2016 INFORMS Annual Meeting Program

MD05

INFORMS Nashville – 2016

MD05 101E-MCC Power System Generation and Transmission Expansion Sponsored: Energy, Natural Res & the Environment, Energy I Electricity Sponsored Session

2 - Implementationof The Genetic Gain Performance Metric Accelerates Agricultural Productivity Joseph Byrum, Syngenta, West Des Moines, IA, 50265, United States, joseph.byrum@syngenta.com, Craig Davis, Greg Doonan, Tracy Doubler, Bill Beavis, Von Kaster, Sam Parry, Ronald Mowers Yield is the most important metric for a farmer, as crop output directly impacts profitability. We now refer to the rate of increase in the genetic potential for yield in cultivars as “genetic gain,” and it is measured in bushels per acre (bu/ac). The challenge was to develop models and methods that provide unbiased estimates of the genetic components of yield for unbalanced field trials conducted across years. An algorithm was implemented that minimizes the confounding influence of unpredictable environmental contributions to estimated yields of varieties enabling real-time unbiased estimation of performance metrics that are used in operational decision making and optimization. MD04 101D-MCC Optimization for Enhancing Critical Infrastructure Resilience Sponsored: Energy, Natural Res & the Environment, Energy I Electricity Sponsored Session Chair: Feng Qiu, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL, 60439, United States, fqiu@anl.gov Co-Chair: Matteo Spada, Paul Scherrer Institut, OHSA/D19, 5232 Villigen PSI, Villigen PSI, Switzerland, matteo.spada@psi.ch 1 - A Framework For Measuring Infrastructure Resilience Of Energy Systems Taking Natural Hazards And Technical Failures As Disruption Triggers Peter Lustenberger, Paul Scherrer Institut/ETH Zurich, Singapore, Singapore, lustenberger@frs.ethz.ch, Tianyin Sun, Patrick Gasser, Wansub Kim, Peter Burgherr, Matteo Spada, Stefan Hirschberg This work first provides a quantifiable and feasible technical resilience definition and measure specifically for energy systems. Following this definition, we propose a framework for measuring the technical resilience of both the components (power plants, substations, refineries, compressor stations etc.) and the network topology of power grid and oil/gas supply systems by considering probabilistic disruption triggers of both natural hazards and technical failures as well as recovery dynamics. 2 - Prioritization Of Infrastructure Resilience Investments Julia Phillips, Argonne National Laboratory, phillipsj@anl.gov Recent events emphasize the importance of the protection and resilience of systems of critical infrastructure. This talk explores initial research on prioritization of infrastructure investments for resilience through optimization considering owner risk profiles. It is hypothesized that owners of different types of critical infrastructure systems may let their risk tendencies influence how to allocate funds as opposed to what investments are “optimal” considering monetary value only. The research community has struggled with the measurement of resilience. We use a technique of perceived value to assist in measurement of resilience and prioritization of investment funds. 3 - Wind-participated Power System Restoration Feng Qiu, Argonne National Laboratory, fqiu@anl.gov Black-start resources, electric generators that can start on their own without power supply from the grid, are critical initial power sources for power system restoration. Wind generation, with black-start capability, however, has not been considered as black-start resources due to its unreliability (variable and uncertain). As the wind integration continues to grow, wind-participated system restoration becomes not only a viable but also a valuable solution. In this talk, we will present an optimization model to incorporate wind in the system restoration. Wind uncertainty and variability will be addressed to ensure the success of system restoration. 4 - Repair, Rebuild, Or Replace? Protecting Aging Infrastructure From Hazards And Threats David L. Alderson, Naval Postgraduate School, dlalders@nps.edu, Jan Brendecke, Kyle Y Lin We consider an infrastructure system whose function depends on a number of components that fail randomly according to known rates. Components that are “new” have a small failure rate and components that are “old” have a larger failure rate. When a component fails it can be replaced to “new” status or repaired to “old” status. An “old” component can also be proactively replaced to “new” status. We formulate and solve a Markov decision process to identify the optimal replace/repair policies for given system operating costs and discuss implications for real infrastructure systems.

Chair: Enzo E Sauma, Pontificia Universidad Catolica de Chile, Vicuña Mackenna 4860, Macul., Santiago, 7820436, Chile, esauma@ing.puc.cl 1 - Risk-averse Transmission And Generation Planning: Wecc Case Study Francisco Munoz, Universidad Adolfo Ibáñez, elpanchomunoz@gmail.com, Harry van der Weijde, Benjamin Field Hobbs, Jean-Paul Watson We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive market. To do so, we formulate a stochastic model which minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR), and which can be shown to have an equivalent solution to a perfectly competitive risk-averse Stackelberg equilibrium in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. 2 - How Technical Operational Details Affect Generation Expansion In Oligopolist Markets Efraim Centeno, Universidad Pontificia Comillas - IIT, Efraim.Centeno@iit.comillas.edu, Sonja Wogrin, Adelaida Nogales We propose a generation expansion model including an oligopolistic market representation based on an equilibrium approach. We incorporate the system states methodology into this generation expansion model allowing us to recover some chronological information in a LDC framework, thereby more accurately accounting for start-up and shut-down costs without making use of an hourly representation of demand. We find that when operational details are considered, flexible technologies are preferred by the companies in the optimal mix. We also observe that under perfect competition in comparison with oligopolistic markets, more base-load plants are built as well as more peaking plants. 3 - Sustainable Transmission Planning In Imperfectly Competitive Electricity Industries: Balancing Economic Efficiency And Environmental Outcomes Afzal S. Siddiqui, Stockholm University, Stockholm, Sweden, Afzal S. Siddiqui, HEC Montréal, Montréal, QC, H3T 2A7, Canada, afzal.siddiqui@ucl.ac.uk, Makoto Tanaka, Yihsu Chen We address the problem of a TSO that builds a transmission line in order to maximise social welfare inclusive of the cost of emissions. A TSO in a deregulated industry can only indirectly influence outcomes via its choice of the transmission line capacity. Via a bi-level model, we show that this results in less transmission capacity with limited emissions control if industry is perfectly competitive. A carbon tax on industry leads to perfect alignment of incentives and maximised social welfare only under perfect competition. By contrast, a carbon tax actually lowers social welfare under a Cournot oligopoly as the resulting reduction in consumption facilitates the further exercise of market power. 4 - Power Capacity Expansion Planning And The Influence Of Network Payment Schemes We propose a multi-annual transmission expansion planning model seeking to reduce the total system costs and considering different network payment schemes. The proposed models are reformulated as Mixed Integer Linear Programming (MILP) problems. A realistic case study based on the main power system in Chile is analyzed to illustrate the proposed models. It is shown that integrating line cost-recovering equations into the Transmission Expansion Planning model may result into a more realistic and less congested power network. Also, total system cost is highly related with transmission tariff discrimination. Enzo Sauma, Pontificia Universidad Católica de Chile, esauma@ing.puc.cl, Diego Bravo, Javier Contreras, Sebastián de la Torre, José Aguado, David Pozo

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