2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
457
WD04
101D-MCC
Robust and Stochastic Optimization for
Energy Systems
Sponsored: Energy, Natural Res & the Environment,
Energy I Electricity
Sponsored Session
Chair: Andy Sun, Georgia Institute of Technology, 755 Ferst Dr,
Atlanta, GA, 30312, United States,
1 - Robust Optimization For The Alternating Current Optimal Power
Flow Problem
Alvaro Lorca, Georgia Institute of Technology,
, Andy Sun
We present an adaptive robust optimization model for the alternating current
optimal power flow problem (ACOPF) under uncertainty in renewable power
availability and the active and reactive power injections at demand nodes. We will
discuss solution methods and the performance of the approach proposed through
computational experiments.
2 - Comparison Of Stochastic Programming And Robust
Optimization For Risk Management In Energy Generation
Ricardo M Lima, KAUST, Thuwal, Saudi Arabia,
, Sabique Langodan, Ibrahim Hoteit,
Omar Knio, Antonio J. Conejo
In this talk, we address the optimal self-scheduling and market involvement of a
virtual power plant (VPP) by using three different methods. The VPP faces a
decision-making problem with uncertainty in the wind power and electricity
prices forecast. We define this problem using a risk-averse stochastic
programming model, a robust optimization model, and with a new hybrid robust-
stochastic formulation. We analyze these methods from the point of view of
formulations, uncertainty quantification and risk, decomposition algorithms, and
computational performance. Furthermore, we compare the impact of the risk
measures and their parameterizations on the results obtained with the three
methods.
3 - Risk-constrained Optimal Power Flow With Moment And
Unimodality Information
Bowen Li, University of Michigan, Ann Arbor, MI, United States,
, Ruiwei Jiang, Johanna Mathieu
We propose a risk-constrained optimal power flow (OPF) problem with uncertain
renewables. Using historical data and domain knowledge, we incorporate
moments of the renewable forecast errors and assume unimodality to derive
reformulations and approximations based on semidefinite programs and second-
order cone programs, and evaluate them on IEEE test systems.
4 - An Efficient Robust Solution To The Two-stage Stochastic Unit
Commitment Problem
Ignacio Blanco, PhD Student, Technical University of Denmark,
Kgs. Lyngby, 2800, Denmark,
Juan M Morales Gonzalez
This paper proposes a reformulation of the scenario-based two-stage unit
commitment problem under uncertainty that allows finding unit-commitment
plans that perform reasonably well both in expectation and for the worst case
realization of the uncertainties. The proposed reformulation is based on
partitioning the sample space of the uncertain factors by clustering the scenarios
that approximate their probability distributions. It is, furthermore, very amenable
to decomposition and parallelization using a column-and-constraint generation
procedure.
WD05
101E-MCC
Wildland Fire Management I - Suppression
Sponsored: Energy, Natural Res & the Environment II Forestry
Sponsored Session
Chair: Eghbal Rashidi, Mississippi State University, Industrial & Systems
Engineering, Mississippi State University, MS, 39762, United States,
1 - Vulnerability Analysis Of The Initial Attack In Suppressing The
Worst Case Wildfires
Eghbal Rashidi, Mississippi State University, ,
Hugh Medal
In this research, we perform a quantitative gap analysis between available
capacity for responding to wildfires and the estimated capacity needs for
responding to a worst case scenario wildfire. We model the problem as a
Stackelberg game using a bilevel max-min MIP model. We use the model to
evaluate the impact of the worst-case wildfire, i.e., the arrangement of ignition
points that causes the maximal damage. We then investigate the relationship
between fire response capacity and the rate of spread, fire ignition location and
number of fire ignitions in the landscape.
2 - An Optimization Model For Wildfire Suppression
Andres L Medaglia, Professor and Chair, Universidad de los Andes,
Cr 1 este #19 A 40, ML708, Bogota, Cundinamarca, 111711,
Colombia,
An effective early attack is essential to control wildfires. In this work, we propose
an MIP to support decisions related to the planning and response phases of fire
management. For the planning phase, the MIP addresses the decisions related to
the location of facilities and how much inventory to store. In the response phase,
decisions are concerned to the location of coordination centers and how to
allocate available resources. The model includes a risk measure to limit the
downside risk of different scenarios. We apply this methodology in a setting that
resembles wildfires nearby the city of Bogotá (Colombia).
WD06
102A-MCC
Optimization in Data Mining 1
Sponsored: Data Mining
Sponsored Session
Chair: Orestis Panagopoulos, 9303 Nelson Park Circle, Apt 204,
Orlando, FL, 32817, United States,
1 - New Developments Of L1 Splines: Fast Computation And
Shape-preserving Capability
Ziteng Wang, Assistant Professor, Northern Illinois University,
DeKalb, IL, United States,
Cubic splines are widely used for data interpolation and approximation in terrain
surface fitting, computer aided design, and numerical control. Conventional L2-
norm based splines often show undesired oscillation and do not preserve shape,
especially for irregular or multiscale data. L1 splines, by minimization of the L1-
norm based metrics, have shown superior and robust shape-preserving
performances and enjoyed increasing application potentials. We introduce the
development of L1 splines over the past decade, present the latest research on the
fast computing strategy and the quantitative measure of shape-preserving
capability, and discuss future opportunities.
2 - Probing The Pareto Frontier Of Computational
Statistical Tradeoffs
Zhaoran Wang, Princeton University, Princeton, NJ, 08540,
United States,
In this talk, we discuss the fundamental tradeoffs between computational
efficiency and statistical accuracy in big data. Based on an oracle computational
model, we introduce a systematic hypothesis-free approach for developing
minimax lower bounds under computational budget constraints. This approach
mirrors the classical Le Cam’s method, and draws explicit connections between
algorithmic complexity and geometric structures of parameter spaces. Based on
this approach, we sharply characterize the computational-statistical phase
transitions that arise in a broad class of learning problems.
WD07
102B-MCC
Predictive Analysis and Applications in Data Mining
Sponsored: Data Mining
Sponsored Session
Chair: Juxihong Julaiti, Penn State University, 445 Waupelani Drive,
Apt J1, State College, PA, 16801-4445, United States,
1 - Proactive Data: A Rich Source Of Occupational
Accident Prediction
Jhareswar Maiti, Professor, IIT, Kharagpur, Kharagpur, 721302,
India, , Sobhan Sarkar,
Saicharan Pardhu, Rutwick Ayi
The proactive data ( e.g., inspection reports) is a source of rich information for
prediction of occurrence of accidents. The main aim of the study is to use the
proactive data properly along with reactive data (e.g., incident reports) retrieved
from an integrated steel industry in building a prediction model to predict the
occurrences of future incidents. Decision tree algorithms like C5.0, CART, CHAID,
exhaustive CHAID, and ensemble techniques i.e., boosting have been
implemented in order to predict the accidents. Results show that the C5.0
outperforms all other algorithms in terms of higher accuracy.
WD07