Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 33775 

1The attendee registration form is located at 

https://www.surveymonkey.com/r/SHFLFKV. 

issuance of this notice: (1) A copy of the 

water quality certification; (2) a copy of 

the request for certification, including 

proof of the date on which the certifying 

agency received the request; or (3) 

evidence of waiver of water quality 

certification. Please note that the 

certification request must comply with 

40 CFR 121.5(b), including 

documentation that a pre-filing meeting 

request was submitted to the certifying 

authority at least 30 days prior to 

submitting the certification request. 

Please also note that the certification 

request must be sent to the certifying 

authority and to the Commission 

concurrently. 

Dated: May 26, 2022. 

Kimberly D. Bose, 

Secretary. 

[FR Doc. 2022–11867 Filed 6–2–22; 8:45 am] 

BILLING CODE 6717–01–P 

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. 

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 FERCOnlineSupport@ferc.gov, 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 accessibility@ferc.gov 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, 

Sarah.McKinley@ferc.gov. 

Alexander Smith (Technical 

Information), Office of Energy Policy 

and Innovation, (202) 502–6601, 

Alexander.Smith@ferc.gov. 

Dated: May 27, 2022. 

Kimberly D. Bose, 

Secretary. 

Technical Conference: Increasing RealTime 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 


khammond on DSKJM1Z7X2PROD with NOTICESEN03JN22.020</GPH>
33776 Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 

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 BurnerEquipped 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 

Que ́bec (Montre ́al, Canada) 

Audun Botterud, Principal Research 

Scientist, Massachusetts Institute of 

Technology (Cambridge, MA) 

Magnus Korpa ̊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 


khammond on DSKJM1Z7X2PROD with NOTICES
Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 33777 

Energy Laboratory (Golden, CO) 

Integrated Modeling Framework For 

Multi-energy Systems’ Planning 

Violette Berge, Vice President, Artelys 

Canda Inc. (Montre ́al, Canada) 

Tobias Bossmann, Project Director, 

Artelys Canada Inc. (Montre ́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 SecurityConstrained 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. 


khammond on DSKJM1Z7X2PROD with NOTICES
33778 Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 

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 multiperiod 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 


khammond on DSKJM1Z7X2PROD with NOTICES
Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 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 


khammond on DSKJM1Z7X2PROD with NOTICES
33780 Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 

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 

Que ́bec (Montre ́al, QC) 

Dr. Audun Botterud, Principal Research 

Scientist, Massachusetts Institute of 

Technology (Cambridge, MA) 

Dr. Magnus Korpa ̊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 zerocarbon 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 


khammond on DSKJM1Z7X2PROD with NOTICES
Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 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 realtime 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/ISOmanaged 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/ISOmanaged wholesale power markets 

primarily take the form of RTO/ISOdispatchable 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 swingcontract 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. 


khammond on DSKJM1Z7X2PROD with NOTICES
33782 Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 

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 temperaturedependent 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 temperatureoutage 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. (Montre ́al, 

Canada) 

Dr. Tobias Bossmann, Project Director, 

Artelys Canada Inc. (Montre ́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 coexistence 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 (socalled 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) 


khammond on DSKJM1Z7X2PROD with NOTICES
Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 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 


khammond on DSKJM1Z7X2PROD with NOTICES
33784 Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 

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 timeconsuming. 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 unitlevel 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 gridenhancing 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 


khammond on DSKJM1Z7X2PROD with NOTICES
Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 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 securityconstrained 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 SecurityConstrained 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 AgencyEnergy, 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 


khammond on DSKJM1Z7X2PROD with NOTICES
33786 Federal Register / Vol. 87, No. 107 / Friday, June 3, 2022 / Notices 

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 

EXECUTIVE OFFICE OF THE 

PRESIDENT 

Request for Information to Make 

Access to the Innovation Ecosystem 

More Inclusive and Equitable 

AGENCY: White House Office of Science 

and Technology Policy (OSTP). 

ACTION: Notice of Request for 

Information (RFI). 

SUMMARY: The White House Office of 

Science and Technology Policy (OSTP), 

on behalf of the National Science and 

Technology Council (NSTC) Lab-toMarket (L2M) Subcommittee, seeks 

information to improve inclusive and 

equitable access to Federal programs 

and resources by broadly engaging 

stakeholders in the U.S. innovation 

ecosystem. The public input provided 

in response to this RFI will inform 

OSTP and NSTC on work with Federal 

agencies and other stakeholders to 

improve existing programs and/or 

develop new programs to improve 

inclusive and equitable access in the 

Federally-funded research and 

development-driven sector. 

DATES: Interested persons and 

organizations are invited to submit 

responses on or before 5:00 p.m. ET on 

July 5, 2022. 

ADDRESSES: Responses should be 

submitted electronically to 

LabtoMarketRFI@ostp.eop.gov and 

include ‘‘L2M RFI Response’’ in the 

subject line of the email. Due to time 

constraints, mailed paper submissions 

will not be accepted, and electronic 

submissions received after the deadline 

cannot be ensured to be incorporated or 

taken into consideration. 

Instructions: Response to this RFI is 

voluntary. Each responding entity 

(individual or organization) is requested 

to submit only one response. 

Respondents need not reply to all 

questions listed. Responses must not 

exceed 6 pages in 12 point or larger font, 

with a page number provided on each 

page. Responses should include the 

name of the person(s) or organization(s) 

filing the comment, as well as the 

respondent type (e.g., academic 

institution, advocacy group, 

professional society, community-based 

organization, industry, trainee/student, 

member of the public, government, 

other). Respondent’s role in the 

organization may also be provided (e.g., 

researcher, faculty, student, 

administrator, program manager, 

journalist) on a voluntary basis. 

Comments containing references, 

studies, research, and other empirical 

data that are not widely published 

should include copies or electronic 

links of the referenced materials. Please 

be aware that comments submitted in 

response to this RFI, including the 

submitter’s identification (as noted 

above), may be posted on OSTP’s 

website or otherwise released publicly. 

OSTP, therefore, requests that no 

business proprietary information, 

copyrighted information, or personally 

identifiable information be submitted in 

response to this RFI. 

In accordance with FAR 15.202(3), 

responses to this notice are not offers 

and cannot be accepted by the Federal 

Government to form a binding contract. 

Additionally, those submitting 

responses are solely responsible for all 

expenses associated with response 

preparation. 

FOR FURTHER INFORMATION CONTACT: For 

additional information, please direct 

questions to Kylie Gaskins at 

LabtoMarketRFI@ostp.eop.gov or 202– 

456–4444. 

SUPPLEMENTARY INFORMATION: Our 

nation’s people are rich with diverse 

experiences. However, there is 

tremendous untapped science, 

technology, engineering, and 

mathematics (STEM) innovative 

potential throughout the nation. 

Demographic and socioeconomic groups 

in every geographic region of the 

country are full of talent that should 

have access to Federal programs and 

resources that afford them opportunities 

to contribute to the nation’s innovation 

enterprise. There is ample evidence that 

our nation’s potential in the arenas of 

innovation and entrepreneurship can be 

enhanced by engagement with the 

untapped talent of people who belong to 

groups that have historically been and 

are currently underrepresented. 

Through this RFI, the L2M 

Subcommittee seeks input from the 

public to identify and better understand: 

(1) Barriers that prevent innovators from 

underrepresented groups or 

underserved communities from 

participating in the innovation 

ecosystem; (2) Recommendations of 

methods to include and meet the 

specific needs of innovators from 

underrepresented backgrounds and 

communities to increase their 

participation in the innovation 

ecosystem; and (3) Examples of 

government programs or initiatives 

which have seen success in supporting 


khammond on DSKJM1Z7X2PROD with NOTICES