Notice2023-13168
Increasing Market and Planning Efficiency through Improved Software; Second Supplemental Notice of Technical Conference on Increasing Real-Time and Day-Ahead Market and Planning Efficiency Through Improved Software
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Published
June 21, 2023
Issuing agencies
Energy DepartmentFederal Energy Regulatory Commission
Full Text
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<title>Federal Register, Volume 88 Issue 118 (Wednesday, June 21, 2023)</title>
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[Federal Register Volume 88, Number 118 (Wednesday, June 21, 2023)]
[Notices]
[Pages 40234-40253]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2023-13168]
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DEPARTMENT OF ENERGY
Federal Energy Regulatory Commission
[Docket No. AD10-12-014]
Increasing Market and Planning Efficiency through Improved
Software; Second 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 7, 2023, Commission staff will convene a
technical conference on June 27, 28, and 29, 2023 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 Second Supplemental Notice is the 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:
[[Page 40235]]
PJM Interconnection, L.L.C., Docket No. EL21-83-000
California Independent System Operator Corp., Docket No. ER21-2455-004
New York Independent System Operator, Inc., Docket No. ER21-2460-003
ISO New England, Inc., Docket No. ER22-983-002
PJM Interconnection, L.L.C., Docket No. ER22-962-003
Southwest Power Pool, Inc., Docket No. ER22-1697-001
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
California Independent System Operator Corp., Docket No. ER23-1485-000
California Independent System Operator Corp., Docket No. ER23-1533-000
California Independent System Operator Corp., Docket No. ER23-1534-000
Midcontinent Independent System Operator, Inc., Docket No. EL23-28
Midcontinent Independent System Operator, Inc., Docket No. ER23-1195
Midcontinent Independent System Operator, Inc., Docket No. EL23-46
The conference will take place in a hybrid format, with presenters
and attendees allowed to participate either in-person or virtually.
Further details on both in-person and virtual participation will be
available on the conference web page.\1\ Foreign nationals attending
in-person must register through the Commission's website on or before
June 2, 2023. We also encourage all other in-person attendees to also
register through the Commission's website on or before June 2, 2023, to
help ensure Commission staff can provide sufficient physical and
virtual facilities and to communicate with attendees in the case of
unanticipated emergencies or other changes to the conference schedule
or location. Access to the conference (virtual or in-person) may not be
available to those who do not register.
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\1\ <a href="https://www.ferc.gov/news-events/events/increasing-real-time-and-day-ahead-market-and-planning-efficiency-through">https://www.ferc.gov/news-events/events/increasing-real-time-and-day-ahead-market-and-planning-efficiency-through</a>.
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The Commission will accept comments following the conference, with
a deadline of July 28, 2023.
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 <a href="/cdn-cgi/l/email-protection#89cfccdbcac6e7e5e0e7ecdafcf9f9e6fbfdc9efecfbeaa7eee6ff"><span class="__cf_email__" data-cfemail="d99f9c8b9a96b7b5b0b7bc8aaca9a9b6abad99bfbcabbaf7beb6af">[email protected]</span></a>, 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 <a href="/cdn-cgi/l/email-protection#91f0f2f2f4e2e2f8f3f8fdf8e5e8d1f7f4e3f2bff6fee7"><span class="__cf_email__" data-cfemail="d0b1b3b3b5a3a3b9b2b9bcb9a4a990b6b5a2b3feb7bfa6">[email protected]</span></a> 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, <a href="/cdn-cgi/l/email-protection#0f5c6e7d6e6721426c446661636a764f696a7d6c21686079"><span class="__cf_email__" data-cfemail="22714350434a0c6f41694b4c4e475b62444750410c454d54">[email protected]</span></a>
Alexander Smith (Technical Information), Office of Energy Policy and
Innovation, (202) 502-6601, <a href="/cdn-cgi/l/email-protection#53123f362b323d3736217d003e3a273b13353621307d343c25"><span class="__cf_email__" data-cfemail="eeaf828b968f808a8b9cc0bd83879a86ae888b9c8dc0898198">[email protected]</span></a>
Dated: June 14, 2023.
Debbie-Anne A. Reese,
Deputy Secretary.
[GRAPHIC] [TIFF OMITTED] TN21JN23.069
Technical Conference: Increasing Real-Time and Day-Ahead Market
Efficiency Through Improved Software
Agenda
AD10-12-014
June 27-29, 2023
Tuesday, June 27, 2023
9:15 a.m. Introduction
Elizabeth Topping, Federal Energy Regulatory Commission (Washington,
DC)
9:30 a.m. Session T1 (Commission Meeting Room)
Probabilistic Energy Adequacy Assessment under Extreme Weather Events
Jinye Zhao, ISO New England (Holyoke, MA)
Stephen George, ISO New England (Holyoke, MA)
Ke Ma, ISO New England (Holyoke, MA)
Steven Judd, ISO New England (Holyoke, MA)
Eamonn Lannoye, EPRI (Dublin, Ireland)
Juan Carlos Martin, EPRI (Madrid, Spain)
Transmission Outage Probability Estimation Based on Real-Time Weather
Forecast
Mingguo Hong, ISO New England (Holyoke, MA)
Xiaochuan Luo, ISO New England (Holyoke, MA)
Slava Maslennikov, ISO New England (Holyoke, MA)
Tongxin Zheng, ISO New England (Holyoke, MA)
Overview of MISO and PJM Hybrid Multiple Configuration Resource Model
Implementation Within PROBE Software
Qun Gu, PowerGEM (Clifton Park, NY)
Boris Gisin, PowerGEM (Clifton Park, NY)
Anthony Giacomoni, PJM Interconnection (Audubon, PA)
Chuck Hansen, Midcontinent ISO (Carmel, IN)
Optimizing Combined Cycle Units in PJM's Wholesale Energy Markets using
a Hybrid Multiple Configuration Resource Model
Anthony Giacomoni, PJM Interconnection (Audubon, PA)
Danial Nazemi, PJM Interconnection (Audubon, PA)
Qun Gu, PowerGEM (Clifton Park, NY)
Boris Gisin, PowerGEM (Clifton Park, NY)
11:30 a.m. Lunch
12:30 p.m. Session T2 (Commission Meeting Room)
Enhancements to Ramp Rate Dependent Spinning Reserve Modeling
Shubo Zhang, New York ISO (Rensselaer, NY)
John L. Meyer, New York ISO (Rensselaer, NY)
Iiro Harjunkoski, Hitachi Energy (Mannheim, Germany)
Determining Dynamic Operating Reserve Requirements for Reliability and
Efficient Market Outcomes: Tradeoffs and Price Formation Challenges
Matthew Musto, New York ISO (Rensselaer, NY)
Kanchan Upadhyay, New York ISO (Rensselaer, NY)
Edward O Lo, Hitachi Energy (San Jose, CA)
Operational Experience with Nodal Procurement of Flexible Ramping
Product
Guillermo Bautista-Alderete, California ISO (Folsom, CA)
George Angelidis, California ISO (Folsom, CA)
Yu Wan, California ISO (Folsom, CA)
[[Page 40236]]
Kun Zhao, California ISO (Folsom, CA)
Impact of DERs on Load Distribution Factors in Forecasting
Khaled Abdul-Rahman, California ISO (Folsom, CA)
Hani Alarian, California ISO (Folsom, CA)
Trevor Ludlow, California ISO (Folsom, CA)
Chiranjeevi Madvesh, California ISO (Folsom, CA)
Increased Congestion in SPP and Optimization in the Day Ahead Market
with Gurobi
Seth Mayfield, Southwest Power Pool (Little Rock, AR)
Yasser Bahbaz, Southwest Power Pool (Little Rock, AR)
3:00 p.m. Break
3:30 p.m. Session T3 (Commission Meeting Room)
MISO Operations Risk Assessment and Uncertainty Management
Congcong Wang, Midcontinent ISO (Carmel, IN)
Long Zhao, Midcontinent ISO (Carmel, IN)
Jason Howard, Midcontinent ISO (Carmel, IN)
Market Simulation Tools and Uncertainty Quantification Methods to
Support Operational Uncertainty Management
Nazif Faqiry, Midcontinent ISO (Carmel, IN)
Arezou Ghesmati, Midcontinent ISO (Carmel, IN)
Bing Huang, Midcontinent ISO (Carmel, IN)
Yonghong Chen, Midcontinent ISO (Carmel, IN)
Bernard Knueven, National Renewable Energy Laboratory (Golden, CO)
Pumped Storage Optimization in Real-time Markets under Uncertainty
Bing Huang, Midcontinent ISO (Carmel, IN)
Arezou Ghesmati, Midcontinent ISO (Carmel, IN)
Yonghong Chen, Midcontinent ISO (Carmel, IN)
Ross Baldick, University of Texas at Austin (Austin, TX)
Forecasting Aggregate Electricity Demand on a 5-minute Basis using
Machine Learning
Yinghua Wu, PJM Interconnection (Audubon, PA)
Laura Walter, PJM Interconnection (Audubon, PA)
Anthony Giacomoni, PJM Interconnection (Audubon, PA)
Long-Term Outlook for the ERCOT Grid
Pengwei Du, Electric Reliability Corporation of Texas (Austin, TX)
6:00 p.m. Adjourn
Wednesday, June 28, 2023
9:00 a.m. Session W-A1 (Commission Meeting Room)
Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel
Framework
Dongwei Zhao, Massachusetts Institute of Technology (Cambridge, MA)
Vladimir Dvorkin, Massachusetts Institute of Technology (Cambridge,
MA)
Stefanos Delikaraoglou, Axpo Solutions AG (Zurich, Switzerland)
Alberto J. Lamadrid L., Lehigh University (Bethlehem, PA)
Audun Botterud, Massachusetts Institute of Technology (Cambridge,
MA)
Enhancing Power System Resilience and Efficiency through Proactive
Security Assessments and the Use of powerSAS.m: A Robust, Efficient,
and Scalable Security Analysis Tool for Large-Scale Systems
Yang Liu, Argonne National Laboratory (Lemont, IL)
Feng Qiu, Argonne National Laboratory (Lemont, IL)
Jianzhe Liu, Argonne National Laboratory (Lemont, IL)
Stochastic Unit Commitment and Market Clearing in Julia with
UnitCommitment.jl
Alinson Santos Xavier, Argonne National Laboratory (Lemont, IL)
Og[uuml]n Yurdakul, Technische Universit[auml]t Berlin (Berlin,
Germany)
Aleksandr M. Kazachkov, University of Florida (Gainesville, FL)
Jun He, Purdue University (West Lafayette, IN)
Feng Qiu, Argonne National Laboratory (Lemont, IL)
Reduced-order Decomposition and Coordination Approach for Markov-based
Stochastic UC with High Penetration Level of Wind and BESS
Niranjan Raghunathan, University of Connecticut (Storrs, CT)
Peter B. Luh, University of Connecticut and National Taiwan
University (Alexandria, VA)
Zongjie Wang, University of Connecticut (Storrs, CT)
Mikhail A. Bragin, University of California, Riverside (Riverside,
CA)
Bing Yan, Rochester Institute of Technology (Rochester, NY)
Meng Yue, Brookhaven National Laboratories (Upton, NY)
Tianqiao Zhao, Brookhaven National Laboratories, (Upton, NY)
Learn to Branch and Dive for Large-scale Unit Commitment Problem
Jingtao Qin, University of California, Riverside (Riverside, CA)
Nanpeng Yu, University of California, Riverside (Riverside, CA)
Mikhail Bragin, University of Connecticut (Storrs, CT)
9:00 a.m. Session W-B1 (Hearing Room One)
Stochastic Nodal Adequacy Pricing Platform (SNAP)
Richard D. Tabors, Tabors Caramanis Rudkevich (Newton, MA)
Aleksandr Rudkevich, Newton Energy Group (Newton, MA)
Russel Philbrick, Polaris Systems Optimization (Seattle, WA)
Selin Yanikara, Newton Energy Group (Newton, MA)
Assessing Nodal Adequacy of Large Power Systems
F. Selin Yanikara, Newton Energy Group (Newton, MA)
Russ Philbrick, Polaris Systems Optimization (Seattle, WA)
Aleksandr M. Rudkevich, Newton Energy Group (Newton, MA)
Sophie Edelman, The Brattle Group (New York, NY)
Comparison of Flexibility Reserve and ORDC for Increasing System
Flexibility
Phillip de Mello, Electric Power Research Institute (Niskayuna, NY)
Erik Ela, Electric Power Research Institute (Boulder, CO)
Nikita Singhal, Electric Power Research Institute (Palo Alto, CA)
Alexandre Moreira da Silva, Lawrence Berkeley National Laboratory
(Berkeley, CA)
Miguel Heleno, Lawrence Berkeley National Laboratory (Berkeley, CA)
ABSCORES, A Novel Application of Banking Scoring and Rating for
Electricity Systems
Alberto J. Lamadrid L., Lehigh University (Bethlehem, PA)
Audun Botterud, Massachusetts Institute of Technology (Cambridge,
MA)
Jhi-Young Joo, Lawrence Livermore National Laboratory (Livermore,
CA)
Shijia Zhao, Argonne National Laboratory (Lemont, IL)
Recent Developments in the Day-ahead and Real-time Electricity Market
Design and Software Caused by the Higher Energy Costs and Emerging
Technologies--European Experience
Petr Svoboda, Unicorn Systems A.S. (Prague, Czech Republic)
[[Page 40237]]
11:30 a.m. Lunch
12:30 p.m. Session W-A2 (Commission Meeting Room)
System Resilience through Electricity System Restoration and Related
Services
Douglas Wilson, General Electric (Edinburgh, United Kingdom)
James Yu, ScottishPower Energy Networks (Glasgow, United Kingdom)
Ian Macpherson, ScottishPower Energy Networks (Glasgow, United
Kingdom)
Marta Laterza, General Electric (Glasgow, United Kingdom)
Marcos Santos, General Electric (Glasgow, United Kingdom)
Richard Davey, General Electric (Glasgow, United Kingdom)
Coordinated Cross-Border Capacity Calculation Through The FARAO Open-
Source Toolbox
Violette Berge, Artelys Canada (Montr[eacute]al, Canada)
Nicolas Omont, Artelys (Paris, France)
Advanced Scenario Selection Methods for Probabilistic Transmission
Planning Assessments
Eknath Vittal, Electric Power Research Institute (Palo Alto, CA)
Anish Gaikwad, Electric Power Research Institute (Palo Alto, CA)
Parag Mitra, Electric Power Research Institute (Palo Alto, CA)
Incorporating Climate Projections into Grid Models: Bridging the Data
Gap to Capture Weather Dependent Representative and Extreme Events and
Corresponding Uncertainties
Zhi Zhou, Argonne National Laboratory (Lemont, IL)
Neal Mann, Argonne National Laboratory (Lemont, IL)
Yanwen Xu, University of Illinois at Chicago, Urbana-Champaign
(Champaign, IL)
Zuguang Gao, University of Chicago (Chicago, IL)
Akintomide Akinsanola, University of Illinois at Chicago (Chicago,
IL)
Todd Levin, Argonne National Laboratory (Lemont, IL)
Jonghwan Kwon, Argonne National Laboratory (Lemont, IL)
Audun Botterud, Senior Energy Systems Engineer, Argonne National
Laboratory (Lemont, IL)
12:30 p.m. Session W-B2 (Hearing Room One)
Enhancing Decision Support for Electricity Markets with Machine
Learning
Yury Dvorkin, Johns Hopkins University (Baltimore, MD)
Robert Ferrando, University of Arizona (Tucson, AZ)
Laurent Pagnier, University of Arizona (Tucson, AZ)
Zhirui Liang, Johns Hopkins University (Baltimore, MD)
Daniel Bienstock, Columbia University (New York, NY)
Michael Chertkov, University of Arizona (Tucson, AZ)
Boosting Power System Operation Economics via Closed-loop Predict-and-
Optimize
Lei Wu, Stevens Institute of Technology (Hoboken, NJ)
Xianbang Chen, Stevens Institute of Technology (Hoboken, NJ)
Synergistic Integration of Machine Learning and Mathematical
Optimization for Sub-hourly Unit Commitment
Jianghua Wu, University of Connecticut (Storrs, CT)
Zongjie Wang, University of Connecticut (Storrs, CT)
Yonghong Chen, MIDCONTINENT ISO (Carmel, IN)
Bing Yan, Rochester Institute of Technology (Rochester, NY)
Mikhail Bragin, University of California, Riverside (Riverside, CA)
Privacy-Preserving Synthetic Dataset Generation for Power Systems
Research
Vladimir Dvorkin, Massachusetts Institute of Technology (Cambridge,
MA)
Audun Botterud, Massachusetts Institute of Technology (Cambridge,
MA)
2:30 p.m. Break
3:00 p.m. Session W-A3 (Commission Meeting Room)
Parallel Interior-Point Solver for Security Constrained ACOPF problems
on SIMD/GPU Architectures
Mihai Anitescu, Argonne National Laboratory (Lemont, IL)
Fran[ccedil]ois Pacaud, Ecole des Mines (Paris, France)
Michel Schanen, Argonne National Laboratory (Lemont, IL)
Sungho Shin, Argonne National Laboratory (Lemont, IL)
Daniel Adrian Maldonado, Argonne National Laboratory (Lemont, IL)
The Need for More Rigorous Calculation of Shadow Prices and LMPs
Xiaoming Feng, Hitachi Energy (Raleigh, NC)
Real-Time Market Enhancements for Reliability and Efficiency
Mort Webster, Pennsylvania State University (University Park, PA)
Anthony Giacomoni, PJM Interconnection (Audubon, PA)
Aravind Retna Kumar, Pennsylvania State University (University
Park, PA)
Sushant Varghese, Pennsylvania State University (University Park,
PA)
Shailesh Wasti, Pennsylvania State University (University Park, PA)
Economics of Grid-Supported Electric Power Markets: A Fundamental
Reconsideration
Leigh Tesfatsion, Iowa State University (Ames, IA)
3:00 p.m. Session W-B3 (Hearing Room One)
Simulation of Wholesale Electricity Markets with Capacity Expansion and
Production Cost Models to Understand Feedback between Short-Term Market
Procedures and Long-Term Investment Incentives
Jesse Holzer, Pacific Northwest National Laboratory (Richland, WA)
Abhishek Somani, Pacific Northwest National Laboratory (Richland,
WA)
Brent Eldridge, Pacific Northwest National Laboratory (Bel Air, MD)
Diane Baldwin, Pacific Northwest National Laboratory (Richland, WA)
Making the Right Resource Choice Requires Making the Right Model Choice
Rodney Kizito, Ascend Analytics (Wheaton, MD)
Gary W. Dorris, Ascend Analytics, CEO (Boulder, CO)
David Millar, Ascend Analytics (Boulder, CO)
Transmission Shortage Pricing By MW-Mile Based Demand Curve
Sina Gharebaghi, Pennsylvania State University (University Park,
PA)
Xiaoming Feng, Hitachi Energy (Raleigh, NC)
Grid OS--A Modern Software Portfolio for Grid Orchestration
Renan Giovanini, General Electric (Edinburgh, UK)
Joseph Franz, General Electric (Melbourne, FL)
5:00 p.m. Adjourn
Thursday, June 29, 2023
9:30 a.m. Session H1 (Commission Meeting Room)
Integration of DER Aggregations in ISO-Scale SCUC Models
Brent Eldridge, Pacific Northwest National Laboratory (Bel Air, MD)
Jesse Holzer, Pacific Northwest National Laboratory (Richland, WA)
Abhishek Somani, Pacific Northwest National Laboratory (Richland,
WA)
Eran Schweitzer, Pacific Northwest National Laboratory (Richland,
WA)
Rabayet Sadnan, Pacific Northwest National Laboratory (Richland,
WA)
Nawaf Nazir, Pacific Northwest National Laboratory (Richland, WA)
[[Page 40238]]
Soumya Kundu, Pacific Northwest National Laboratory (Richland, WA)
Current-Voltage AC Optimal Power Flow for Unbalanced Distribution
Network
Mojdeh Khorsand Hedman, Arizona State University (Tempe, AZ)
Zahra Soltani, Arizona State University (Tempe, AZ)
Shanshan Ma, Arizona State University (Las Vegas, NV)
Empowering Electricity Markets through Distributed Energy Resources and
Smart Building Setpoint Optimization: A Graph Neural Network-Based Deep
Reinforcement Learning Approach
You Lin, Massachusetts Institute of Technology (Cambridge, MA)
Audun Botterud, Massachusetts Institute of Technology (Cambridge,
MA)
Daisy Green, Massachusetts Institute of Technology (Cambridge, MA)
Leslie Norford, Massachusetts Institute of Technology (Cambridge,
MA)
Jeremy Gregory, Massachusetts Institute of Technology (Cambridge,
MA)
Multi-timescale Operations of Nuclear-Renewable Hybrid Energy Systems
for Reserve and Thermal Products Provision
Jie Zhang, University of Texas at Dallas (Richardson, TX)
Jubeyer Rahman, University of Texas at Dallas (Richardson, TX)
11:30 a.m. Lunch
12:30 p.m. Session H2 (Commission Meeting Room)
Optimizing Stand-Alone Battery Storage Operations Scheduling Under
Uncertainties in German Residential Electricity Market Using Stochastic
Dual Dynamic Programming
Pattanun Chanpiwat, University of Maryland & Aalto University
(College Park, MD; Espoo, Finland)
Fabricio Oliveira, Aalto University (Espoo, Finland)
Steven A. Gabriel, University of Maryland (College Park, MD)
Integration of Hybrid Storage Resources into Wholesale Electricity
Markets
Nikita Singhal, Electric Power Research Institute (Palo Alto, CA)
Rajni Kant Bansal, Johns Hopkins University (Baltimore, MD)
Erik Ela, Electric Power Research Institute (Palo Alto, CA)
Julie Mulvaney Kemp, Lawrence Berkeley National Laboratory
(Berkeley, CA)
Miguel Heleno, Lawrence Berkeley National Laboratory (Berkeley, CA)
Predicting Strategic Energy Storage Behaviors
Yuexin Bian, University of California (San Diego, CA)
Ningkun Zheng, Columbia University (New York City, NY)
Yang Zheng, University of California--San Diego (San Diego, CA)
Bolun Xu, Columbia University (New York, NY)
Yuanyuan Shi, University of California--San Diego (San Diego, CA)
Energy Storage Participation Algorithm Competition (ESPA-Comp)
Brent Eldridge, Pacific Northwest National Laboratory (Bel Air, MD)
Jesse Holzer, Pacific Northwest National Laboratory (r)
Abhishek Somani, Pacific Northwest National Laboratory (Richland,
WA)
Kostas Oikonomou, Pacific Northwest National Laboratory (Richland,
WA)
Brittany Tarufelli, Pacific Northwest National Laboratory (Laramie,
WY)
Li He, Pacific Northwest National Laboratory (Richland, WA)
2:30 p.m. Break
3:00 p.m. Session H3 (Commission Meeting Room)
Congestion Mitigation with Transmission Reconfigurations in the Evergy
Footprint
Pablo A. Ruiz, NewGrid (Somerville, MA)
Derek Brown, Evergy (Topeka, KS)
Jeremy Harris, Evergy (Topeka, KS)
German Lorenzon, NewGrid (Somerville, MA)
Grant Wilkerson, Evergy (Kansas City, MO)
Optimal Transmission Expansion Planning with Grid Enhancing
Technologies
Swaroop Srinivasrao Guggilam, Electric Power Research Institute
(Knoxville, TN)
Alberto Del Rosso, Electric Power Research Institute (Knoxville,
TN)
The Key Role of Extended ACOPF-based Decision Making for Supporting
Clean, Cost-Effective and Reliable/Resilient Electricity Services
Maria Ilic, Carnegie Mellon University (Pittsburgh, PA)
Rupamathi Jaddivada, SmartGridz (Boston, MA)
Jeffrey Lang, Massachusetts Institute of Technology (Cambridge, MA)
Eric Allen, SmartGridz (Boston, MA)
Data & API Standards for Clean Energy Solutions and Digital Innovation
Priya Barua, Clean Energy Buyers Institute (Washington, DC)
Ben Gerber, M-RETS (Minneapolis, MN)
Mine Production Scheduling under Time-of-Use Power Rates with Renewable
Energy Sources
Daniel Bienstock, Columbia University (New York, NY)
Amy Mcbrayer, South Dakota School of Mines (Rapid City, SD)
Andrea Brickey, South Dakota School of Mines (Rapid City, SD)
Alexandra Newman, Colorado School of Mines (Golden, CO)
5:30 p.m. Adjourn
Conference Abstracts
Day 1--Tuesday, June 27
Session T1 (Tuesday, June 27, 9:30 a.m.) Commission Meeting Room
Probabilistic Energy Adequacy Assessment Under Extreme Weather Events
Dr. Jinye Zhao, Technical Manager, ISO New England (Holyoke, MA)
Stephen George, Director, ISO New England (Holyoke, MA)
Dr. Ke Ma, Senior Analyst, ISO New England (Holyoke, MA)
Steven Judd, Manager, ISO New England (Holyoke, MA)
Dr. Eamonn Lannoye, Program Manager, Electric Power Research Institute
(Dublin, Ireland)
Juan Carlos Martin, Senior Engineer, Electric Power Research Institute
(Madrid, Spain)
As intermittent and limited energy resources become a larger
portion of the region's generation resource mix, and as the region's
demand becomes increasingly electrified, it has become increasingly
important to understand the operational risks associated with future
weather extremes. To better inform the region's understanding of these
risks, ISO New England in collaboration with EPRI, has developed a
probabilistic energy adequacy assessment framework. This approach of
stress testing the system's energy adequacy focuses on generating
comprehensive extreme weather scenarios for the New England region and
performing risk analyses across these scenarios. The framework offers a
tailored approach to identify unique energy adequacy risks faced by the
New England power system and enables us to analyze related stressors
under extreme events.
Transmission Outage Probability Estimation Based on Real-Time Weather
Forecast
Dr. Mingguo Hong, Principal Analyst, ISO New England (Holyoke, MA)
Dr. Xiaochuan Luo, Manager, ISO New England (Holyoke, MA)
[[Page 40239]]
Dr. Slava Maslennikov, Technical Manager, ISO New England (Holyoke, MA)
Dr. Tongxin Zheng, Director, ISO New England (Holyoke, MA)
Extreme weather patterns including both winter and summer storms
have been posing increasing threats to power transmission security in
the New England area. Being able to accurately predict their impacts
will benefit both power system operation and planning. In recent years,
the ISO New England has been developing machine-learning algorithms for
estimating the probability of transmission line outage in real-time,
given weather forecast variables such as wind, temperature, snow, and
rain precipitation, etc. This presentation will share our study
findings and on-going software implementation experience.
Overview of MISO and PJM Hybrid Multiple Configuration Resource Model
Implementation Within PROBE Software
Dr. Anthony Giacomoni, Manager, Advanced Analytics, PJM Interconnection
(Audubon, PA)
Dr. Danial Nazemi, Operations Research Engineer II, PJM Interconnection
(Audubon, PA)
Dr. Qun Gu, Principal Consultant, PowerGEM (Clifton Park, NY)
Dr. Boris Gisin, President, PowerGEM (Clifton Park, NY)
For the past three years, PJM, MISO and PowerGEM have been working
jointly on developing an advanced SCUC algorithm to prepare for the
full-scale implementation of a Multiple Configuration Resource (MCR)
model in their energy markets. PJM currently uses aggregate models for
MCRs that do not accurately capture their true operating
characteristics. Often MCRs may need to overestimate costs to ensure
cost recovery, underestimate costs to ensure selection or offer reduced
operating ranges to be able to accurately reflect their operating
capabilities. This presentation will focus on the impacts to PJM's
energy markets from optimizing the multiple configurations and
components of their combined cycle units. The optimization of multiple
configurations and components is very challenging due to the additional
integer variables and constraints that impact the solution time and may
lead to performance challenges. A prototype full-scale MCR model has
been implemented in the PROBE Day-Ahead software, which is currently a
critical component of PJM's Day-Ahead Market (DAM) clearing process.
The prototype MCR model has the ability to perform energy and ancillary
service co-optimization for combined cycle units with multiple
configurations and components. The developed model has no practical
limits on the number of configurations that each unit can have and the
model allows for simultaneously enforcing configuration and component
level constraints. Benefits of the new model include enhanced modeling
flexibility and accuracy, which allows combined cycle participants to
submit bids that align with their units' physical operating
constraints, better alignment with the real-time model and market
outcomes with increased social benefits. To quantify the impacts of the
MCR model on PJM's energy markets, PJM gathered configuration and
component data from a large number of combined cycle units in its
footprint. Simulations using one year of historical DAM data were then
performed to measure the impacts of the MCR model on the clearing
engine's computational performance and market outcomes. Results clearly
demonstrate significant potential bid production cost savings of over
$100 million per year with a very modest increase in solution time. The
MCR model is currently being implemented in PJM's DAM for the
optimization of synchronous condensers. It is planned that after
successful implementation of the MCR model for synchronous condensers
the same model will be implemented for combined cycle units and
possibly for hybrid resources as well.
Session T2 (Tuesday, June 27, 12:30 p.m.) (Commission Meeting Room)
Enhancements to Ramp Rate Dependent Spinning Reserve Modeling
Dr. Shubo Zhang, Energy Market Engineer, New York ISO (Rensselaer, NY)
John L. Meyer, Senior Energy Market Engineer, New York ISO (Rensselaer,
NY)
Iiro Harjunkoski, Researcher, Hitachi Energy (Mannheim, Germany)
In a joint effort between the NYISO and Hitachi Energy, a Ramp Rate
Dependent (RRD) formulation of spinning reserve scheduling that
utilizes Multiple Response Rates (MRR) across a Combined Cycle Gas
Turbine (CCGT) generator or other dispatchable resource's range of
output has been developed. To provide more flexibility to Market
Participants, a ``Limited Participation'' conceptual strategy is also
included that would allow a CCGT or other dispatchable resource to
selectively provide spinning reserves or regulation for a certain range
of output. This presentation will discuss the market basis and design
of Limited Participation in spinning reserves and regulation, in the
context of Ramp Rate Dependent Spinning Reserve Modeling.
Determining Dynamic Operating Reserve Requirements for Reliability and
Efficient Market Outcomes: Tradeoffs and Price Formation Challenges
Matthew Musto, Technical Specialist--Market Solutions Engineering,
NYISO (Rensselaer, NY)
Kanchan Upadhyay, Senior Energy Market Engineer--Market Solutions
Engineering, NYISO (Rensselaer, NY)
Edward O Lo, Consultant, Hitachi Energy (San Jose, CA)
With increasing intermittent resources in the generation mix, the
need for more economic responsiveness and operational flexibility while
maintaining system reliability is growing. The NYISO and Hitachi Energy
have been working on advanced design and techniques for calculating
operating reserve requirements dynamically for each reserve region
while simultaneously optimizing the dispatch solution in the market
clearing engine. A key benefit of the dynamic reserves formulation is
the functionality to determine the least-cost generation and reserve
mix to meet load. This dynamic determination of reserve requirements in
New York Control Area (NYCA) and all reserve regions within the NYCA
creates new tradeoffs between energy schedules and reserve
requirements. This presentation will discuss these tradeoffs and
highlight the associated price formation challenge.
Operational Experience with Nodal Procurement of Flexible Ramping
Product
Dr. Guillermo Bautista-Alderete, Director, Market Analysis &
Forecasting, California ISO (Folsom, CA)
George Angelidis, Executive Principal--Power Systems and Market
Technology, California ISO (Folsom, CA)
Yu Wan, Power Systems Engineer, California ISO (Folsom, CA)
Kun Zhao, Market Engineering Specialist Lead, California ISO (Folsom,
CA)
The CAISO's market procures flexible ramping capacity to manage
weather-based uncertainty realized in real time. The CAISO introduced
this product in 2016 using a procurement requirement at the system
level. Using a system-level procurement requirement, the market
frequently procured flexible ramping capacity from locations impacted
by
[[Page 40240]]
congestion, thereby stranding the flexible ramping capacity. The CAISO
has enhanced the design of the flexible ramping product using a
formulation that observes transmission constraints. This approach
considers congestion management as part of the procurement of flexible
ramping capacity helping to ensure the CAISO can deploy this capacity
when uncertainty arises. This new design poses additional complexity
because the market clearing process now considers transmission
constraints for energy and for flexible ramping capacity. The CAISO
will provide an update on the performance of its flexible ramping
product under this new design.
Impact of DERs on Load Distribution Factors in Forecasting
Dr. Khaled Abdul-Rahman, Vice President, Power System and Market
Technology, California ISO (Folsom, CA)
Hani Alarian, Executive Director of Power Systems Technology
Operations, California ISO (Folsom, CA)
Trevor Ludlow, Specialist Lead of Power Systems Technology Operations,
California ISO (Folsom, CA)
Chiranjeevi Madvesh, Lead Engineer of Power Systems Technology
Operations, California ISO (Folsom, CA)
The calculation of load distributing factors (LDFs) is
traditionally performed based on a collection of historical state
estimator calculated values and stored in libraries for use when
simulating power system operations in look-ahead market and reliability
applications. The inherit assumption is that bus loads are accurately
estimated from the aggregate system load forecast using LDFs, and
generation quantities are deterministically known. Accordingly, it is
assumed that there is a strong correlation between the system load and
individual bus loads. However, the proliferation of behind-the-meter
distributed energy resources, solar rooftops, batteries, hybrid
resources, as well as the use of behind the-meter demand response
utility programs, and electric vehicles introduces a non-conforming
load component at locations that were previously conforming loads.
This issue requires a more accurate forecast of non-conforming
loads by taking into consideration the probabilistic nature of bus
loads and variable/intermittent generation. The CAISO's enhanced LDF
forecast algorithm takes into account not just the average hour of the
day and the day of the week but includes machine learning ability to
distinguish between flows that scales up with load in both a non-linear
and linear fashion. It also includes a new fusion-forecasting model
that improves forecasting accuracy. Additionally, the CAISO's algorithm
uses data engineering and preprocessing options to increase the
accuracy of the proposed model. The CAISO analyzes load data to verify
that the proposed methodology provides higher forecasting accuracy with
lower error indices.
Increased Congestion in SPP and Optimization in the Day Ahead Market
With Gurobi
Seth Mayfield, Manager of Market Support & Analysis, Southwest Power
Pool (Little Rock, AR)
Yasser Bahbaz, Director of Markets Development, Southwest Power Pool
(Little Rock, AR)
SPP has seen substantial increased congestion in recent years.
These trends have numerous reliability and economic impact. In the Day-
Ahead Market, SPP has noticed high transmission activation leading to
longer optimization runtimes. High activations results in large
increases in the mathematical growth, which then results in slower
Mixed Integer Program (MIP) runtimes. Other factors include increasing
market rules complexity (such as uncertainty product) and additional
market resource registrations. SPP performed a study where we evaluate
swapping our existing optimization engine (IBM's CPlex) with Gurobi's
optimization engine. The study reran every approved DAMKT SCUC
operating day for 2021 (365 cases). Gurobi solved the cases 41% faster
than CPlex using Gurobi without tuning. A very light discussion with
Gurobi resulted in a few tuning suggestions which pushed the runtime
reduction to 43%. SPP is in the process of acquiring Gurobi licenses
and will work with our software vendor to incorporate the engine into
our market. Phase 1 will include simultaneously running both CPlex and
Gurobi as we believe this will give us the best/fastest results for
each day. It is expected that there will be a transition to using more
Gurobi instances than CPlex as time goes on.
Session T3 (Tuesday, June 27, 3:30 p.m.) (Commission Meeting Room)
MISO Operations Risk Assessment and Uncertainty Management
Dr. Congcong Wang, Lead, Operations Risk Assessment, Midcontinent ISO
(Carmel, IN)
Dr Long Zhao, Senior Advisor of Operations Risk Assessment,
Midcontinent ISO (Carmel, IN)
Jason Howard, Director of Operations Risk Management, Midcontinent ISO
(Carmel, IN)
Fleet transition is driving a new risk profile at MISO. Uncertainty
and Variability are increasing in their intensity, diversity, and
volatility. While probabilistic forecasting has made progress for wind
and solar, its integration into operations and markets is uneven.
Furthermore, uncertainty comes in more sources than just renewable
energy such as generation and transmission outages, fuel scarcity
especially during extreme weather events, resulting in challenges for
the RTO to manage the aggregated or net uncertainty. This presentation
will outline MISO's operations risk assessment and uncertainty
management initiatives including: (1) Characterize Risks--transform
traditional deterministic renewable, load and ``net'' load forecasts to
probabilistic forecasts in production systems; and assess generation
and fuel risks to better capture the unknowns; (2) Integrate risks into
Operations Situational Awareness and Operations Planning--provide
control room a dynamic and geographically granular visualization of
operating reserve margin; and visibility of weather driven operations
risks; (3) Automate risk management through market products with
dynamic reserve requirements--assess net uncertainty across different
timeframes; and predict risks to establish a daily target for procuring
market-based reserves using analytical and meteorological techniques.
This work is done in collaboration with R&D through the joint
Uncertainty Roadmap.
Market Simulation Tools and Uncertainty Quantification Methods To
Support Operational Uncertainty Management
Dr. Nazif Faqiry, R&D Engineer, Midcontinent ISO (Carmel, IN)
Dr. Arezou Ghesmati, R&D Engineer, Midcontinent ISO (Carmel, IN)
Dr. Bing Huang, R&D Engineer, Midcontinent ISO (Carmel, IN)
Dr. Yonghong Chen, Consulting Advisor, Midcontinent ISO (Carmel, IN)
Dr. Bernard Knueven, Research Scientist, National Renewable Energy
Laboratory (Golden, CO)
Portfolio evolution and more frequent extreme weather events are
introducing more challenges to MISO Market Operations with new risk
profiles. To improve market efficiency and generate efficient price
signals for operational and investment decisions, it is increasingly
important to align market
[[Page 40241]]
design with reliability and risk management needs. This work presents
the Electrical Grid Research & Engineering (EGRET) market simulation
tool adapted and enhanced at MISO to evaluate existing and future
system, and a novel netload ramp uncertainty prediction and scenario
generation method to support stochastic simulation and reserve
requirement settings. First, it presents a multi-periods market
simulation tool and its capabilities, including rolling real-time unit
commitment and economic dispatch (UCED), followed by the results of 8
GW solar penetration study. Then, it presents a novel method that is
developed to predict and generate scenarios for uncertainties across
different lead times. The scenarios can be used as inputs to the market
simulation tool for stochastic simulation. The two parts together may
lead to multi-scenario stochastic unit commitment in the future. In the
near term, the stochastic market simulation can help to validate market
design and operational procedures. The uncertainty predication and
scenario generation may help operational situational awareness and
better define reserve requirements and operational margins.
Pumped Storage Optimization in Real-Time Markets Under Uncertainty
Bing Huang, Research Engineer, Midcontinent ISO (Carmel, IN)
Arezou Ghesmati, R&D Scientist, Midcontinent ISO (Carmel, IN)
Yonghong Chen, Consulting Advisor, Midcontinent ISO (Carmel, IN)
Ross Baldick, Emeritus Professor, University of Texas at Austin
(Austin, TX)
Pumped storage hydro units (PSHU) can provide flexibility to power
systems and may especially be valuable with increasing shares of
intermittent renewable resources. However, the scheduling of PSHUs,
particularly in the real-time market, has not been thoroughly studied.
To enhance the use of PSH resources and leverage their flexibility, it
is important to incorporate the uncertainties to properly address the
risks in the real-time market operation. In this work, first a
deterministic PSHU model that incorporates the state of charge in the
Day-ahead market optimization is introduced. Second, two pumped storage
hydro (PSH) models that use probabilistic price forecasts are proposed
for Look-ahead commitment (LAC) in the real-time market operation. A
risk neutral stochastic PSH model and a risk averse robust optimization
PSH model are developed using the probabilistic price forecasts to
capture the real-time market uncertainties. Numerical studies in Mid-
continent Independent System Operator (MISO) system demonstrate that
the proposed models improve market efficiency and reduce PSH real time
risk compared to the current approach. Probabilistic forecast for Real
Time Locational Marginal Price (RT-LMP) is created and embedded into
the proposed stochastic and robust optimization models, a statistically
robust approach is used to generate scenarios for reflecting the
temporal inter-dependence of the LMP forecast uncertainties.
Forecasting Aggregate Electricity Demand on a 5-Minute Basis Using
Machine Learning
Dr. Yinghua Wu, Senior Lead Data Scientist, PJM Interconnection
(Audubon, PA)
Laura Walter, Senior Lead Data Scientist, PJM Interconnection (Audubon,
PA)
Dr. Anthony Giacomoni, Manager--Advanced Analytics, PJM Interconnection
(Audubon, PA)
PJM currently has two load forecasts used in dispatch and real-time
operations. These forecasts are comprised of the short-term forecast,
which is the forecasted hourly average load for the next seven days,
and the very short-term load forecast, which is the forecasted 5-minute
load averages for the next six hours. The very short-term load forecast
is constantly fed into the real-time dispatch software for optimal
power flow calculations and real-time market pricing. It is of crucial
importance that these forecasts closely match the actual load in the
near future to maintain system frequency and voltage. If not,
dispatchers must take action to quickly intervene and adjust the load
up or down. The load profiles generally follow temporal patterns, but
are also driven by weather and other usage patterns. Given the recent
rapid growth of machine learning technologies, this presentation will
survey a collection of some of the most representative and innovative
methods that are suitable to time series predictions such as load
forecasting, e.g., gradient boosting, recurrent neural network, causal
convolution, etc. We will also revisit some traditional methods such as
generalized linear models and automatic regressive moving average
(ARMA) methods to explore whether they can capture the load shape in
short horizons. We will survey and analyze these new technologies for
their power of prediction to see if these methods provide the potential
to improve on current forecasting practices.
Long-Term Outlook for the ERCOT Grid
Pengwei Du, Supervisor--Economic Analysis & Long Term Planning Studies,
The Electric Reliability Council of Texas (Austin, Texas)
The bulk transmission network within ERCOT consists of the 60-
kilovolt (kV) and higher transmission lines and associated equipment.
ERCOT conducts a forward-looking study to understand long-term
reliability and economics need to ensure continued system reliability
and efficiency. This talk will present the key challenges and findings
from the most recent long-term system assessment planning study, which
accounts for the inherent uncertainty of planning the system in the 10-
to 15-year planning horizon.
Day 2--Wednesday, June 28
Session W-A1 (Wednesday, June 28, 9:00 a.m.) (Commission Meeting Room)
Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel
Framework
Dr. Dongwei Zhao, Postdoctoral Associate, Massachusetts Institute of
Technology (Cambridge, MA)
Dr. Vladimir Dvorkin, Postdoctoral Fellow, Massachusetts Institute of
Technology (Cambridge, MA)
Dr. Stefanos Delikaraoglou, Data Scientist, Axpo Solutions AG (Zurich,
Switzerland)
Dr. Alberto J. Lamadrid L., Associate Professor, Lehigh University
(Bethlehem, PA)
Dr. Audun Botterud, Principal Research Scientist, Massachusetts
Institute of Technology (Cambridge, MA)
The fast-growing variable renewable energy sources (VRES) in
electricity markets are creating challenges to uncertainty management.
This work addresses these challenges by adopting an uncertainty-
informed adjustment toward VRES bidding quantities in the day-ahead
market and minimizing expected system costs under the sequential
market-clearing structure. However, implementing this mechanism
requires solving a bilevel optimization problem, which is
computationally difficult for practical large-scale systems. To
overcome this challenge, we propose a novel technique based on strong
duality and McCormick envelopes. This approach relaxes the original
problem to a linear program, enabling efficient computation for large-
scale systems. We conduct case studies on the 1576-bus NYISO systems
and compare our bilevel VRES-adjustment model with the myopic strategy
where VRES producers bid the forecast value in the day-ahead market.
The results
[[Page 40242]]
demonstrate that under a future high VRES penetration level (e.g.,
40%), our bilevel framework can significantly reduce the expected
system cost and the volatility of the market prices, participants'
revenues, and real-time re-dispatch adjustments, by efficiently
optimizing VRES quantities in the day-ahead market. Furthermore, we
found that increasing transmission ability may incur a much higher
system cost under the myopic strategy while a lower cost under the
bilevel model) because of the lack of flexible generators or reserves
in real time to deal with uncertainty.
Enhancing Power System Resilience and Efficiency Through Proactive
Security Assessments and the Use of powerSAS.m: A Robust, Efficient,
and Scalable Security Analysis Tool for Large-Scale Systems
Dr. Yang Liu, Postdoctoral Appointee, Argonne National Laboratory
(Lemont, IL)
Dr. Feng Qiu, Principal Computational Scientist, Argonne National
Laboratory (Lemont, IL)
Dr. Jianzhe Liu, Energy Systems Scientist, Argonne National Laboratory
(Lemont, IL)
Power system security assessment is directly related to increasing
real-time and day-ahead market and planning efficiency because it helps
ensure the reliable and secure operation of the power system, which is
essential for efficient market and planning activities. Without proper
security assessments, the power system is vulnerable to a variety of
threats, including cyber attacks, natural disasters, and equipment
failures, which can disrupt the operation of the system and lead to
market inefficiencies and planning uncertainties. By performing
security assessments and identifying potential vulnerabilities, system
operators can take proactive measures to mitigate risks and improve the
reliability and efficiency of the power system, which, in turn,
supports the goals of real-time and day-ahead market and planning
efficiency. Additionally, advanced software tools and models can be
used to support security assessments, enabling operators to better
anticipate and respond to potential security threats and further
improve the efficiency and reliability of the power system. Existing
tools (commercial or open-source) work fine for routine security
analysis under normal operating conditions. However, in resilience
analysis, which studies the system security and reliability under
stressed scenarios, existing tools often experience various numerical
issues, significantly impacting operators' assessment of system
resilience. A recent example is the non-convergence issues with PSS/E,
one of the best commercial power system analysis tools used in the DOE
Puerto Rico resilience project led by Argonne. The numerical issues
forced the team to give up more advanced analysis. A robust and
efficient security analysis tool is imperative for resilience study in
large-scale systems. In this talk, we will introduce a recently
released open-source power system security analysis tool called
powerSAS.m. The powerSAS.m is a robust, efficient, and scalable power
grid analysis framework based on semi-analytical solutions (SAS)
technology. The talk will cover the following two critical aspects and
discuss how they are directly related to increasing real-time and day-
ahead market and planning efficiency. First, we will introduce the
fundamentals of the SAS technology and the major functionalities of the
powerSAS.m, including (1) Steady-state analysis, including power flow,
continuation power flow, and contingency analysis. (2) Dynamic security
analysis, including voltage stability analysis, transient stability
analysis, and flexible user-defined simulation. (3) Hybrid extended-
term simulation provides adaptive quasi-steady-state-dynamic hybrid
simulation in extended term with high accuracy and efficiency. We will
also introduce some ongoing functionalities, including the SAS-based
electromagnetic transient (EMT) simulation and multi-scale simulations.
Second, we will present some use cases to demonstrate the key features
and performance of the SAS technology and powerSAS.m tool, including:
(1) High numerical robustness. Backed by the SAS approach, the PowerSAS
tool provides much better convergence than the tools using traditional
Newton-type algebraic equation solvers when solving algebraic
equations/ordinary differential equations/differential-algebraic
equations. (2) Enhanced computational efficiency and scalability. Due
to the analytical nature, PowerSAS provides model-adaptive high-
accuracy approximation, which brings significantly extended effective
range and much larger steps for steady-state/dynamic analysis. PowerSAS
has been used to solve large-scale system cases with 200,000+ buses.
Stochastic Unit Commitment and Market Clearing in Julia With
UnitCommitment.jl
Dr. Alinson Santos Xavier, Computational Scientist, Argonne National
Laboratory (Lemont, IL)
Og[uuml]n Yurdakul, Ph.D. Candidate, Technische Universit[auml]t Berlin
(Berlin, Germany)
Dr. Aleksandr M. Kazachkov, Assistant Professor, University of Florida
(Gainesville, FL)
Jun He, Professor, Purdue University (West Lafayette, IN)
Dr. Feng Qiu, Principal Computational Scientist, Argonne National
Laboratory (Lemont, IL)
UnitCommitment.jl (UC.jl) is a comprehensive open-source
optimization package for the Security-Constrained Unit Commitment
Problem (SCUC), providing an extensible and fully-documented data
format for the problem, Julia/JuMP implementations of state-of-the-art
mathematical formulations and solution methods, as well as a diverse
collection of realistic and large-scale benchmark instances. This talk
focuses on two major features recently introduced to the package.
Firstly, the package now supports modeling and optimizing two-stage
stochastic versions of the problem, in addition to the deterministic
SCUC. Compared to existing implementations, UC.jl allows a broader set
of network parameters to be treated as uncertain, including not only
demands and generation limits, but also production costs, network
topology, transmission limits, among others. Benchmark scripts are
provided to accurately evaluate the performance of different stochastic
solution methods. Secondly, the package now includes various
functionalities for market clearing, such as the computation of
generator payments and locational marginal prices (LMPs) using
different methods proposed in the literature. In this talk, we will
discuss the usage of these new features, technical challenges
associated with them, and the potential simulations or studies that
they enable.
Reduced-Order Decomposition and Coordination Approach for Markov-Based
Stochastic UC With High Penetration Level of Wind and BESS
Niranjan Raghunathan, Ph.D. Student, University of Connecticut (Storrs,
CT)
Dr. Peter B. Luh, Professor, University of Connecticut and National
Taiwan University (Alexandria, VA)
Dr. Zongjie Wang, Professor, University of Connecticut (Storrs, CT)
Dr. Mikhail A. Bragin, Professor, University of California, Riverside
(Riverside, CA)
Dr. Bing Yan, Professor, Rochester Institute of Technology (Rochester,
NY)
Dr. Meng Yue, Research Staff Electrical Engineer, Brookhaven National
Laboratories (Upton, NY)
[[Page 40243]]
Dr. Tianqiao Zhao, Renewable Energy Group, Brookhaven National
Laboratories (Upton, NY)
With the growing need to achieve carbon neutrality, integrating
renewable energy (e.g., wind and solar) and battery energy storage
systems (BESSs) into the grid is an urgent and challenging enterprise.
At the day-ahead stage, unit commitment (UC) decisions need to account
for uncertainties of geographically distributed renewable generation.
BESS integration can help mitigate intermittence and reduce curtailment
by storing energy during high renewable generation periods and
releasing energy when needed, thus improving the cost efficiency of
grid operation. Therefore, ensuring economic and reliable grid
operations with the significant rise in renewable energy penetration
necessitates the consideration of spatially distributed uncertainties
and BESS in UC. To achieve this, a risk-neutral approach (i.e.,
scenario-based stochastic UC and Markov-based stochastic UC) is
preferred over risk-averse approaches (e.g., robust optimization and
interval optimization), as the latter yields overly conservative
solutions. Between the risk-neutral approaches, Markov-based approaches
have two advantages over scenario-based approaches: (1) Due to the
Markov property, where stochastic information at the next time step
depends only on the information at the current time step, the
uncertainty can be compactly modeled by wind generation states at each
time step and state transitions between subsequent time steps.
Consequently, the overall number of possible states and transitions in
the Markov model increases linearly with the number of intervals in the
optimization horizon, whereas the number of possible scenarios
increases exponentially. (2) Reduced Markov models preserve the
volatility of wind generation, the underlying spatio-temporal
correlation structure, and low-probability, high-impact events more
effectively in uncertainty sets compared to scenarios. Therefore, the
problem is formulated as Markov-based stochastic UC. With distributed
wind, however, the number of possible wind states grows exponentially
with the number of wind farms in different locations considered, posing
major computational difficulties. To reduce complexity, an innovative
decomposition and coordination framework is developed, where
approximate area subproblems are formulated by utilizing area-
perspective, reduced-order Markov models. In these models, the
variability of local (in-area) windfarms is emphasized while that of
nonlocal (out-of-area) windfarms is approximated by using Principal
Component Analysis (PCA) to reduce dimensionality while preserving the
maximum amount of variation. This is a reasonable approximation because
variations at the local level have more impact on the behavior of local
units and power flow through local transmission lines compared to
variations at distant locations. The objective of an approximate area
subproblem is to optimize in-area resources based on its area-
perspective Markov model. The approximate area subproblems are solved
iteratively while their solutions are coordinated using Surrogate
Absolute-Value Lagrangian Relaxation (SAVLR), a state-of-the-art dual
method with faster convergence than traditional Lagrangian Relaxation
(LR)-based methods. To improve performance, an online filtering method
for removing redundant transmission capacity constraints at each
iteration is implemented in parallel by utilizing multiple cores. The
solutions are validated using Monte Carlo simulations. Testing results
based on the 118-bus system with 5 distributed wind farms show the
effectiveness of the method in finding low-cost and robust UC solutions
in a timely manner for multiple cases with different volatilities of
wind generation and simulated extreme weather events. Analysis of the
operation of BESSs shows that they absorb excess energy during high
wind periods and release the energy during low wind periods, thus
reducing wind curtailment and overall costs.
Learn To Branch and Dive for Large-Scale Unit Commitment Problem
Jingtao Qin, Research Assistant, University of California, Riverside
(Riverside, CA)
Nanpeng Yu, Associate Professor, University of California, Riverside
(Riverside, CA)
Mikhail Bragin, Assistant Research Professor, University of Connecticut
(Storrs, CT)
Unit commitment (UC) problems are typically formulated as mixed-
integer program (MIP) and solved by the branch-and-bound (B\&B)
paradigm. The recent advances in graph neural network (GNN) motivate
the application of GNN in learning to dive and branch for B\&B
algorithm in modern MIP solvers. Existing GNN models are mostly
constructed from B\&B trees, which are computationally expensive when
dealing with large-scale UC problems. In this paper, we propose a
physical network information-based hierarchical graph convolution model
for neural diving that leverages the underlying features of various
components of power systems to find high-quality variable assignments.
Furthermore, we adopt the B\&B tree-based graph convolution model for
neural branching to select the optimal variables for branching at each
node of the B\&B tree. Finally, we integrate neural diving and neural
branching into a modern MIP solver to establish a novel neural MIP
solver that is specially designed for large-scale UC problems. Numeral
studies show that our proposed model has better performance and
scalability than the baseline B\&B tree-based model on neural diving.
Moreover, the neural MIP solver yields the lowest MIPGap for all
testing days after combining it with our proposed neural diving model
and baseline neural branching model.
Session W-B1 (Wednesday, June 28, 9:00 a.m.) (Hearing Room One)
Stochastic Nodal Adequacy Pricing Platform (SNAP)
Dr Richard D. Tabors, Partner and President, Tabors Caramanis Rudkevich
(Newton, MA)
Dr. Aleksandr Rudkevich, President, Newton Energy Group (Newton, MA)
Russel Philbrick, President, Polaris Systems Optimization (Seattle, WA)
Dr. Selin Yanikara, Analyst, Newton Energy Group (Newton, MA)
The Stochastic Nodal Adequacy Pricing Platform (SNAP) software
system provides an implemented methodology to calculate the probability
and value of RESOURCE INADEQUACY of electricity supply on an hourly
basis for a period of one to five days ahead of real time. The
stochasticity of SNAP is driven by the stochastic weather forecasts
available and provided by IBM The Weather Company on a i5 day forward
basis for a 4x4km grid worldwide (SNAP uses at most 5). Forecasts are
developed from 76 different numerical weather prediction models (and
their ensemble members) as inputs to their forecast system. Bayesian
model averaging is used to correct for systematic errors (bias).
Results are rearranged to create 100 synthetic weather system scenarios
through the use of Ensemble Copula Quantile-Coupling technique. The
result is a probabilistic forecast within which each of the scenarios
is equally likely. As the electric supply system moves toward greater
dependence on renewable sources both in front of and behind the meter
and as weather conditions are evolving, the stochastics of weather have
become a, if not the
[[Page 40244]]
driving force in forecasting power system adequacy. SNAP is developed
as an information/assist tool for operational planning at the utility
system level. SNAP has been developed with funding from the Department
of Energy's ARPA-E PERFORM program. SNAP uses the individual components
of the weather forecast scenarios to create 100 probabilistic scenarios
of the output of individual wind and solar locations as well
forecasting of demand incorporating behind the meter generation. Based
on the probability of renewable supply, demand, and the probability of
outage of traditional supply sources and transmission, SNAP runs
100,000 Monte Carlo SCED/SCUC runs of the commercially available cloud-
based ENELYTIX software system to identify the existence of resource
inadequacy, the nodal location of that inadequacy, its cause and
potential solutions. The objective is to present the structure of the
computational and analytic processes that allow for running and
evaluation of 100,000 scenarios for each individual forecast hour. The
presentation will discuss the cloud-based structure the allows the
analysis to be completed in under 50 minutes using 500 virtual machines
at a costs of $120 at spot rates.
Assessing Nodal Adequacy of Large Power Systems
Dr. F. Selin Yanikara, Energy Analyst, Newton Energy Group (Newton, MA)
Russ Philbrick, President, Polaris Systems Optimization (Seattle, WA)
Aleksandr M. Rudkevich, President, Newton Energy Group (Newton, MA)
Sophie Edelman, Electricity Research Analyst, The Brattle Group (New
York, New York)
Extreme weather events, increasing electrification, and integration
of wind and solar power pose significant challenges for reliable
operation of the power grid. Quantitative evaluation of these impacts
is critical for making efficient policy and investment decisions and in
designing markets and engineering controls. This presentation will
summarize the theoretical foundation for nodal probabilistic assessment
of resource adequacy and its applications to modern electrical systems
with a significant penetration of weather dependent variable energy
resources and storage technologies. In addition, this presentation will
address the need for, and will present, new adequacy metrics that
reflect an economically justified contribution of each system asset--
generation, transmission, or demand resource to system adequacy. The
analysis relies on the Monte Carlo based methodology using new
computationally efficient and statistically accurate methods. We
illustrate the numerical results and computational performance of our
approach using the ENELYTIX[supreg] powered by PSO SaaS and our
standard dataset for the ERCOT market.
Comparison of Flexibility Reserve and ORDC for Increasing System
Flexibility
Phillip de Mello, Senior Technical Leader, Electric Power Research
Institute (Palo Alto, CA)
Erik Ela, Program Manager, Electric Power Research Institute (Boulder,
CO)
Nikita Singhal, Technical Leader, Electric Power Research Institute
(Palo Alto, CA)
Alexandre Moreira da Silva, Research Scientist, Lawrence Berkeley
National Laboratory (Berkeley, CA)
Miguel Heleno, Research Scientist/Engineer, Lawrence Berkeley National
Laboratory (Berkeley, CA)
Power system composition changes are making flexible resources more
important to balance the increasing variability and uncertainty. System
operators often look to increase the amount of flexibility available to
give real time operations greater control. Two common methods for
increasing flexibility are to create new reserve products that are
targeted towards flexibility and ramping capability or using an
extended operating reserve demand curve (ORDC) to procure more of an
existing reserve when the additional value exceeds costs. Detailed
operation simulations to mimic day ahead and real time markets were
conducted to compare flexibility reserves and ORDCs. Benefits to
reliability were measured by a reduction in shortages of reserves and
energy experienced across the system. The extra reserves generally
increased the costs of running the system, but it was lower than the
penalty prices of the shortages relieved. Some periods showed a
reduction of system costs with added reserves, suggesting that more
efficient designs of reserves could not only increase system
reliability but also reduce costs. Both methods increase the
flexibility on the system, but function differently in typical
deployments in current ISO/RTO practice. The different parameters
defining each technique was explored to understand how their
differences manifest in improving reliability. Most differences reflect
the tradeoff between flexibility in designing a new product versus ease
of implementation of procuring more of an existing product. The key
difference of the techniques results due to the sharing of generator
ramp rates between different reserve products. Most existing
implementations require dedicated capacity for each reserve product but
often do not require dedicated ramp capability. Using a new flexibility
reserve that can share ramp rates will typically be able to schedule
more reserve for a certain available generator capacity than applying
an ORDC to an existing product. This impacts the cost and effectiveness
of those reserves particularly in periods of system stress. Toggling
the ramp sharing constraint can be used to make either implementation
perform similarly as the other.
ABSCORES, A Novel Application of Banking Scoring and Rating for
Electricity Systems
Alberto J. Lamadrid L., Associate Professor, Lehigh University
(Bethlehem, PA)
Audun Botterud, Principal Research Scientist, Massachusetts Institute
of Technology (Cambridge, MA)
Jhi-Young Joo, Research Scientist, Lawrence Livermore National
Laboratory (Livermore, CA)
Shijia Zhao, Energy Systems Scientist, Argonne National Laboratory
(Lemont, IL)
This presentation discusses the basis for the establishment of an
Electric Assets Risk Bureau. We are developing different scores
customized according to the application required. We study the use of
financial models to determine the risk associated to individual assets
in the system. We present a model focused on managing operational risk,
and outline the methodology for risk metrics applied to high impact,
low probability (HILP) events. We distinguish between, first, public
risk, related to the physical provision of supporting services required
for the stability of the electricity system (i.e., ancillary services);
and second, financial risk, derived from positions taken by
participants with pecuniary repercussions. A key paradigm of our
framework is a focus on implementability of the approach (under
existing regulatory structures) and a method for dispute resolution
given potential decisions taken with the metrics proposed.
Recent Developments in the Day-Ahead and Real-Time Electricity Market
Design and Software Caused by the Higher Energy Costs and Emerging
Technologies--European Experience
Petr Svoboda, Engineer, Unicorn Systems a.s. (Prague, Czech Republic)
Europe has been dealing with the imbalance of production and
[[Page 40245]]
consumption for years. This has led to the development of the single
de-regulated electricity market to solve the barriers between the
individual states and provide the most cost-effective way to ensure
secure, sustainable, and affordable energy supply to the customers.
Recent changes in the market caused by the increase of the energy costs
and emergence of the new technologies have caused the fundamental
shifts in the market design and software enabling its operations. In
our presentation we would like to discuss the latest developments in
the areas of: 1. New algorithms of transmission capacity calculation
that have proven to increase the efficiency of capacity usage and
relevant economic welfare. 2. Development of the HVDC interconnectors
and their impact on the market efficiency and transmission costs. 3.
15-minute day-ahead markets. 4. Emergence of the integrated real-time
markets, new reserve products and multi-interval market clearing. 5.
Introduction of the flexibility instruments to the energy markets. 6.
Successful implementation of the hourly renewable certificates as the
next step towards clean energy transition.
Session W-A2 (Wednesday, June 28, 12:30 p.m.) (Commission Meeting Room)
System Resilience Through Electricity System Restoration and Related
Services
Douglas Wilson, Principal Analytics Engineer, GE (Edinburgh, United
Kingdom)
James Yu, Head of Future Networks, ScottishPower Energy Networks
(Glasgow, United Kingdom)
Ian Macpherson, Senior Innovation Manager, ScottishPower Energy
Networks (Glasgow, United Kingdom)
Marta Laterza, Power Systems Engineer, General Electric (Glasgow,
United Kingdom)
Marcos Santos, Senior Power Systems Engineer, General Electric
(Glasgow, United Kingdom)
Richard Davey, Senior Project Manager, General Electric (Glasgow,
United Kingdom)
Electricity system restoration following a partial or system-wide
outage is an essential service in the power system. There is a need to
apply new resources based on renewable resources to replace the
services that up to now have depended on fossil fuel generation. This
presentation describes a project led by SP Energy Networks in
collaboration with GE to demonstrate a co-ordinated restoration
approach in the distribution grid using a novel control approach
applied to a controlled zone with multiple resources. Live trials of
the approach in the SP Energy Networks power system are presented, as
well as results of testing the approach extensively in a hardware-in-
the-loop environment. The emerging weaknesses of the traditional
methodology were recognised in UK electricity regulation, which was
recently changed to include a requirement for 60% of customer load to
be restored within 24 hours on a regional basis, with all supplies
restored within 5 days (Electricity System Restoration Standard, 2021).
Previous restoration requirements were less onerous on the timeframes
and did not define geographic requirements. Since some regions now lack
large transmission-connected blackstart-capable plant for the
traditional top-down restoration approach, there is a need to harness
the capabilities that renewable and distributed generation and storage
can offer to address the deficit of system restoration capability. The
new service being developed and trialled involves starting distributed
generation and growing an island with customer load within the
distribution network. This island can be sustained by automated control
through managed load pickup as well unplanned disturbances with
existing distributed energy resources, battery storage and demand
response providing the control capability to keep the island in
balance. The blackstart zone may then be reconnected to the
transmission network if this is energised and can then contribute to
managing the power balance as the restoration of the wider system
continues. If appropriate, neighbouring islands can be connected
together, and the resulting larger island is capable of greater block
load pickup of active and reactive loads. One of the distinctive
benefits of the approach taken is that it uses diverse resources of
existing generation, storage and demand response capability that is
present and operational in the network for other day-to-day purposes.
These resources can be harnessed to provide the new electricity system
restoration services with few additional power assets. Inherently, some
devices can provide faster response than others, and large
instantaneous power, and some may be able to sustain an energy supply
while others have limited energy resource. Voltage support and short
circuit current are also considerations. A diversity of renewable
resources is useful to mitigate against individual resources being
unavailable e.g. low wind or low solar conditions. A key requirement
for the co-ordination of an electricity system restoration zone is a
wide area monitoring and control system that manages the power
balancing and switching of the network to automate the process of
growing and sustaining the power island. The approach being trialled
includes a SCADA/distribution management system with the topology
information for network switching, together with a synchrophasor based
wide area control system that manages the balancing, frequency control
and resynchronization alignment of the network. Since the island is
small in comparison to the normal interconnection, a rapid response to
disturbances is required to maintain a stable frequency. Once a
distribution zone is instrumented with the measurement, communication
and control equipment to deliver the service, it is possible to use the
same infrastructure to offer further services to manage grid stability
in the more common circumstance of disturbances during grid-connected
operation.
Coordinated Cross-Border Capacity Calculation Through The FARAO Open-
Source Toolbox
Violette Berge, Vice President, Artelys Canada (Montr[eacute]al,
Canada)
Dr. Nicolas Omont, Vice President of Operations, Artelys (Paris,
France)
Cross-borders exchanges have taken a major role in European
strategy to achieve climate goals. The European Commission set a target
of 15% interconnections in 2030, meaning that each country should have
the physical capability to export at least 15% of their production.
Increasing exchanges makes short term planning more complex. In this
context, the French TSO (RTE) released an open-source toolbox FARAO to
perform Coordinated Capacity Calculation (CCC) and ensure the security
of supply. Artelys is a consultancy expert in power systems
optimization and carries out various projects around TSO operational
coordination in Europe. FARAO performs the optimization of both
preventive and curative remedial actions, including HVDC lines, phase-
shifter transformers and counter-trading but also topological actions.
It is operationally used for the exchanges between Italy and its
northern neighbors as well as between France, Spain and Portugal.
Artelys will present the algorithms of the FARAO toolbox and how they
are actually used to enable greater operational coordination amongst
the countries.
[[Page 40246]]
Advanced Scenario Selection Methods for Probabilistic Transmission
Planning Assessments
Dr. Eknath Vittal, Principal Technical Leader, EPRI (Palo Alto, CA)
Anish Gaikwad, Senior Program Manager, Electric Power Research
Institute (Palo Alto, CA)
Parag Mitra, Senior Technical Leader, Electric Power Research Institute
(Palo Alto, CA)
Given the temporal and spatial characteristics of extreme weather
events, developing transmission planning scenarios, i.e., snapshots of
instantaneous operational conditions, is a challenging problem. It
requires a multi-model assessment that links long-term planning models
that capture the operational performance of the system (resource
adequacy and production cost modeling) to the future meteorological
projections that will inform the impacts of weather and extreme events.
Scenario generation and analysis is computationally and labor
intensive. Identifying snapshot conditions for future system states can
be challenge. This presentation will highlight and detail an EPRI
application that helps transmission planners identify critical power
flow conditions from operational simulations such as production cost
simulations. The EPRI High-Level Screening (HiLS) for Data Analytics
tool allows planners to apply statistical analysis to large dataset
that capture the operational performance of the system. The tool allows
for the data to be organized into clusters of similar operating
conditions reducing the dimensionality of the state space. As an
example, an operational simulation of 8760 hours can be reduced to 10
operating hours that capture 95% of the variability seen over the
course of the year. As uncertainty and variability increase on both the
generation and load, developing methods and processes to understand the
conditions that present the most challenging reliability and stability
conditions will be critical. The HiLS tools, provides transmission
planners a platform that can help them organize and visualize data
representing future operational conditions of the system that considers
both load variability and generator availability.
Incorporating Climate Projections Into Grid Models: Bridging the Data
Gap To Capture Weather Dependent Representative and Extreme Events and
Corresponding Uncertainties
Dr. Zhi Zhou, Principal Computational Scientist, Argonne National
Laboratory (Lemont, IL)
Dr. Neal Mann, Energy Systems Engineer, Argonne National Laboratory
(Lemont, IL)
Yanwen Xu, Graduate Student, University of Illinois at Chicago, Urbana-
Champaign (Champaign, IL)
Zuguang Gao, Graduate Student, University of Chicago (Chicago, IL)
Dr. Akintomide Akinsanola, Assistant Professor, University of Illinois
at Chicago (Chicago, IL)
Dr. Todd Levin, Team Lead, Argonne National Laboratory (Lemont, IL)
Dr. Jonghwan Kwon, Energy Systems Engineer, Argonne National Laboratory
(Lemont, IL)
Dr. Audun Botterud, Senior Energy Systems Engineer, Argonne National
Laboratory (Lemont, IL)
It is crucial to consider high-fidelity weather data and climate
projections in grid models in order to capture future weather trends,
extremes, and uncertainties. However, traditional power system studies
often overlook many of these considerations and rely solely on
historical weather data. To address this challenge, we develop a
computationally manageable framework to process high-quality
representations of climate data for use with power system models. The
framework includes a three-stage architecture to select representative
regions and periods, and also identify periods of extreme weather
conditions after translating climate data (temperature, wind-speed,
etc.) into grid inputs (load, power generation profiles and outage
probabilities). The framework also models and represents uncertainty of
future weather events based on ensembles of climate model simulations.
The outcome of the framework is a set of processed grid inputs in time
series format that capture the impact of climate features on the
system. This includes grid inputs directly converted from weather
variables at the cell level, as well as those from representative
regions and time periods, those representing the impact from extreme
weather events, and their associated uncertainties. We apply this
computational framework to translate downscaled climate projections
generated by three different global climate models, encompassing over
60 different weather variables at 12-km geographic and 3-hour temporal
resolution for all North America. We then demonstrate how consideration
of high-quality climate-driven grid inputs in electricity system models
impacts optimal long-term planning decisions. Capturing future weather
conditions and associated uncertainties is becoming important as power
systems, and their associated markets, are being impacted by both
efforts to decarbonize the effects of a changing climate. These are
also important considerations when updating market designs to maintain
reliability and economic efficiency as the underlying power system
evolves. In addition, capturing weather uncertainty is critical for
risk-aware decision making. Therefore, this work provides a valuable
resource for power system modelers by bridging the gap between climate
models and grid models to help ensure that long-term system planning
decisions are informed by the impacts of future climate conditions.
Session W-B2 (Wednesday, June 28, 12:30 p.m.) (Hearing Room One)
Enhancing Decision Support for Electricity Markets With Machine
Learning
Yury Dvorkin, Faculty, Johns Hopkins University (Baltimore, MD)
Robert Ferrando, Graduate Assistant, University of Arizona (Tucson, AZ)
Laurent Pagnier, Assistant Professor, University of Arizona (Tucson,
AZ)
Zhirui Liang, Ph.D. Student, Johns Hopkins University (Baltimore, MD)
Daniel Bienstock, Professor, Columbia University (New York, NY)
Michael Chertkov, Professor, University of Arizona (Tucson, AZ)
This presentation describes how machine learning can be leveraged
to enhance computational speed of day-ahead and real-time unit
commitment and optimal power flow routines, which are at the core of
market-clearing procedures in US ISOs. Our machine learning
architecture embeds both power flow physics and market design
properties (e.g., cost recovery and revenue adequacy) into the training
stage, which increases accuracy of computations and preserves a
relationship between primal (dispatch) and dual (prices) variables. The
accuracy and scalability of the proposed method is tested on a
realistic 1814-bus NYISO system with current and future renewable
energy penetration levels. We also demonstrate ~100x gain in
computations relative to traditional optimization approaches.
Synergistic Integration of Machine Learning and Mathematical
Optimization for Sub-Hourly Unit Commitment
Jianghua Wu, Ph.D. Candidate, University of Connecticut (Vernon, CT)
Dr. Zongjie Wang, Assistant Professor, University of Connecticut
(Storrs, CT)
Dr. Yonghong Chen, Consulting Advisor, Midcontinent ISO (Carmel, IN)
[[Page 40247]]
Dr. Bing Yan, Assistant Professor, Rochester Institute of Technology
(Rochester, NY)
Dr. Mikhail Bragin, Assistant Project Scientist, University of
California, Riverside (Riverside, CA)
The integration of intermittent renewables into power systems
presents significant challenges for operators due to increased
uncertainties and greater intra-hour net load variability. Sub-hourly
Unit Commitment (UC) has been suggested as a solution to quickly
respond to changes in electricity supply and demand, which is more
complicated than hourly UC because of a higher number of time periods,
and higher dependencies among coupled periods. Traditional optimization
methods could be time-consuming while machine learning (ML) may have
additional feasibility concerns. To address these challenges, a hybrid
approach based on synergistic integration of ML and optimization is
developed. This novel approach adopts our recent decomposition and
coordination Surrogate Absolute-Value Lagrangian Relaxation (SAVLR)
method with efficient coordination and accelerated convergence. ML is
then used to quickly predict SAVLR subproblem solutions. Compared to
those of the original overall problem, subproblem solutions are much
easier to learn. Nevertheless, predicting ``good'' subproblem solutions
is still challenging because of the ``jumps'' of binary decisions and
many types of unit-level constraints. To overcome these issues, a
generic ML model, embedding recurrent neural networks (RNNs) and the
Attention mechanism in the encoder-and-decoder structure, is developed.
Because of the features of RNNs and Attention, this generic model can
learn different subproblem solutions to reduce the training effort, and
can provide time-based predictions to capture dependencies. In
addition, to resolve the limitation of ML in handling constraints, a
rule-based feasibility layer is incorporated in the predicting process,
ensuring feasibility with respect to unit-level constraints. Testing on
the IEEE 118-bus system demonstrates the effectiveness of our approach,
providing feasible and accurate subproblem solutions quickly, and
obtaining near-optimal overall solutions efficiently.
Boosting Power System Operation Economics Via Closed-Loop Predict-and-
Optimize
Dr. Lei Wu, Anson Wood Burchard Chair Professor, Stevens Institute of
Technology (Hoboken, NJ)
Xianbang Chen, Ph.D. Candidate, Stevens Institute of Technology
(Hoboken, NJ)
By and large, power system operations are implemented by
Independent System Operators (ISO) in an open-loop predict-then-
optimize (O-PO) process. First, the uncertainty realizations (e.g.,
renewable energy availability) are predicted as accurately as possible.
Taking the predictions as inputs, day-ahead unit commitment and real-
time economic dispatch problems are then optimally resolved for
determining the operation plan (i.e., optimization). The operation goal
is to achieve the minimum system operation cost, i.e., the optimal
operation economics. However, the operation economics could suffer from
the open-loop process because its predictions may be myopic to the
optimizations, i.e., the predictions seek to improve the immediate
statistical prediction errors (i.e., accuracy-oriented) instead of the
ultimate operation economics. To this end, we propose to improve
operation economics by closing the open loop between the prediction and
the optimization, i.e., a closed-loop predict-and-optimize (C-PO) idea.
Specifically, two C-PO frameworks are designed, including a feature-
driven C-PO framework and a bilevel mixed-integer program (MIP) C-PO
framework. Their core is to feed the induced operation cost back for
training the predictor and measuring the prediction quality with the
operation cost (i.e., cost-oriented). As a result, the prediction and
the optimization can be implemented jointly in a single framework.
Based on real-world data, the feature-driven C-PO is compared to the
traditional O-PO, showing noticeable improvement in operation
economics, although with slightly compromised prediction accuracy for
certain cases. The experiments on a large-size system show that the C-
PO has potential in a real-world application. The bilevel MIP C-PO is
more versatile than the feature-driven C-PO. Based on an IEEE 118-bus
system, the bilevel MIP C-PO is compared to the state-of-the-art
methods of handling uncertainties, i.e., stochastic programming and
robust optimization. The case studies show that the bilevel MIP C-PO is
economically competitive with the state-of-the-art methods but is more
compatible with the current operational practice.
Privacy-Preserving Synthetic Dataset Generation for Power Systems
Research
Dr. Vladimir Dvorkin, Postdoctoral Fellow, Massachusetts Institute of
Technology (Cambridge, MA)
Dr. Audun Botterud, Principal Research Scientist, Massachusetts
Institute of Technology (Cambridge, MA)
Power systems research heavily relies on the availability of real-
world power system datasets (network parameters, time series, etc.).
However, data owners, such as system operators, are often hesitant to
share their data due to valid security and privacy concerns. To
overcome these challenges, we have developed state-of-the-art
algorithms that enable the synthetic generation of optimization and
machine learning datasets for the power systems industry. Our
algorithms take real-world datasets as input and output their
synthetic, perturbed versions that maintain the accuracy of the
original data on specific problem classes, such as power system
dispatch and wind power forecasting. Importantly, the original data
remains undisclosed, effectively controlling the privacy risk in data
releases. To ensure privacy preservation, we employ rigorous
perturbation techniques of differential privacy that strictly control
the amount of privacy loss. Furthermore, we preserve the accuracy of
original data through post-processing convex optimization. Our
algorithms have many applications, including synthetic generation of
transmission parameters and renewable generation records. We have open-
sourced our algorithms to encourage their use by interested parties.
For more information, please visit our GitHub repository at <a href="https://github.com/wdvorkin/SyntheticData">https://github.com/wdvorkin/SyntheticData</a>.
Session W-A3 (Wednesday, June 28, 3:30 p.m.) (Commission Meeting Room)
Parallel Interior-Point Solver for Security Constrained ACOPF Problems
on SIMD/GPU Architectures
Dr. Mihai Anitescu, Senior Mathematician, Argonne National Laboratory
(Lemont, IL)
Fran[ccedil]ois Pacaud, Assistant Professor, Ecole des Mines (Paris,
France)
Michel Schanen, Computer Scientist, Argonne National Laboratory
(Lemont, IL)
Sungho Shin, Postdoctoral Scientist, Argonne National Laboratory
(Lemont, IL)
Daniel Adrian Maldonado, Assistant Energy Systems Scientist, Argonne
National Laboratory (Lemont, IL)
We investigate how to port the standard interior-point method for
security constrained ACOPF problems, which are block-structured
nonlinear programs with state equations, on SIMD/GPU architectures.
Computationally, we decompose the interior-point algorithm into two
[[Page 40248]]
successive operations: the evaluation of the derivatives and the
solution of the associated Karush-Kuhn-Tucker (KKT) linear system. Our
method accelerates both operations using two levels of parallelism.
First, we distribute the computations on multiple processes using
coarse parallelism. Second, each process uses a SIMD/GPU accelerator
locally to accelerate the operations using fine-grained parallelism.
The KKT system is reduced by eliminating the inequalities and the state
variables from the corresponding equations, to a dense matrix encoding
the sensitivities of the problem's degrees of freedom, drastically
minimizing the memory exchange. Our experiments on SIMD/GPU with
security-constrained AC optimal power flow problem show that the method
can achieve a 50x speed-up compared to the state-of-the-art method.
The Need for More Rigorous Calculation of Shadow Prices and LMPs
Dr. Xiaoming Feng, Research Fellow, Hitachi Energy (Raleigh, NC)
LMPs (locational Marginal Prices) are used in nodal electricity
markets to determine payments or charges to market participants. Due to
the great monetary impact, it is imperative LMP is defined rigorously
and calculated consistently. It has been observed the current method of
shadow price and LMP calculation could produce values that are non-
unique under certain conditions, which might signal non-economic
incentives to the market. We start with formal definitions for shadow
price and LMP and present the properties of the perturbation functions
and their computational consequences. We use simple examples to
illustrate the discrepancy between theoretical shadow price and the
shadow price calculated by state-of-the-art optimization solvers. From
the discussion, we make the case for more rigorous calculation of both
shadow prices and LMPs.
Real-Time Market Enhancements for Reliability and Efficiency
Dr. Mort Webster, Professor of Energy Engineering, Pennsylvania State
University (University Park, PA)
Dr. Anthony Giacomoni, Manager, Advanced Analytics, PJM Interconnection
(Audubon, PA)
Aravind Retna Kumar, Ph.D. Candidate, Pennsylvania State University
(University Park, PA)
Sushant Varghese, Ph.D. Candidate, Pennsylvania State University
(University Park, PA)
Shailesh Wasti, Ph.D. Candidate, Pennsylvania State University
(University Park, PA)
The projected trends in the U.S. power system, increasing wind and
solar generation and retiring fossil fuel generation, will increase the
net load variability and forecast uncertainty over the next several
decades. There has been considerable research focusing on how to
provide more flexibility to the power system. Within this line of
research, numerous market design proposals have been explored: multi-
interval dispatch, ramp products, stochastic market clearing, an
increase in flexible resources (virtual power plants (VPP), energy
storage). Although flexibility is often cited as an objective the
outcomes of concern are reliability (unserved demand and reserve
shortages), efficiency (reducing bid production cost and uplift
payments), curtailment of renewable generation, and incentives for
future flexible resources (i.e., price formation). In the U.S.,
Independent System Operator (ISO) and Regional Transmission
Organization (RTO) real-time market clearing and operations have the
following properties: they operate on a rolling horizon basis
throughout the operating day, face changing forecasts throughout the
day with forecast errors, and frequently solve a real-time unit
commitment (RUC), which is separate from the real-time dispatch. In
contrast, most of the analysis and academic literature on market design
enhancements neglect one or more of these characteristics in their
analysis framework. The separation of commitment from dispatch raises
the question: which market enhancement in which clearing engine? In
this work, we present a simulation framework for the PJM wholesale
energy markets with a rolling horizon and forecast errors.
Specifically, we simulate the solution of the day-ahead market,
followed by PJM's Intermediate-Term Security Constrained Economic
Dispatch (IT-SCED) (real-time commitment process) every 15 minutes and
PJM's Real-Time Security Constrained Economic Dispatch (RT-SCED) (real-
time dispatch) every 5 minutes throughout the operating day. Net load
forecasts change every 5 minutes. We use this framework to simulate
several of the commonly discussed market enhancements applied to either
IT-SCED, RT-SCED, or both. We consider multi-interval dispatch, ramp
products, and stochastic market clearing. Our results demonstrate that
market design changes are most successful if they addresses both
commitment (bringing enough capacity and operating range online) and
dispatch (using the online operating range effectively).
Economics of Grid-Supported Electric Power Markets: A Fundamental
Reconsideration
Dr. Leigh Tesfatsion, Research Professor of Economics, Courtesy
Research Professor of Electrical & Computer Engineering, Iowa State
University (Ames, IA)
U.S. RTO/ISO-managed wholesale power markets operating over high-
voltage AC transmission grids are transitioning from heavy reliance on
fossil-fuel based power to greater reliance on renewable power. This
presentation highlights four conceptually-problematic economic
presumptions reflected in the legacy core design of these markets that
are hindering this transition. The key problematic presumption is the
static conceptualization of the basic transacted product as grid-
delivered energy (MWh) competitively priced at designated grid delivery
locations during successive operating periods, supported by ancillary
services. The presentation then discusses an alternative conceptually-
consistent ``Linked Swing-Contract Market Design'' that appears well-
suited for the scalable support of increasingly decarbonized grid
operations with more active participation by demand-side resources.
This alternative design entails a fundamental switch to a dynamic
insurance focus on advance reserve procurement permitting continual
balancing of real-time net load. Reserve consists of the guaranteed
availability of diverse power-path production capabilities for possible
RTO/ISO dispatch during future operating periods, as protection against
volumetric grid risk. Each reserve offer submitted by a dispatchable
power resource m to a forward reserve market M(T) for a future
operating period T is a two-part pricing swing-contract in firm or
option form that permits m to ensure its revenue sufficiency.
Session W-B3 (Wednesday, June 28, 3:30 p.m.) (Hearing Room One)
Simulation of Wholesale Electricity Markets With Capacity Expansion and
Production Cost Models To Understand Feedback Between Short Term Market
Procedures and Long Term Investment Incentives
Dr. Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)
Dr. Abhishek Somani, Electrical Engineer, Pacific Northwest National
Laboratory (Richland, WA)
Dr. Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory (Bel Air, MD)
[[Page 40249]]
Diane Baldwin, Project Manager, Pacific Northwest National Laboratory
(Richland, WA)
Wholesale electricity markets are undergoing rapid changes,
including variability and uncertainty and low prices from wind and
solar, load flexibility and price responsiveness, distributed energy
resources, energy storage, and revenue adequacy concerns. In response
to these changes, enhancements to electricity market procedures have
been proposed, including new reserve product, sloped reserve demand
curves, multi-settlement forward markets, and stochastic modeling in
market clearing optimization engines. These enhancements have the
potential to improve operational outcomes in the short term time scale
of hours to days by enabling better market responses to the changing
market conditions. But they also affect the long run incentives for
investment in grid equipment that ultimately result in the mix and
capacity of various grid technologies. This mix in turn influences
short term market conditions. We use linked models of capacity
expansion and production cost to explore this feedback between short
term and long term market conditions and to shed light on how this
feedback affects the assessment of market enhancements to address
changing market conditions.
Making the Right Resource Choice Requires Making the Right Model Choice
Dr. Rodney Kizito, Senior Manager, Ascend Analytics (Boulder, CO)
Gary W. Dorris, Ph.D., CEO, Ascend Analytics (Boulder, CO)
David Millar, Director of Consulting Services, Ascend Analytics
(Boulder, CO)
Production cost modeling simulates the operation of electric
systems. It provides a lens into a highly uncertain future, allowing
utilities to craft strategy and make critical decisions for their
customers, shareholders, and stakeholders. The power and acuity of this
lens will determine what resources will be deemed the most economic to
provide a reliable, lower-carbon supply portfolio. Resource planning
using production cost models that simulate the operation of power
systems, once a straightforward exercise of deciding how many new power
plants would be needed to meet future load growth, has become a much
more complicated and challenging enterprise. The dramatic decline in
the cost of renewables and storage technologies and the societal push
for decarbonization means planners must model more complex and
uncertain portfolio options. Renewables and their meteorologically
determined fuel supply are creating new dynamics that highlight the
need for more powerful modeling tools to capture the increasing
variability in the power supply and the ensuing effect on market price
volatility. This presentation highlights the benefits of using a new
class of resource planning models to plan for a decarbonized future.
Utilities, regulators, independent system operators, and other industry
stakeholders rely heavily on modeling to support decision making for
the allocation of scarce capital resources, as well as to ensure that
the right resources are available to maintain a high level of
reliability and resilience. This presentation argues that the older
generations of models that remain widely in use today fail to capture
the emerging dynamics of a power grid supplied primarily by renewable
energy. For this reason, industry decision makers are unknowingly
burdened by ``model-limited choice,'' which can lead to imprudent
investments in assets liable to become functionally useless and
ultimately disallowed. This presentation also provides a new
terminology to classify a model's ability to capture the new market
dynamics, high-definition production cost models (HD PCMs) versus
traditional production cost models (PCMs). HD PCMs use simulation to
capture the stochastic nature of load and electricity production
generated by renew able energy sources, as well as to drill down to a
5-minute level of temporal and spatial (i.e., nodal) granularity to
capture the flexibility requirements of renewable integration. Further,
HD PCMs mimic real-world uncertainty by simulating imperfect foresight
of future system conditions between the day-ahead forecast and the
real-time dispatch. Traditional PCMs are highly simplified because they
were developed when computing power was a significant limitation.
Today, resource planners can take advantage of the rapid increase in
computing power provided by distributed computing to upgrade their
analytical platforms to enable HD PCMs that provide more robust
analysis.
Transmission Shortage Pricing By MW-Mile Based Demand Curve
Sina Gharebaghi, Graduate Research Assistant, Pennsylvania State
University, Hitachi Energy (University Park, PA)
Dr. Xiaoming Feng, Research Fellow, Hitachi Energy (Raleigh, NC)
ISOs use transmission demand curves (TDC) in security constrained
unit commitment (SCUC) to relax transmission constraints when no
feasible solution exists with hard transmission constraints. TDC is a
penalty curve administratively specified as a function of the amount of
MW violation of the transmission line's limits. Use of TDCs to ensure
non-empty feasible solution space can result in excessively high LMP
when multiple TDCs are active. Researchers have studied a transmission
constraint screening approach to remove `redundant constraints' of
serially connected transmission lines before the pricing run to avoid
the accumulation of high shadow prices over multiple redundant
constraints for LMP calculation. The screening approach alleviates to a
large degree the occurrence of excessive LMP but has subtle and
significant unintended consequences with respect to SCUC solution
stability. We propose an alternative approach using MW-Mile based TDC
to solve the transmission constraint violation problem and eliminate
the root cause of excessive LMP without the need to remove redundant
constraints. We discuss the economic justification of the MW-Mile based
TDC approach and its advantage of solution stability with illustrative
examples.
Grid OS--A Modern Software Portfolio for Grid Orchestration
Renan Giovanini, Ph.D., MBA, Transmission Product Marketing Director,
General Electric (Edinburgh, United Kingdom)
Joseph Franz, Senior Marketing Manager, General Electric (Melbourne,
FL)
The 21st century has brought new challenges for Transmission and
Distribution Operators that were hardly perceived in the turn of the
century. There have been fast increases in bulk and micro renewable
resources in conjunction with international agreements on
CO<INF>2</INF> emission targets. Severe droughts, and more frequent
floods happening in the same country are driving needs also. An
increasing number of changing weather patterns creating disruptions at
several levels. Data tsunami has been created due to increasing types
and number of sensors installed in the field. The grid itself was
initially designed in the early 1900s based on a uni-directional flow
requirement now is called to become bi-directional. Previous electric
software solutions were created very organically since late 1970s/early
1980s addressing
[[Page 40250]]
use-cases from that era. New tools were created over time, but always
bolted-on to existent solutions. Energy Management Systems became more
and more complex and started to present challenges in terms of
scalability and maintainability leading to increasing staff and costs.
Previous well defined siloes between Generation, Transmission and
Distribution are becoming more blurred. In order to address all of
these challenges, utilities and software companies started a journey to
re-invent itself. Based on the most recent digital technologies, these
companies created new modular and composable solution prepared for
ultra-scaling and immense amounts of data ready to leverage the most
modern mathematical algorithms and artificial intelligence methods
available to date for assisted and automated control. The need for
project executions in months as opposed to years has been taken
carefully in consideration, creating a software solution ready for
faster time-to-value. These solutions are already in production at a
few customers and a number of new use-cases are currently under proof-
of-concept, development or available for productization. The
presentation will cover some of these latest software developments and
highlight regulatory challenges to slowing the adoption of these
technologies by utilities: 1. A new market system prepared to validate
& clear more frequent and increasing number of bids with smaller
amounts of power; 2. Digital twin technologies such as digital dynamic
line ratings ready to integrate electrical and weather data to provide
real-time and forecast ampacity for transmission lines integrated to
real-time and look-ahead security assessment systems; 3. Advanced
forecasting solutions based on AI for (1) renewable power production at
T&D levels and (2) outage predictions for improved crew allocation and
faster restoration times; 4. Optimal system restoration management in
real-time in assisted and automated modes; 5. Exploration of
Distributed Energy Resource to supply grid services at transmission
level such as grid stabilization and blackstart restoration.
Day 3--Thursday, June 29
Session H1 (Thursday, June 29, 9:30 a.m.) (Commission Meeting Room)
Integration of DER Aggregations in ISO-Scale SCUC Models
Dr. Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory (Bel Air, MD)
Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)
Abhishek Somani, Economist, Pacific Northwest National Laboratory
(Richland, WA)
Eran Schweitzer, Electrical Engineer, Pacific Northwest National
Laboratory (Richland, WA)
Rabayet Sadnan, Electrical Engineer, Pacific Northwest National
Laboratory (Richland, WA)
Nawaf Nazir, Electrical Engineer, Pacific Northwest National Laboratory
(Richland, WA)
Soumya Kundu, Electrical Engineer, Pacific Northwest National
Laboratory (Richland, WA)
FERC issued Order 2222 in September 2020, which will require all
ISOs in the U.S. to implement participation models for DER aggregators.
Among other requirements, this rule required ISOs to lower the
participation threshold for wholesale market participation to 0.1 MW.
Wider participation of these resources can bring significant benefits
to the grid, such as by locating energy supply closer to demand,
opening up more participation from the demand side, and providing an
additional flexibility source to balance intermittent renewables.
However, DER aggregations will have unique characteristics that may
pose challenges to the large-scale security-constrained unit commitment
(SCUC) software used by ISOs. This presentation will focus on the
formulation of a new mathematical model to represent the internal
constraints of a DER aggregator and the study design that is intended
to better understand the challenges associated with DER integration.
Current-Voltage AC Optimal Power Flow for Unbalanced Distribution
Network
Dr. Mojdeh Khorsand Hedman, Assistant Professor, Arizona State
University (Tempe, AZ)
Zahra Soltani, Ph.D. Candidate, Arizona State University (Tempe, AZ)
Dr. Shanshan Ma, Postdoctoral Research Scholar, Arizona State
University (Las Vegas, NV)
With proliferation of distributed energy resources (DERs),
distribution management systems (DMSs) need to be advanced in order to
enhance the reliability and efficiency of modern distribution systems.
This work proposes novel nonlinear and convex AC optimal power flow
(ACOPF) models based on current-voltage (IVACOPF) formulation for an
unbalanced distribution system with DERs. In the proposed formulation,
untransposed distribution lines, shunt elements of distribution lines,
and detailed representation of distribution transformers and DERs are
modeled. The proposed nonlinear IVACOPF model is linearized and
convexified using the Taylor series. The performance of the proposed
nonlinear and convex IVACOPF approaches is compared with OpenDSS and
the widely used LinDistFlow method for modeling unbalanced distribution
systems. The proposed accurate convex IVACOPF model has multiple
applications for distribution system management, planning, and
operation. Applications of the proposed model on two key parts of
advanced DMS, (i) DERs scheduling and (ii) simultaneous topology
processor and state estimation, will be presented. Two models are
developed including Quadratic Programming (QP) and linear programming
(LP) for performing the distribution state estimation. The performance
of the methods is compared. The proposed models are tested using
distribution feeder of an electric utility in Arizona.
Empowering Electricity Markets Through Distributed Energy Resources and
Smart Building Setpoint Optimization: A Graph Neural Network-Based Deep
Reinforcement Learning Approach
Dr. You Lin, Postdoctoral Associate, Massachusetts Institute of
Technology (Cambridge, MA)
Dr. Audun Botterud, Principal Research Scientist, Massachusetts
Institute of Technology (Cambridge, MA)
Dr. Daisy Green, Postdoctoral Associate, Massachusetts Institute of
Technology (Cambridge, MA)
Dr. Leslie Norford, Professor, Massachusetts Institute of Technology
(Cambridge, MA)
Dr. Jeremy Gregory, Executive Director of Climate and Sustainability
Consortium, Massachusetts Institute of Technology (Cambridge, MA)
Smart buildings play a pivotal role in the electricity market by
boosting energy efficiency and demand flexibility by implementing
advanced control strategies. In this study, a setpoint optimization
model is proposed using a graph neural network-based deep reinforcement
learning (DRL) algorithm that considers thermal exchanges among various
zones within buildings. By intelligently scheduling the day-ahead
temperature setpoints and adjusting the real-time setpoints in response
to dynamic conditions and price signals, DRL-based controllers can
optimize energy consumption while reducing overall costs. This
strategic energy
[[Page 40251]]
management not only benefits building occupants but also bolsters the
electricity grid through load balancing and the provision of essential
grid services. Through the testbed of MIT campus buildings, it is
demonstrated that smart buildings employing DRL for setpoint
optimization contribute to a more efficient, reliable, and sustainable
electricity market.
Multi-Timescale Operations of Nuclear-Renewable Hybrid Energy Systems
for Reserve and Thermal Products Provision
Jie Zhang, Associate Professor, University of Texas at Dallas
(Richardson, TX)
Jubeyer Rahman, Ph.D. Student, University of Texas at Dallas
(Richardson, TX)
This talk will present an optimal operation strategy of a nuclear-
renewable hybrid energy system (N-R HES), in conjunction with a
district heating network, which is developed within a comprehensive
multi-timescale electricity market framework. The grid-connected N-R
HES is simulated to explore the capabilities and benefits of N-R HES of
providing energy products, different reserve products, and thermal
products. An N-R HES optimization and control strategy is formulated to
exploit the benefits from the hybrid energy system in terms of both
energy and ancillary services. A case study is performed on the
customized NREL-118 bus test system with high renewable penetrations,
based on a multitimescale (i.e., three-cycle) production cost model.
Both day-ahead and real-time market clearing prices are determined from
the market model simulation. The results show that the N-R HES can
contribute to the reserve requirements and also meet the thermal load,
thereby increasing the economic efficiency of N-R HES (with increased
revenue ranging from 1.55% to 35.25% at certain cases) compared to the
baseline case where reserve and thermal power exports are not
optimized.
Session H2 (Thursday, June 29, 12:30 p.m.) (Commission Meeting Room)
Optimizing Stand-Alone Battery Storage Operations Scheduling Under
Uncertainties in German Residential Electricity Market Using Stochastic
Dual Dynamic Programming
Pattanun Chanpiwat, Doctoral Candidate, University of Maryland (College
Park, MD) & Aalto University (Espoo, Finland) (Silver Spring, MD)
Fabricio Oliveira, Ph.D., Associate Professor, Aalto University (Espoo,
Finland)
Steven A. Gabriel, Ph.D., Full Professor, University of Maryland
(College Park, MD)
We present a new variation of the stochastic dual dynamic
programming (SDDP) algorithm for solving multistage, convex stochastic
programming problems considering uncertainties such as electricity
prices, variable renewable energy generation, and residential demand in
the electricity market. We approximate the convex expected-cost-to-go
functions via a linear policy graph, to obtain optimal operational
strategies for the battery storage usage of residential households. We
develop a heuristic algorithm (i.e., executable on edge-computing
devices located at the households) of a residential electricity network
with a flexible structure that allows residents to efficiently hedge
their electricity consumption via community-shared battery storage
while accounting for uncertainties and limitations of the energy
system. We provide an economic assessment and insights into battery
storage scheduling strategies and the model capabilities through case
studies on a test network model of Southern German residential
households. The results are compared with other mathematical models
including a multistage stochastic convex optimization model with the
assumptions of a perfect information case and/or a business-as-usual
case.
Integration of Hybrid Storage Resources Into Wholesale Electricity
Markets
Dr. Nikita Singhal, Technical Leader, Electric Power Research Institute
(Palo Alto, CA)
Rajni Kant Bansal, Ph.D. Candidate, Johns Hopkins University
(Baltimore, MD)
Dr. Erik Ela, Program Manager, Electric Power Research Institute (Palo
Alto, CA)
Dr. Julie Mulvaney Kemp, Research Scientist, Lawrence Berkeley National
Laboratory (Berkeley, CA)
Dr. Miguel Heleno, Research Scientist, Lawrence Berkeley National
Laboratory (Berkeley, CA)
Electric storage resources and other technologies that are co-
located and share a common point of interconnection are presently being
incorporated into bulk power systems in increasing numbers, with more
hybrid storage resources planned and under study within interconnection
queues. Such hybrid storage resources are predominantly seen being
combined with variable energy resources and are either being operated
as two separate resources or as a single integrated resource. Market
designers and system operators are presently researching ways to
effectively integrate hybrid storage resources into their existing
system operations and scheduling processes given the ambiguity around
their impacts, particularly when high levels of hybrid resources are
present. This research explores advanced market participation modeling
options for integrating utility-scale hybrid storage resources into
market clearing software in addition to discussing the economic and
reliability implications of the different modeling options. This
includes the consecutive impact of the participation models on the
market clearing software solution and the dispatch and revenue of
hybrid battery projects. The alternate participation models evaluated
in this research include two separate resources ISO-managed co-located
participation model, single integrated resource self-managed hybrid
participation model and two separate resources ISO-managed linked co-
located participation model.
Predicting Strategic Energy Storage Behaviors
Yuexin Bian, Ph.D. Student, University of California, San Diego (San
Diego, CA)
Ningkun Zheng, Ph.D. Student, Columbia University (New York City, NY)
Yang Zheng, Assistant Professor, University of California, San Diego
(San Diego, CA)
Bolun Xu, Assistant Professor, Columbia University (New York, NY)
Yuanyuan Shi, Assistant Professor, University of California, San Diego
(San Diego, CA)
Energy storage are strategic participants in electricity markets to
arbitrage price differences. Future power system operators must
understand and predict strategic storage arbitrage behaviors for market
power monitoring and capacity adequacy planning. This paper proposes a
novel data-driven approach that incorporates prior model knowledge for
predicting the behaviors of strategic storage participants. We propose
a gradient-descent method to find the storage model parameters given
the historical price signals and observations. We prove that the
identified model parameters will converge to the true user parameters
under a class of quadratic objective and linear equality-constrained
storage models. We demonstrate the effectiveness of our approach
through numerical experiments with synthetic and real-world storage
behavior data. The proposed approach significantly
[[Page 40252]]
improves the accuracy of storage model identification and behavior
forecasting compared to previous blackbox data-driven approaches.
Energy Storage Participation Algorithm Competition (ESPA-Comp)
Dr. Brent Eldridge, Electrical Engineer, Pacific Northwest National
Laboratory (Bel Air, MD)
Jesse Holzer, Mathematician, Pacific Northwest National Laboratory
(Richland, WA)
Abhishek Somani, Economist, Pacific Northwest National Laboratory
(Richland, WA)
Kostas Oikonomou, Electrical Engineer, Pacific Northwest National
Laboratory (Richland, WA)
Brittany Tarufelli, Economist, Pacific Northwest National Laboratory
(Laramie, WY)
Li He, Electrical Engineer, Pacific Northwest National Laboratory
(Richland, WA)
Energy Storage Participation Algorithm Competition (ESPA-Comp) is
an upcoming pilot competition that will challenge participants to
develop innovative algorithms for energy storage participation in
wholesale electricity markets. Energy storage technologies will play a
critical role in making sure we have access to reliable and low-cost
electricity. However, optimizing energy storage systems in wholesale
electricity markets is a complex task that requires sophisticated
algorithms to accurately predict electricity prices and account for the
physical constraints of energy storage technologies. ESPA-Comp aims to
bring together researchers, engineers, and students with expertise in
AI/ML, optimization, and economics to develop algorithms that can
effectively address these challenges. In this competition, participants
will ``operate'' an energy storage resource in a simulated wholesale
electricity market and will be awarded based on the profits they earn.
Participants will need to submit algorithms that generate strategic
offer curves, taking into account factors like weather, market
competition, and network congestion. Competition results will help us
to understand how different market designs can affect storage
incentives and support the efficient use of storage resources.
Session H3 (Thursday, June 29, 3:00 p.m.) (Commission Meeting Room)
Congestion Mitigation With Transmission Reconfigurations in the Evergy
Footprint
Dr. Pablo A. Ruiz, CEO and CTO, NewGrid, Inc. (Somerville, MA)
Derek Brown, Regulatory Affairs Manager, Evergy (Topeka, KS)
Jeremy Harris, Transmission Operations Planning Manager, Evergy
(Topeka, KS)
German Lorenzon, Senior Engineer, NewGrid (Somerville, MA)
Grant Wilkerson, Director of Business Development, Evergy (Kansas City,
MO)
Transmission needs are becoming more variable and are rising
rapidly, as shown by significant increases in congestion management
costs and in the frequency of transmission overloads. Further,
transmission capability has been critical during recent extreme events,
to support power transfers from less affected areas to the more
affected ones. 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 increase the transmission network
performance from both reliability and market efficiency perspectives.
At the same time, the use of reconfigurations remains limited. For
example, the usual practice in the Southwest Power Pool is to employ
known reconfigurations as a last resort, after resource redispatch is
exhausted and constraints are breached. This presentation will discuss
the reliability and cost saving impacts of reconfigurations implemented
in the Evergy footprint to mitigate congestion under the current SPP
practice, as well as illustrate additional benefits that could be
obtained if topology optimization opportunities were used proactively
to address congestion.
Optimal Transmission Expansion Planning With Grid Enhancing
Technologies
Swaroop Srinivasrao Guggilam, Senior Engineer, Electric Power Research
Institute (Knoxville, TN)
Alberto Del Rosso, Program Manager, Electric Power Research Institute
(Knoxville, TN)
The power system is evolving with a rapid increase in demand. It
provokes rethinking ways to increase generation and expand the system's
capacity to support it. This combination of fast-paced demand growth
and supply has made the planning and expansion of the transmission
system challenging in recent years. The futuristic hyperactive power
system grid needs to be versatile. The grid should be able to host a
variety of renewable energy resources, adapt to various system
conditions, be highly secured under extreme events, and be dynamically
responsive to make the power system reliable. All this is to be
achieved at minimal cost to the customers and efficiently. The
traditional transmission solutions will continue to be the backbone of
the power system transmission grid, but upcoming state-of-the-art grid-
enhancing technologies can significantly aid in supporting these ever-
changing power system grid requirements with optimal cost and improved
efficiency. Various grid-enhancing technologies include power flow
control devices such as SmartValve devices and phase shift
transformers, dynamic and adaptive transmission line ratings, and
optimal topology control. The increasing penetration of distributed
energy resources such as batteries also activates a different avenue to
pursue being able to support transmission expansion planning needs. The
term around the battery as a viable alternative is coined as a non-wire
alternative solution. In many utilities, it's necessary to assess the
non-wire alternative solutions such as batteries to meet FERC
requirements. Developing and analyzing these various modern
transmission solutions that work in tandem is challenging. One needs
proper technical characterization of these technologies and assess the
technology readiness. One also needs to evaluate its performance under
normal and extreme conditions, the flexibility to deploy and install
these technologies, calculate capital and operational costs, understand
different available control options for these devices, and analyze
potential limitations. Suitable analytical methods and high-performing
software tools are needed to run the optimization simulations to enable
integration and efficient use of these grid-enhancing technologies.
EPRI has developed a software tool called CPLANET (Controlled PLANning
Expansion Tool) that helps identify effective and low-cost solutions
for mitigating thermal overloads in a power system over various
operating scenarios. An optimal solution is determined from a given set
of candidate projects, including various grid-enhancing technologies
and traditional transmission expansion projects such as installing new
transmission lines or upgrading existing substations. The software uses
a mixed-integer linear programming formulation in the optimization
engine to identify the least-cost solution for the grid's various
physical and operating needs. The scope and goal of this presentation
are to discuss the ongoing efforts at EPRI's forefront around grid-
enhancing technologies. Showcase the current capabilities of the
CPLANET tool and
[[Page 40253]]
discuss case studies and share existing challenges and future goals.
The Key Role of Extended ACOPF-Based Decision Making for Supporting
Clean, Cost-Effective and Reliable/Resilient Electricity Services
Maria Iilic, Professor Emerita, Carnegie Mellon University (Pittsburgh,
PA)
Rupamathi Jaddivada, Director of Innovation, SmartGridz (Boston, MA)
Jeffrey Lang, Vitesse Professor, Massachusetts Institute of Technology
(Cambridge, MA)
Eric Allen, Director of Engineering, SmartGridz (Boston, MA)
Societal objectives are rapidly moving towards decarbonized,
affordable, and reliable/resilient electricity services. In this talk
we first revisit these objectives by identifying basic changes and the
related challenges taking place. In particular, decarbonization
requires planning and operations of the changing electric energy
systems so that seamless integration of clean resources, ranging across
wind, solar, nuclear, geothermal, and hydro, is enabled. Notably, this
must be done with an eye on generation adequacy. Also, these new
resources present locational issues (NIMBY) in operating the existing
power grid. Finally, the end users still must be served without
interruptions and without being exposed to wide-spread blackouts.
Similar challenges are related to ensuring cost-effective and reliable/
resilient services. Second, we show how an extended (robust, adaptive,
multi-temporal) ACOPF is essential for meeting these societal
challenges. Pretty much any of the new software needed (for wind
integration, resilient service, and preventing blackouts) requires
effective optimization tools for identifying the main bottlenecks/
obstacles to physical implementation and for advising operators and
planners regarding the most effective remedial actions (new investments
and/or flexible utilization). We illustrate potential benefits from
utilizing ACOPF as a basic means of supporting software tools needed
for meeting the societal challenges. We offer a taxonomy of such badly
needed tools and illustrate the role of extended ACOPF estimated
benefits on several real-world systems based on our work to-date.
Data & API Standards for Clean Energy Solutions and Digital Innovation
Priya Barua, Director of Market Policy and Innovation, Clean Energy
Buyers Institute (Washington, DC)
Ben Gerber, President & CEO, M-RETS (Minneapolis, MN)
There is an opportunity for energy attribute certificate (EAC)
issuing bodies in the U.S. and abroad to enable next generation carbon-
free electricity (CFE) procurement solutions that accelerate grid
decarbonization investments by capturing more attributes and better
serving as a digital ``platform of platforms''. Energy customers who
buy clean energy rely on EACs to assert ownership claims over each
megawatt-hour of CFE they procure for auditing, reporting, and
marketing purposes. EAC issuing bodies promote CFE procurement
integrity and validation by issuing, tracking, and canceling EACs,
which each represent a unique standardized tradable instrument
representing one megawatt-hour of verified CFE generation. By adopting
open data and automated programming interface (API) standards, EAC
issuing bodies can improve data access and solutions for customers.
This session will explore opportunities for EAC issuing bodies to
establish consistent, modern automated programming interfaces (APIs),
template legal agreements, and other tools that will make it easier for
data providers to deliver data and for users to update the status of
EACs through connected digital trading platforms-- enabling innovation
for CFE procurement solutions.
Mine Production Scheduling Under Time-of-Use Power Rates With Renewable
Energy Sources
Dr. Daniel Bienstock, Professor, Columbia University (New York, NY)
Amy Mcbrayer, Ph.D. Candidate, South Dakota School of Mines (Rapid
City, SD)
Andrea Brickey, Professor, South Dakota School of Mines (Rapid City,
SD)
Alexandra Newman, Professor, Colorado School of Mines (Golden, CO)
Renewable energy use on active and reclaimed mine lands has
increased dramatically in recent years. With mining companies focused
on increasing efficiencies, reducing carbon intensity, and developing
sustainable mining practices, opportunity exists to integrate data on
electricity usage and demand into mine production schedules to
capitalize on alternative energy sources and to take advantage of
favorable pricing strategies. Utilizing real data from an active coal
mine that has already integrated electric equipment into their loading
fleet, we show the impacts of (i) seasonal power price fluctuations on
a medium-term production schedule; and, (ii) hourly power price
fluctuations on a short-term extraction schedule. Results reveal the
economic potential both for: (i) the integration of renewable energy
sources on reclaimed and active mine lands; and (ii), the corresponding
synchronization of a production schedule with time-of-use energy
pricing contracts.
[FR Doc. 2023-13168 Filed 6-20-23; 8:45 am]
BILLING CODE 6717-01-P
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