2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
238
2 - Sampling Based Optimization Algorithms For Power
Systems Application
Harsha Gangammanavar, Clemson University,
We present sampling-based approaches for addressing a class of stochastic
optimization problems arising in power systems with significant renewable
penetration, including economic dispatch and distributed storage control. These
approaches provide a distribution-free alternative to methods based on Benders
decomposition. This allows them to directly operate with external state-of-the-art
simulators accessible to power systems operators. We will demonstrate their
advantages in two-stage and multistage setups through computational
experiments on real-scale power systems.
3 - Robust Strategic Bidding In Day Ahead Electricity Markets
Bruno Fanzeres, Georgia Institute of Technology, 765 Ferst Drive
NW, Atlanta, GA, United States, ,
Shabbir Ahmed, Alexandre Street
The standard approach to devise bidding strategies in day-ahead electricity
markets assumes available a joint probability distribution that drives the
probabilistic nature of rival players’ behavior. Nevertheless, construct such
probabilistic description is a challenging task due to its complex nature. In this
talk, robust optimization techniques are adapted to the bidding strategy problem
to characterize the uncertainty on rival players’ bids. A Column-and-Constraint
Generation algorithm is constructed to solve the bidding problem. An illustrative
example is presented to highlight the applicability of the proposed model as well
as to provide intuition behind the algorithm.
4 - Modeling Power Markets With Multi-stage Stochastic
Nash Equilibrium
Joaquim Dias Garcia, PSR-Inc., Praia de Botafogo, 228, Botafogo,
Rio de Janiero, Brazil,
The modeling of modern power markets requires the representation of the
following main features: (i) a stochastic dynamic decision process, with
uncertainties related to renewable production and fuel costs, and (ii) a game-
theoretic framework that represents the strategic behaviour of multiple agents.,
These features can be in theory represented as a stochastic dynamic programming
recursion, where we have a Nash equilibrium for multiple agents. This work
presents an iterative process to solve the above problem for realistic power
systems. The proposed algorithm consists of a fixed point algorithm, in which,
each step is solved via stochastic dual dynamic programming method.
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105B-MCC
Stochastic Programming for Long Term Planning
Sponsored: Optimization, Optimization Under Uncertainty
Sponsored Session
Chair: Anderson Rodrigo de Queiroz, NCSU, n, 1, NC, 12,
United States,
Co-Chair: Joseph F DeCarolis, North Carolina State University,
Raleigh, NC, United States,
1 - The Value Of Stochastic Programming For Energy Systems
Planning
Anderson Rodrigo de Queiroz, North Carolina State University,
Raleigh, NC, United States, ,
Joseph F DeCarolis
Energy system models should reflect the reality that planners must make
decisions prior to the realization of future uncertainties. Multi-stage stochastic
programs, which embed uncertainty in the decision process, optimize over future
possibilities to yield a near-term decision strategy. We use the expected value of
perfect information and the value of the stochastic solution as metrics to quantify
the value of such strategies for long-term capacity expansion of energy systems.
2 - Stochastic Optimization Of Design Under Heuristic Operation In
Mixed Integer Programs
Alexander Zolan, The University of Texas at Austin,
, David Morton, Alexandra M Newman
We present a framework for optimizing system design in the face of a restricted
class of policies governing system operation, which aim to model realistic
operation for stochastic integer programs with a long operating time horizon. This
leads to a natural decomposition of the problem yielding upper and lower
bounds, which we can compute quickly. We illustrate application of these ideas
using a model that seeks to design and operate a microgrid to support a forward-
operating base under load and photovoltaic (PV) uncertainty, as well as other
examples from the literature.
4 - Power System Planning In Fragile States: A Case Study Of
South Sudan
Evangelia Spyrou, Johns Hopkins University, Baltimore, MD,
United States, , Morgan Bazilian,
Debabrata Chattopadhyay, Benjamin Field Hobbs
In countries suffering from fragility, conflict and violence, power system planning
and investment is essential for development and economic growth. However, the
sector has to contend with deep uncertainty that may impact on an already
vulnerable power system. We propose the application of a multi-stage stochastic
program that explicitly considers probability of conflict and its consequences on
power system infrastructure. Results for the power system in South Sudan are
provided and discussed.
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106A-MCC
Finance, Portfolio I
Contributed Session
Chair: Markku Kallio, Professor, Aalto Univertsity, Runeberginkatu
22-24, Helsinki, FIN-00200, Finland,
1 - Trade Space Exploration Tools And Methods With Applications
To Capital Investments And Portfolio Management Decisions
For Optimality
Simon Miller, Applied Research Laboratory, The Pennsylvania
State University, 411 Waupelani Drive, D-221, State College, PA,
16801, United States, , Christopher M. Farrell,
Michael A. Yukish, Gary M Stump
Faced with constrained portfolio conditions, senior leaders must often make
strategic choices and fiscal trades with implications on capabilities, capacities, and
system attributes to maximize value, manage risk, and satisfy stakeholder
requirements. Researchers have developed robust tools and methods to explore
large scale, complex, multi-objective problems for portfolio analyses, where data
visualization techniques and optimization algorithms are simultaneously applied
to support decision processes in a binary combinatorial space. The tools and
methods may be applied to a wide range of financial management and resource
allocation problems to provide flexibility and options.
2 - Forth Order Stochastic Dominance Efficiency Test And An
Empirical Evaluation
Nasim Dehghan Hardoroudi, PhD Candidate, Aalto University
School of Business, Runeberginkatu 22-24, Chydenia (4th floor),
Helsinki, 00100, Finland,
Stochastic dominance is an important tool in aiding decisions under uncertainty
when the decision maker’s utility function is unknown. In this study, we propose
a novel forth order stochastic dominance (FOSD) efficiency test. We derive the
necessary and sufficient conditions for such test, which is based on nonlinear
convex optimization problem. For comparison, we provide numerical illustrations
for second and third order stochastic dominance (SSD, TSD) as well as decreasing
absolute risk aversion stochastic dominance (DSD) besides the FOSD test, using
stock market data of the US. The market index as benchmark is found inefficient
and dominated under the all types of stochastic dominance.
3 - Some Tests For Stochastic Dominance Efficiency
Markku Kallio, Professor, Aalto Univertsity, Runeberginkatu
22-24, Helsinki, FIN-00200, Finland,
We consider third order stochastic dominance (TSD), decreasing absolute risk
aversion (DARA) stochastic dominance (DSD) as well as stochastic dominance
(ESD) based on the family negative exponential utility functions. These concepts
are of interest because the respective classes of utility functions convey observed
properties of individual preferences. Using the efficiency concept introduced by
Post, we derive necessary and sufficient tests for efficiency under the three types
of stochastic dominance. Our DSD efficiency test is new, it relies on our argument
for the TSD test, and it circumvents shortcomings in recent literature.
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