Modern electricity markets face new sources of risk as renewables footprint increases, and price formation in the traditional sense will ultimately be subordinated to reliability quantification. The ORFEUS platform ascribes risk and costs to each asset’s contribution to system operational cost. Its major components include:

- Nodal load and generation scenarios
- Analysis of system operation costs
- Risk allocation and construction of system risk indices
- Securitization and risk transfer

Renewable energy resources reduce carbon footprint and marginal cost, but also introduce risk to the grid that is not fully accounted for under current operational paradigms. The fulcrum of the ORFEUS platform is a set of asset-specific modules which calibrate stochastic models of the joint behavior of forecasted and actual production, tailored to wind, solar and load. Models are linked through a highdimensional correlation constructor which renders spatial and temporal correlation structure tractable via LASSO-based methods and parametric representations of locational correlation structure (Gaussian Random Fields). The last figure shows examples of graphical LASSO correlation effects for NYISO and ERCOT zonal load forecast errors.

The ORFEUS simulation module takes the correlated asset models and produces large batch simulations. We focus on generating day-ahead scenarios at the 15- and 60-minute scale based on realistically coupled production and demand realizations based upon the forecasts available at run time. These simulations are critical inputs to existing SCUC and SCED software from which reliability cost indices are then constructed.

Zero-marginal-cost assets are usually guaranteed to be committed even if they create potentially costly externalities due to uncertain production. The ORFEUS risk-based cost module rigorously decomposes the results of the simulation batch into reliability costs by asset and zone using coherent risk measure methodologies ensuring that system operations allocate realized costs equitably.

The ORFEUS team and Industry Advisory Board are well-positioned to deploy and test the platform in production settings, ultimately launching Princeton Grid Analytics as the commercial delivery vehicle.

René Carmona, formerly the chair of the department of
Operations Research and Financial Engineering, is an associate
member of the Department of Mathematics, a member of the
Program in Applied and Computational Mathematics, and Director
of Graduate Studies of the Bendheim Center for Finance where
he oversees the Master in Finance program. He obtained a Ph.D.
in probability from Marseille University where he held his
first academic job. After time spent at Cornell and Princeton,
he moved to the University of California-Irvine in 1981, and
eventually back to Princeton University in 1995.

Professor Carmona is a Fellow of the Institute of
Mathematical Statistics, of the Society for Industrial and
Applied Mathematics, and of the American Mathematical Society.
He is the founding chair of the SIAM Activity Group on
Financial Mathematics and Engineering, a founding editor of
the Electronic Journal of Probability and Electronic
Communications in Probability, and the SIAM Journal on
Financial Mathematics. He is on the editorial board of several
peer-reviewed journals and book series.

His publications
include over one hundred articles and eleven books in
probability, statistics, mathematical physics, signal
analysis, and financial mathematics. He also developed
computer programs for teaching and research. He has worked on
the commodity and energy markets as well as the credit
markets, and he is recognized as a leading researcher and
consultant in these areas. Over the last decade his research
focused on the development of a probabilistic approach to Mean
Field Games and Mean Field Control. His two-volume book on the
subject, co-authored with F. Delarue, was the recipient of the
J.L. Doob Prize awarded every three years by the American
Mathematical Society.

Ronnie Sircar is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, and is affiliated with the Bendheim Center for Finance, the program in Applied and Computational Mathematics, and the Andlinger Center for Energy and the Environment. He received his doctorate from Stanford University, and taught for three years at the University of Michigan in the Department of Mathematics. His research interests center on Financial Mathematics, stochastic volatility models, energy markets and exhaustible resources, credit risk, asymptotic and computational methods, portfolio optimization and stochastic control problems, and stochastic differential games. He is a co-author of the book "Multiscale Stochastic Volatility for Equity, Interest-Rate and Credit Derivatives'', published by Cambridge University Press in 2011, and was founding co-editor-in-chief of the SIAM Journal on Financial Mathematics, from 2009-2015. He was made a Fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2020 for “contributions to financial mathematics and asymptotic methods for stochastic control and differential games”.

Mike Ludkovski is Professor and Chair at the Department of
Statistics and Applied Probability at University of California
Santa Barbara where he has been faculty since 2008. He
co-directs the UCSB Center for Financial Mathematics and
Actuarial Research and leads the Simulation and Control in
Finance and Insurance (SCiFI) Lab. Among his research
interests are stochastic modeling, quantitative finance,
energy markets, and applications of machine learning in
longevity and non-life insurance. He is a former Chair of the
SIAM Activity Group on Financial Mathematics and Engineering
and was the Co-Chair of the FM'12 and FM'16 conferences.

Ludkovski's research focuses on building statistical
emulators for renewable energy generation across multiple
spatial assets. He has also worked extensively on analyzing
competitive behavior in energy oligopolies, such as in the
context of long-term capacity expansion and
producers-consumers dynamic equilibrium.

Glen Swindle is Managing Partner at Scoville Risk Partners, an energy analytics firm. Glen has held senior positions at Constellation Energy from 2000 to 2004 where he ran the Strategies group for the merchant energy business and at Credit Suisse from 2004 to 2012 where he was co-head of electricity and natural gas trading. Previously he held tenured positions at UCSB and Cornell University, in addition to adjunct positions at New York University and Rutgers. Glen is the author of Valuation and Risk Management in Energy Markets (Cambridge University Press, 2014) and Natural Gas Trading in North America (Scoville Risk Partners, 2018). He holds a Ph.D. in Applied Mathematics from Cornell University, an M.S.E. in Mechanical Aerospace Engineering from Princeton, and a B.S. in Mechanical Engineering from Caltech.

Xinshuo Yang is a postdoctoral research associate of Operations Research and Financial Engineering at Princeton University. Previously he was a postdoctoral fellow at Colorado School of Mines, and a postdoctoral associate at National Renewable Energy Laboratory (NREL). He received his Ph.D. in Applied Mathematics from University of Colorado Boulder. His research interests include on stochastic modeling, financial mathematics for energy markets and numerical optimization.

Arvind is a postdoc at ORFE Princeton, having recently completed his PhD in Statistics at the University of Toronto, where he focused on applying stochastic control techniques to emissions markets.

Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) is a program at Advanced Research Projects Agency-Energy (ARPA-E).
Nowadays, the trade-off between a lower level of reliability - caused by rising renewable penetration - and a more affordable electricity to consumers is being addressed more and more while we mitigate to a zero-emission grid.
The main question is are the renewable energies posing any risks to the power grid?
PERFORM aims at optimizing the usage of assets in the United States electricity grid by precisely measuring the risks at the asset-level.

ARPA-E is an agency of the United States' Departments of Energy (DOE) to support and fund advanced researches and technologies in the field of energy.