Informs 2015

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INFORMS Philadelphia – 2015
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3 - Public Electric Vehicle Fast Charging Station
Management Strategies
Fei Wu, The Ohio State university, 1971 Neil Ave., Columbus,
OH, 43210, United States of America,
Ramteen Sioshansi
Fast EV charging stations typically use high-power chargers. Without control,
transformers serving the stations will suffer accelerated aging. A charging station
control model (CSCM) is introduced. It is formulated as a two-stage stochastic
programming model to minimize the station’s expected operation cost. A
sequential sampling procedure with sample average approximation is proposed to
solve the CSCM. Simulations show that the operation costs are significantly
reduced by using the CSCM.
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58-Room 110A, CC
Renewables Integration: Market Clearing, Optimal
Sitting and Energy Storage
Sponsor: ENRE – Energy II – Other (e.g., Policy, Natural Gas,
Climate Change)
Sponsored Session
Chair: Dalia Patino-Echeverri, Assistant Professor, Duke University, Box
90328, Duke University, Durham, NC, 27708, United States of America,
1 - Co-optimizing Battery Storage for Energy Arbitrage and
Frequency Regulation
Bolong Cheng, Princeton University, Olden Street Engineering
Quadrangle, Electrical Engineering, Princeton, NJ, 08544, United
States of America, Warren Powell
We want to optimize battery storage for multiple applications; this problem
requires the battery to make charging/discharging decisions at different time
scales while accounting for the stochastic information. Solving the problem for
even a single-day operation would be computationally inefficient due to the large
state space and time steps. We propose a dynamic programming approach that
takes advantage of the nested structure of the problem by solving smaller sub-
problems of different sizes.
2 - Optimizing Wind Site Placement to Minimize Variability Effect
Amelia Musselman, Graduate Research Assistant, Georgia
Institute of Technology, 755 Ferst Drive, NW, Atlanta, GA, 30332,
United States of America, , Valerie
Thomas, Dima Nazzal
As a result of increased environmental awareness, wind power has drawn
considerable attention as a potential renewable energy alternative. However, the
intermittency and uncontrollability of wind warrant concerns about its reliability
as an energy resource. In this research we develop a power generation expansion
planning model that aims to mitigate the effects of wind variability by selecting
sites to complement each other such that the overall wind energy available is
both high and consistent.
3 - Assessing Operation of Wind-coal Hybrid Units with Flexible
Carbon Capture and Storage(CCS) in MISO
Rubenka Bandyopadhyay, PhD Candidate, Duke University,
Box 90328, Duke University, Durham, NC, 27707, United States
of America, , Xin Li, Ali Daraeepour,
Dalia Patino-Echeverri
We simulate the optimal dispatch of coal-wind hybrid units (i.e. existing coal
plants retrofitted with flexible post-combustion amine based CCS and co-located
wind farms) in a Unit Commitment/Economic Dispatch (UC/ED) model of MISO.
We assess market benefits derived from provision of ramp-capability and its
impacts on wind curtailment, system reliability and systems costs.
4 - Generation Expansion Planning under Flexible Performance
Standards with Alternative Compliance Payments
Dalia Patino-Echeverri, Assistant Professor, Duke University,
Box 90328, Duke University, Durham, NC, 27708, United States
of America,
This research explores the effects on costs and emissions from making Alternative
Compliance Payments (ACP) part of the policy mechanisms for reducing CO2
emissions from power plants. Under an ACP States set an emissions rate target, a
fee (the ACP) that emitters pay for each ton of emissions in excess of the target,
and a deadline to permanently reduce emissions (by retrofitting or replacement).
A Stochastic Mixed Integer Linear Program represents the decisions of a regulated
electric utility.
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59-Room 110B, CC
Joint Session ENRE & Integer and Discrete
Optimization: Emerging Operational Approaches in
Electric Power Systems — Transmission Switching,
Data-Driven Maintenance, and Natural
Gas Coordination
Sponsor: ENRE – Energy I – Electricity
Sponsored Session
Chair: Andy Sun, Assistant Professor, Georgia Institute of Technology,
755 Ferst Drive, Atlanta, GA, 30332, United States of America,
1 - New Formulation and Strong MISOCP Relaxations for AC Optimal
Transmission Switching Problem
Burak Kocuk, Georgia Institute of Technology, 755 Ferst Drive,
NW, Atlanta, GA, 30332, United States of America,
Santanu Dey, Andy Sun
In this work, we formulate the AC Optimal Transmission Switching (AC OTS)
problem as a MINLP. We propose a mixed integer SOCP (MISOCP) relaxation and
strengthen this relaxation via several types of valid inequalities, some of which
have demonstrated excellent performance for AC OPF and some others are
specifically developed for the AC OTS. Finally, we propose practical algorithms
that utilize the solutions from the MISOCP relaxation to obtain high quality
feasible solutions for AC OTS problem.
2 - Flexible Transmission Decision Support: Scalable Heuristics for
Power Flow Control Devices
Kory Hedman, Professor, Arizona State University, P.O. Box
875706, GWC 206 School of ECEE, Tempe, AZ, 85287-5706,
United States of America, Mostafa
Ardakani, Xingpeng Li, Mojdeh Abdi-khorsand, Pranavamoorthy
Balasubramanian
While power flow control devices can greatly enhance the efficiency and
reliability of the high voltage power grid, the modeling of such devices in network
flow (optimal power flow) models is limited due to the added computational
burden. We will present two types of power flow control: transmission switching
and variable impedance based series devices. We will present simple heuristics
that reduce the computational burden, are scalable, and still produce high quality
solutions.
3 - Sensor Driven Condition Based Generation Maintenance
Murat Yildirim, PhD Student, Georgia Institute of Technology,
755 Ferst Drive, Atlanta, GA, 30332, United States of America,
Nagi Gebraeel, Andy Sun
We provide an adaptive optimization model to determine the optimal generation
maintenance scheduling by leveraging sensor health monitoring data and
considering network constrained unit commitment decisions. We propose new
mixed-integer optimization models and efficient algorithms that exploit the
special structure of the proposed formulation. We present extensive
computational experiment results to show proposed models achieve significant
improvements in cost and reliability.
4 - Incorporating Natural Gas Pipeline Constraints in Intraday Unit
Commitment and Dispatch
Jeff Baker, Southern Company, 600 18th Street North,
Birmingham, AL, United States of America,
Southern Company secures a reliable natural gas supply for its generation fleet by
procuring firm transportation on several pipelines under long-term contracts.
When there is a significant discrepancy between the day ahead and real time
demand, utilization of gas along a pipeline must be optimized. This talk will
discuss the impact of adding gas burn constraints into intraday unit commitment
and dispatch algorithms as well as the impacts to scheduling on system operators.
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