Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

SB43

4 - Comparing Frequentist and Bayesian Fixed-Confidence Guarantees for Selection-of-the-Best Problems David J. Eckman, Cornell University, 118 Compton Rd, Ithaca, NY, 14850, United States, Shane Henderson The problem of selecting the best from among a finite number of simulated alternatives has been studied under the contrasting frequentist and Bayesian interpretations of probability. We emphasize the conceptual differences in fixed- confidence guarantees under the two frameworks and examine practical implications of this distinction. We also discuss how frequentist selection procedures are inherently conservative and Bayesian selection procedures are relatively easier to design. Through simulation experiments, we compare the performance of selection procedures designed under each framework with respect to the other type of guarantee. Joint Session Energy/Climate & ENRE/Env: Renewable Energy and Storage Modeling and Policy for the US Emerging Topic: Energy and Climate Emerging Topic Session Chair: Zana Cranmer, Bentley University, Waltham, MA, 02452, United States 1 - Dispatch of Wind, Solar, and Energy Storage in Long-term Planning Models Cara Marcy, Renewable Electricity Analyst, U.S. Energy Information Administration, 1000 Independence Avenue SW, Washington, DC, 20585, United States EIA’s National Energy Modelling System (NEMS) provides a detailed multi- decadal assessment of the future of U.S. energy sectors. With the recent growth of wind and solar technologies, EIA has been working on capturing the value of these resources given their variable nature. In addition, energy storage is one technology that can take advantage of value streams presented from curtailment of excess renewable energy. This presentation will review the updated mini- dispatch model in NEMS, as well as highlight scenario results that investigate the relationship between renewable energy and storage. 2 - The Potential for Emissions Reductions with Residential Demand Response Maddie Macmillan, North Carolina State University, Raleigh, NC, United States, Jeremiah Johnson The primary goal of demand response (DR) is to reduce peak electricity demand. In this study, we examine an alternative goal of using DR to reduce air emissions. For the US, we estimate the diurnal and seasonal demand profiles for suitable residential end uses including air conditioning, electric heating, and water heating. We assume that the DR events are load-neutral and test a range of tolerances for demand deferral. We develop an emissions minimization model that utilizes hourly marginal emissions factors for 20 grid regions to show significant potential to reduce CO2 emissions through DR approaches. The magnitude of the benefits are limited by the length of the demand deferral and DR adoption rate. 3 - Estimating the Value of Offshore Wind Along the United States’ Eastern Coast Andrew Mills, Research Scientist, Lawrence Berkeley National Lab., 1 Cyclotron Rd., Berkeley, CA, 94720, United States Offshore wind has been concentrated in Europe, and remains limited in other areas of the world. Among the many challenges to deployment is the need to understand the value that offshore wind provides within electricity markets. To explore the drivers of offshore wind value we use historical (2007-2016) weather data at thousands of potential offshore wind sites, combined with historical wholesale electricity market outcomes at hundreds of possible interconnection points. We find that the average historical market value of offshore wind from 2007-2016 varies significantly by project location, from $40/MWh to more than $110/MWh, and is highest for sites off of NY, CT, RI, and MA. 4 - The Climate Value of Offshore Wind Energy in the US Zana Cranmer, Bentley University, 175 Forest Street, Waltham, MA, 02452, United States, Erin Baker We develop a forward looking method for estimating the value of permitting offshore wind projects in terms of addressing climate change. Offshore wind provides an additional way to reduce emissions as well as potential reductions in the cost of abatement. Our method includes value from both sources using the GCAM-USA model to look at different regions of the world and the US. These values can be compare to the potential costs imposed on local ecosystems. n SB43 North Bldg 227B

n SB44 North Bldg 227C Joint Session ENRE/Practice Curated: Optimization Methods for Power Systems Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Cedric Josz Co-Chair: Somayeh Sojoudi, University of California, Berkeley, Berkeley, CA, 94703, United States 1 - Learning Solutions to Optimal Power Flow: An Active Set Approach Line Roald, University of Wisconsin-Madison, Madison, WI, United States, Sidhant Misra, Yee Sian Ng Power systems optimization involves solving similar optimization problems over and over and over again, with slightly varying input parameters. We consider the problem of directly learning the optimal solution as a function of the input parameters. Our learning framework is based on identifying the relevant set of active constraints, which we discover using our proposed streaming algorithm with performance guarantees. Applying the algorithm to the optimal power flow problem with renewable energy, we establish that the number of active sets is typically small for OPF problems, and discuss theoretical and practical implications for power systems operation. 2 - Improving Bound Tightening with Quadratic Reformulation Method Applied on Optimal Power Flow Hadrien Godard, Rte, Paris, France Optimality-based and reduced-costs bound tightening are classic methods using convex relaxations. For the OPF problem, the Quadratic Reformulation method gives an efficient relaxation, leading to sharp lower bounds, and interior-points methods compute good feasible solutions. We strengthen bound tightening using those sharp bounds, and present computational results on OPF instances up to a thousand nodes. 3 - Tight Piecewise Convex Relaxations for Global Optimization of Optimal Power Flow Harsha Nagarajan, Los Alamos National Laboratory, NM, United States, Mowen Lu, Russell Bent, Sandra D. Eksioglu, Kaarthik Sundar In recent years, there has been an increasing interest in developing convex relaxations for ACOPF, which are often tight in practice. We further improve the quality of these relaxations by employing convex-hull characterizations for multilinear functions and develop tight piecewise convex relaxations. We also provide useful polyhedral results of these relaxations. Using these tight relaxations, we develop an adaptive, multivariate partitioning algorithm with bound tightening that progressively improves these relaxations, thus converging to the global optimal solution. Computational results show that our novel algorithm reduces the best-known optimality gaps of the Nesta ACOPF cases. 4 - Conic Optimization for Robust State Estimation: Deterministic Bounds and Statistical Analysis Igor Molybog, University of California, Berkeley, Berkeley, CA, United States, Ramtin Madani, Javad Lavaei This project is concerned with the robust electric power system state estimation problem, where the goal is to find the unknown state of a system modeled by nonconvex quadratic equations based on unreliable data. We propose two techniques based on conic optimization to address this problem. We analyze the techniques in both deterministic and stochastic (Gaussian) settings by deriving bounds on the number of bad measurements the algorithms can tolerate without producing a nonzero estimation error. The efficacy of the developed methods is demonstrated on synthetic data and the European power grid.

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