NP-hard problems.
To overcome this challenge, the literature has offered numerous
relaxations and approximations of the ACOPF that result in
computationally suboptimal solutions with potentially degraded
reliability. While the impact on reliability can be addressed with
active control algorithms, energy regulators have estimated that the
sub-optimality costs the United States ~$6-19B per year. Furthermore,
and beyond its many applications to electric power system markets and
operation, the sustainable energy transition necessitates renewed
attention towards the ACOPF. This paper contributes a profit-maximizing
security-constrained current-voltage AC optimal power flow (IV-ACOPF)
model and globally optimal solution algorithm. More specifically, it
features a convex separable objective function that reflects a two-
sided electricity market. The constraints are also separable with the
exception of a set of linear network flow constraints. Collectively,
the constraints enforce generator capacities, thermal line flow limits,
voltage magnitudes, power factor limits, and voltage stability. The
optimization program is solved using a Newton-Raphson algorithm and
numerically demonstrated on the data from a transient stability test
case.
ABSCoRES, Managing Risk and Uncertainty on Electricity Systems Using
Banking Scoring and Rating Methdologies
Dr. Alberto J. Lamadrid L., Associate Professor, Lehigh University
(Bethlehem, PA)

    In this presentation we will discuss the advancements done over the
past year for a project funded by the Advanced Research Projects
Agency-Energy, ARPA-E, under the PERFORM program. We are developing an
Electric Assets Risk Bureau. Our framework allows to include asset and
system risk management strategies into the current electricity system
operations to improve economic efficiency, and include environmental
considerations. Our approach is based on three main tenets: (1) We
measure risk based on mathematical norms to calculate

Application of Banking Scoring and Rating for Coherent Risk Measures in
Electric Systems (ABSCoRES) ratings and scores; (2) we developed novel
data driven dispatch algorithms that integrate the ABSCoRES; (3) we
establish a strategy for the application of the scores, and open the
development of new products to mitigate incurred risks. We leverage
scoring and ratings from banking and financial institutions alongside
current optimization methods in dispatching power systems to help
system operators and electricity markets schedule resources. Our
approach is motivated by the observation that there are major
differences between the power scheduled by a system operator and the
actual power generated/consumed in real time. Moreover, the methodology
can be used to develop scores that provide signals in high impact-low
probability (HILP) events. Our framework counteracts two failures in
existing electricity system: (i) Frictions in knowledge of assets
(imperfect or asymmetric information regarding the risk they may induce
in the system) and (ii) missing mechanisms (or markets) for products to
mitigate risk incurred in the system. Generally having large
differences from the expected operating conditions, sometimes augmented
with unplanned contingencies, obeys to different reasons. We consider
these reasons as potential risk sources. There are various sources,
including: Increased participation of renewable energy generators and
the associated integration schemes across balancing areas, different
financial, environmental and risk preferences of power producers,
consumers, and aggregators (e.g., FERC Order Nos. 841 and 2222), loss
of inertia, distributed energy resources, inter-dependencies with other
systems and cybersecurity, and generally a more active demand side. Our
proposed methodologies will improve economic efficiency of assets in
the electricity system while recognizing limitations in assessing the
distribution of information uncertainties affecting agents
participating in these systems. A particularly attractive feature of
our approach is its connection to economic theory of decision making
under uncertainty. The trading of contingent claims in different states
of the world in an Arrow-Debreu Economy with complete markets allows
for full insurance coverage leading to a competitive equilibrium
output. While this is a theoretical benchmark, the score calculation
reduces the information asymmetries and can provide a way to better
coordinate different agents and stakeholders.

[FR Doc. 2022-11965 Filed 6-2-22; 8:45 am]
BILLING CODE 6717-01-P


From:
Raymond, Gabrielle
Sent:
Tuesday, January 26, 2021 4:29 PM
To:
Allen Dittenhoefer<EMAIL_ADDRESS>Jones, DonnaLee<EMAIL_ADDRESS>Cc:
Contact<EMAIL_ADDRESS>Knight, Jeffrey A<EMAIL_ADDRESS>Subject:
Other Emissions Spreadsheets: Method 303, Fugitive Pushing, Boilers, Flares, and Byproducts

Hello,

Thank you all very much for your 1/22/2021 round of review and comments.

Industry changes accepted, no additional EPA comments in any of the files.

Method 303: EPA updated ABC Coke charges/year values and Bluestone number of lids and offtakes values provided by COETF

Fugitive Pushing: EPA updated the non-CBI pushing capture efficiencies (received 1/9/21 ) for each battery and updated the AKS-Follansbee Batteries 1, 2, and 3 production values provided by COETF.

Boilers: EPA updated the 2015 Annual Potential Boiler Usage (mmscf/yr) for Plant H provided by COETF and will be using 2017 NEI data for Plant H Boiler 8.

Flares: EPA updated the flare types for Middletown, Warren, and Clairton, based on the modeling shell COETF comments. Revised calculation of average TPY emissions per pollutant to exclude the Clairton Emergency Flare. EPA agrees to not model the Emergency Flares.

Byproducts: EPA updated the individual HAP per byproduct plant based on the facility specific files provided by COETF. Revised calculation of average TPY emissions per pollutant

Lets stick a fork in these and call them done!!

Thank you again for all your reviews!
Gabrielle

Gabrielle Raymond
she/her/hers
RTI International
3040 Cornwallis Road
P.O. Box 12194
Research Triangle Park, NC 27709
Phone:<PHONE_NUMBER>

<< CokeOvens-Method303_01-25-2021.xlsx >>
<< Coke_Fugitive_Pushing_01-25-2021.xlsx >>
<< CokeOvens-Example_Emissions_Calc-Boiler-01-25-2021.xlsx >>
<< Coke_Flare_Emissions_1-26-2021.xlsx >>
<< Coke_Byproduct_Chemical Plant_Emissions_01-25-2021.xlsx >>


Comment Info: =================

General Comment:Public Hearing Comments for ZAP Xebra Recall Docket No. NHTSA-2012-0133


[Federal Register Volume 76, Number 155 (Thursday, August 11, 2011)]
[Notices]
[Pages 49810-49812]
[FR Doc No: 2011-20376]

-----------------------------------------------------------------------

SECURITIES AND EXCHANGE COMMISSION

[Release No. 34-65049; File No. SR-Phlx-2011-103]

Self-Regulatory Organizations; NASDAQ OMX PHLX LLC; Notice of
Filing and Immediate Effectiveness of Proposed Rule Change Relating to
Rebates and Fees for Adding and Removing Liquidity in Select Symbols

August 5, 2011.
    Pursuant to Section 19(b)(1) of the Securities Exchange Act of 1934
(``Act''),\1\ and Rule 19b-4 \2\ thereunder, notice is hereby given
that, on August 1, 2011, NASDAQ OMX PHLX LLC (``Phlx'' or ``Exchange'')
filed with the Securities and Exchange Commission (``SEC'' or
``Commission'') the proposed rule change as described in Items I, II,
and III below, which Items have been prepared by the Exchange. The
Commission is publishing this notice to solicit comments on the
proposed rule change from interested persons.
---------------------------------------------------------------------------

    \1\ 15 U.S.C. 78s(b)(1).
    \2\ 17 CFR 240.19b-4.
---------------------------------------------------------------------------

I. Self-Regulatory Organization's Statement of the Terms of Substance
of the Proposed Rule Change

    The Exchange proposes to amend its Complex Order Fees in Section I
of its Fee Schedule titled ``Rebates and Fees

for Adding and Removing Liquidity in Select Symbols.''
    The text of the proposed rule change is available on the Exchange's
Web site at http://nasdaqtrader.com/micro.aspx?id=PHLXRulefilings, at
the principal office of the Exchange, at the Commission's Public
Reference Room, and on the Commission's Web site at http://www.sec.gov.

II. Self-Regulatory Organization's Statement of the Purpose of, and
Statutory Basis for, the Proposed Rule Change

    In its filing with the Commission, the Exchange included statements
concerning the purpose of and basis for the proposed rule change and
discussed any comments it received on the proposed rule change. The
text of these statements may be examined at the places specified in
Item IV below. The Exchange has prepared summaries, set forth in
sections A, B, and C below, of the most significant aspects of such
statements.

A. Self-Regulatory Organization's Statement of the Purpose of, and
Statutory Basis for, the Proposed Rule Change

1. Purpose
    The purpose of the proposed rule change is to amend Section I, Part
B of the Exchange's Fee Schedule for Complex Orders. A Complex Order is
any order involving the simultaneous purchase and/or sale of two or
more different options series in the same underlying security, priced
at a net debit or credit based on the relative prices of the individual
components, for the same account, for the purpose of executing a
particular investment strategy. Furthermore, a Complex Order can also
be a stock-option order, which is an order to buy or sell a stated
number of units of an underlying stock or ETF coupled with the purchase
or sale of options contract(s).\3\
---------------------------------------------------------------------------

    \3\ See Exchange Rule 1080, Commentary .08(a)(i).
---------------------------------------------------------------------------

    The Exchange proposes to increase the current Customer Rebate for
Adding Liquidity with respect to Complex Orders for options overlying:
(i) Standard and Poor's Depositary Receipts/SPDRs (``SPY''); \4\ (ii)
the PowerShares QQQ Trust (``QQQ'')[supreg]; (iii) Apple, Inc.
(``AAPL''); (iv) iShares Russell 2000 Index (``IWM''); (v) Bank of
America Corporation (''BAC''); (vi) Citigroup, Inc. (''C''); (vii) SPDR
Gold Trust (''GLD''); (viii) Intel Corporation (``INTC''); (ix)
JPMorgan Chase & Co. (''JPM''); (x) iShares Silver Trust (''SLV'');
(xi) Financial Select Sector SPDR (''XLF''); and (xii) Ford Motor
Company (``F'') (taken together, ``Designated Options''). The Exchange
also proposes to waive the Customer Complex Order Fee for Removing
Liquidity of $0.25 per contract for the following symbols: BAC, C, GLD,
INTC, JPM, SLV, XLF and F. The Exchange believes that increasing the
Customer Complex Order Rebate to Add Liquidity and waiving the Customer
Complex Order Fee for Removing Liquidity for the symbols listed above,
respectively, would attract additional Customer order flow to the
Exchange.
---------------------------------------------------------------------------

    \4\ SPY options are based on the SPDR exchange-traded fund
(``ETF''), which is designed to track the performance of the S&P 500
Index.
---------------------------------------------------------------------------

    Currently, the Exchange pays a Customer Complex Order Rebate for
Adding Liquidity of $0.25 per contract in certain Select Symbols,
namely SPY, QQQ, AAPL and IWM. The Exchange currently pays a Customer
Complex Order Rebate for Adding Liquidity \5\ of $0.24 per contract in
all other Select Symbols, excluding SPY, QQQ, AAPL and IWM.\6\ The
Exchange also currently waives the Customer Complex Order Fee for
Removing Liquidity for options overlying SPY, QQQ, AAPL and IWM.\7\ The
proposal would increase the Customer Rebate for Adding Liquidity to
$0.26 per contract for all Designated Options.\8\ In addition, the
proposal would extend the current waiver of the Customer Complex Order
Fee for Removing Liquidity to include the following symbols: BAC, C,
GLD, INTC, JPM, SLV, XLF and F.
---------------------------------------------------------------------------

    \5\ The only market participant that receives a Rebate for
Adding Liquidity for Complex Orders today is a Customer.
    \6\ A list of all symbols subject to the Rebates and Fees for
Adding and Removing Liquidity are listed in Section I of the
Exchange's Fee Schedule and titled ``Select Symbols.''
    \7\ All other market participants are assessed a Fee for
Removing Liquidity today other than Customer Complex Orders in SPY,
QQQ, IWM and AAPL.
    \8\ This would result in a $0.01 per contract rebate increase
for SPY, QQQ, IWM and AAPL and a $0.02 per contract rebate increase
for BAC, C, GLD, INTC, JPM, SLV, XLF and F.
---------------------------------------------------------------------------

    Under this proposal, the Exchange will pay Customer Complex Orders
a Rebate for Adding Liquidity of $0.24 per contract, in any Select
Symbol, except the Designated Options. The Exchange will also assess
the Customer Complex Order Fee for Removing Liquidity of $0.25 per
contract, in any Select Symbol, except the Designated Options.
2. Statutory Basis
    The Exchange believes that its proposal to amend its Fee Schedule
is consistent with Section 6(b) of the Act \9\ in general, and furthers
the objectives of Section 6(b)(4) of the Act \10\ in particular, in
that it is an equitable allocation of reasonable fees and other charges
among Exchange members. The Exchange also believes that there is an
equitable allocation of reasonable rebates among Exchange members.
---------------------------------------------------------------------------

    \9\ 15 U.S.C. 78f(b).
    \10\ 15 U.S.C. 78f(b)(4).
---------------------------------------------------------------------------

    The Exchange believes that it is reasonable and equitable to only
pay a Complex Order Rebate for Adding Liquidity to Customers, as
compared to other market participants, because the Customer rebate will
attract Customer order flow to the Exchange for the benefit of all
market participants. Likewise, the Exchange believes that it is
reasonable to waive the Complex Order Fee for Removing Liquidity for
Customers transacting BAC, C, GLD, INTC, JPM, SLV, XLF and F, because
by waiving the fee, this also will attract Customer order flow to the
Exchange which in turn also benefits all market participants.
    The Exchange believes that these proposals are equitable and not
unfairly discriminatory because by paying an increased Rebate for
Adding Liquidity to Customers transacting Complex Orders in certain
symbols and waiving Fees for Adding Liquidity to Customers transacting
Complex Orders in certain additional symbols, all market participants
will benefit from the increased liquidity which increased Customer
order flow would bring to the Exchange.
    With respect to the Customer Complex Order Rebate for Adding
Liquidity the Exchange believes that it is reasonable to pay a
different rebate for transacting equity options in certain symbols and
with respect to the Customer Complex Order Fee for Removing Liquidity
the Exchange believes that it is reasonable to assess a different Fee
for Removing Liquidity in certain symbols. The Exchange currently pays
a different Customer Complex Order Rebate for Adding Liquidity and
assesses a different Customer Complex Order Fee for Removing Liquidity
in SPY, QQQ, IWM and AAPL as compared to other Select Symbols. Trading
in these Select Symbols is different from trading in other symbols in
that they are more liquid, have higher volume and competition for
executions is more intense. The Exchange believes the same rationale
applies in paying a different Customer Complex Order Rebate for Adding
Liquidity and assessing a different Customer Complex Order Fee for
Removing Liquidity in BAC, C, GLD, INTC, JPM, SLV, XLF and F in that
these symbols are also more liquid, have higher volume and competition
for executions is more intense.

    The Exchange believes that its proposal to pay a higher rebate for
transactions in equity options in the Designated Options, as compared
to the other Select Symbols, is equitable and not unfairly
discriminatory because the Exchange would uniformly pay the same
Customer Complex Order Rebate for Adding Liquidity for all Customer
Complex Orders in all Designated Options. The Exchange believes that
waiving the Customer Complex Order Fee for Removing Liquidity for the
following additional symbols: BAC, C, GLD, INTC, JPM, SLV, XLF and F is
equitable and not unfairly discriminatory because the Exchange is
uniformly waiving the Customer Complex Order Fee for Removing
Liquidity.
    The Exchange operates in a highly competitive market comprised of
nine U.S. options exchanges in which sophisticated and knowledgeable
market participants can readily send order flow to competing exchanges
if they deem fee levels at a particular exchange to be excessive. The
Exchange believes that the Complex Order fees and rebates it pays/
assesses must be competitive with fees and rebates in place on other
exchanges. The Exchange believes that this competitive marketplace
impacts the fees and rebates present on the Exchange today and
influences the proposals set forth above.

B. Self-Regulatory Organization's Statement on Burden on Competition

    The Exchange does not believe that the proposed rule change will
impose any burden on competition not necessary or appropriate in
furtherance of the purposes of the Act.

C. Self-Regulatory Organization's Statement on Comments on the Proposed
Rule Change Received From Members, Participants, or Others

    No written comments were either solicited or received.

III. Date of Effectiveness of the Proposed Rule Change and Timing for
Commission Action

    The foregoing rule change has become effective pursuant to Section
19(b)(3)(A)(ii) of the Act.\11\ At any time within 60 days of the
filing of the proposed rule change, the Commission summarily may
temporarily suspend such rule change if it appears to the Commission
that such action is necessary or appropriate in the public interest,
for the protection of investors, or otherwise in furtherance of the
purposes of the Act. If the Commission takes such action, the
Commission shall institute proceedings to determine whether the
proposed rule should be approved or disapproved.
---------------------------------------------------------------------------

    \11\ 15 U.S.C. 78s(b)(3)(A)(ii).
---------------------------------------------------------------------------

IV. Solicitation of Comments

    Interested persons are invited to submit written data, views, and
arguments concerning the foregoing, including whether the proposed rule
change is consistent with the Act. Comments may be submitted by any of
the following methods:

Electronic Comments

     Use the Commission's Internet comment form (http://www.sec.gov/rules/sro.shtml); or
     Send an e-mail to<EMAIL_ADDRESS>Please include
File Number SR-Phlx-2011-103 on the subject line.

Paper Comments

     Send paper comments in triplicate to Elizabeth M. Murphy,
Secretary, Securities and Exchange Commission, 100 F Street, NE.,
Washington, DC 20549-1090.

All submissions should refer to File Number SR-Phlx-2011-103. This file
number should be included on the subject line if e-mail is used. To
help the Commission process and review your comments more efficiently,
please use only one method. The Commission will post all comments on
the Commission's Internet Web site (http://www.sec.gov/rules/sro.shtml). Copies of the submission, all subsequent amendments, all
written statements with respect to the proposed rule change that are
filed with the Commission, and all written communications relating to
the proposed rule change between the Commission and any person, other
than those that may be withheld from the public in accordance with the
provisions of 5 U.S.C. 552, will be available for Web site viewing and
printing in the Commission's Public Reference Room, 100 F Street, NE.,
Washington, DC 20549, on official business days between the hours of 10
a.m. and 3 p.m. Copies of the filing also will be available for
inspection and copying at the principal office of the Exchange. All
comments received will be posted without change; the Commission does
not edit personal identifying information from submissions. You should
submit only information that you wish to make available publicly. All
submissions should refer to File Number SR-Phlx-2011-103 and should be
submitted on or before September 1, 2011.

    For the Commission, by the Division of Trading and Markets,
pursuant to delegated authority.\12\
---------------------------------------------------------------------------

    \12\ 17 CFR 200.30-3(a)(12).
---------------------------------------------------------------------------

Elizabeth M. Murphy,
Secretary.
[FR Doc. 2011-20376 Filed 8-10-11; 8:45 am]
BILLING CODE 8011-01-P