2015 Informs Annual Meeting

WA58

INFORMS Philadelphia – 2015

WA59 59-Room 110B, CC Joint Session ENRE & Integer and Discrete

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, wu.1557@osu.edu, 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. WA58 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, dalia.patino@duke.edu 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, bcheng@princeton.edu, 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, amusselman@gatech.edu, 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, rb171@duke.edu, 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, dalia.patino@duke.edu 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.

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, andy.sun@isye.gatech.edu 1 - New Formulation and Strong MISOCP Relaxations for AC Optimal Transmission Switching Problem Burak Kocuk, Georgia Institute of Technology, 755 Ferst Drive, 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, Kory.Hedman@asu.edu, 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, murat@gatech.edu, 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, jeffbake@southernco.com 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. NW, Atlanta, GA, 30332, United States of America, burak.kocuk@gatech.edu, Santanu Dey, Andy Sun

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