[Federal Register Volume 87, Number 107 (Friday, June 3, 2022)]
[Notices]
[Pages 33775-33786]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-11965]


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

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.
---------------------------------------------------------------------------

    \1\ The attendee registration form is located at https://www.surveymonkey.com/r/SHFLFKV.
---------------------------------------------------------------------------

    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 (202) 502-8659.
    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 (202) 502-8659 (TTY), or send a fax to (202) 208-2106 
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, (202) 502-6601, [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

[[Page 33776]]

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

[[Page 33777]]

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.

[[Page 33778]]

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

[[Page 33779]]

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

[[Page 33780]]

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

[[Page 33781]]

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.

[[Page 33782]]

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)

[[Page 33783]]

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

[[Page 33784]]

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

[[Page 33785]]

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

[[Page 33786]]

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


