2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
303
TC04
101D-MCC
Power System Operations Under
Increasing Uncertainty
Sponsored: Energy, Natural Res & the Environment,
Energy I Electricity
Sponsored Session
Chair: Antonio J. Conejo, Prof., The Ohio State University, 1971 Neil
Avenue, 286 Baker Systems Engineering, Columbus, OH, 43210,
United States,
Co-Chair: Ramteen Sioshansi, Ohio State University, 1971 Neil Avenue,
Columbus, OH, 43210, United States,
1 - Ramp Capability Modeling For Reliable And Efficient Integration
Of Renewable Energy
Congcong Wang, MISO, Carmel, IN, United States,
, Dhiman Chatterjee
With increasing penetration of renewable energy, net load variations and
uncertainties impose challenges to maintain real-time power balance. This
presentation highlights MISO’s recent development of Ramp Capability Product to
manage system ramping needs. It starts with an examination of recent market
evolutions that drive both operational and economic needs of resource flexibility
and then presents the design of Ramp Capability Product that systematically pre-
position resources with flexibility to meet future net load at a specified level of
confidence. More importantly, explicit price signals are developed to reflect the
underlying cost causation and provide economic incentives.
2 - Is Being Flexible Advantageous For Demands?
Farzaneh Abbaspourtorbati, EPFL, Lausanne, Switzerland,
Antonio J. Conejo,
Jianhui Wang, Rachid Cherkaoui
This paper analyzes the impacts of flexible demands on day-ahead market
outcomes in a system with significant wind power production. We use a two-
stage stochastic market-clearing model, where the first stage represents the
day-ahead market and the second stage the real-time operation. On one hand,
flexibility of demands is beneficial to the system as a whole since such flexibility
reduces the operation cost, but on the other hand, shifts in demands from peak
periods to off-peak periods may influence prices in such a way that demands may
not be willing to provide flexibility. Specifically, we investigate the impacts of
different degree of demand flexibility on day-ahead prices.
3 - Aggregating (almost) Symmetric Generators In Unit Commitment
Ben Knueven, University of Tennessee, Knoxville, TN, United
States, , Jim Ostrowski, Jean-Paul Watson,
Jianhui Wang
We consider a method to precisely aggregate symmetric ramping unconstrained
generators in unit commitment formulations. We apply the same methods to
nearly symmetric generators to create symmetric relaxations of the unit
commitment problem, and empirically test the strength of the relaxation. We
demonstrate massive computational improvements over the standard formulation
for the CAISO set of generators. Extensions to accelerate stochastic unit
commitment are also examined.
TC05
101E-MCC
Reliable Power System Design and Operations
Sponsored: Energy, Natural Res & the Environment, Energy I
Electricity
Sponsored Session
Chair: Bo Zeng, University of Pittsburgh, Benedum Hall 1009,
Pittsburgh, PA, 15261, United States,
1 - Tighter Modeling And Enhanced Solutions For Power System
Operations Under Uncertain Environment
Lei Wu, University of Clarkson,
In emerging power systems, as the generation side gets more distributed and the
demand side becomes more active, it is of critical importance to evaluate the
impacts of individual assets on the reliable and economic operation of power
systems. This presentation will highlight several key issues in the operation of
power systems with significant penetration of renewable energy and DR assets,
and discuss advanced modeling and optimization techniques, robust security-
constrained unit commitment (SCUC) models in particular, for enhancing the
reliability and economics of power system operations under uncertain
environment.
2 - Reliable Fuel Supply Chain Design
Bo Zeng, University of Pittsburgh,
Anna Danandeh, Brent Caldwell
To ensure reliable operations of a power plant, an optimization based fuel supply
chain model is developed and implemented.
TC06
102A-MCC
Data-Intensive Computational Methods for
Large-scale Infrastructure Systems
Sponsored: Data Mining
Sponsored Session
Chair: Adrian Albert, C3IoT, 1300 Seaport Boulevard, Suite 500,
Redwood City, CA, 94063, United States,
1 - Sparse Data Analytics For Modern Engineering Systems
Borhan Sanandaji, Risk Management Systems (RMS), Hall,
Newark, CA, 24061, United States,
Forecasting plays a vital role in reliable operation of modern engineering systems
such as smart grids and transportation systems. These systems are often large-
scale and generate a huge amount of data. It is, therefore, quite important to
come up with forecasting schemes that can deal with such high-dimensionality. In
this work, we propose a Sparse Spatio-Temporal Forecasting (SSTF) scheme
which exploits the intrinsic low-dimensionality and structure of the generated
data. We applied SSTF to predict wind speed, residential electric load, and solar
irradiance in different scenarios to prove its significance as compared to other
benchmark models.
2 - A Learning Based Method For Real Time Prediction Of
Cascading Failures
Yue Zhao, Stony Brook University, Stony Brook, NY, United States,
, Jianshu Chen
Real time prediction of imminent cascading failures in a dynamically evolving
power grid is studied. As the cascade look-ahead window increases, the number
of future cascade scenarios grows exponentially. A novel learning based method is
developed to compute the marginal failure probability of each line due to cascades
at times deep into the future. The proposed method enjoys the unique advantage
that a labeled data set can be generated in an arbitrarily large amount at very low
cost. Numerical results demonstrate that the off-line trained predictive model
provides very fast online and accurate prediction of cascading failures.
3 - New Approaches In Data Analysis For Infrastructural Networks:
Combinatorial Hodge Theory
Chase Dowling, University of Washington, Seattle, WA, United
States, Lillian Ratliff, Baosen Zhang
Recent advances in Hodge theory have shed light on a deep relationship between
graph theory and calculus. One important theorem in calculus—the Helmholtz
decomposition—splits a vector field into conservative and solenoidal components.
The combinatorial Hodge decomposition extends this technique to graphs, and
gives conservation law respecting flows on edges. Power, gas, and traffic networks
all respect some form of conservation law, and their optimal utilization has
proven difficult owing to nonlinearities in flow. We explore a novel application of
the Hodge decomposition in traffic and power networks with the aim of
developing control strategies in face of these nonlinearities.
4 - Energy Profile Prediction: Implications For Electric Vehicle
Demand Response
Caroline Camille Le Floch, University of California, Berkeley,
Berkeley, CA, United States,
Scott Moura
This work shows a predictive framework that uses demographic data to predict
energy profiles and acceptance of smart grid tariffs. Our analysis is based on the
Australian Smart Grid Data, including electricity use interval readings, customer
demographics, peak event offers and acceptances. First, we use clustering
methods to define a representative dictionary of hourly load shapes, and assign
individual energy profiles as his/her most frequently used shapes. Second, we
present the performance of several estimators to predict energy profiles and peak
event responses from demographic data. Third, we discuss implications for
designing smart grid programs for Electric Vehicles owners.
TC06