Informs Annual Meeting 2017

MA80

INFORMS Houston – 2017

MA80

MA81

381C Integrated Energy Markets From Operation to Planning Sponsored: Energy, Natural Res & the Environment, Energy Sponsored Session Chair: Jalal Kazempour, echnical University of Denmark, Kgs Lyngby, 2800, Denmark, seykaz@elektro.dtu.dk Co-Chair: Christos Ordoudis, Technical University of Denmark, Lyngby, Denmark, chror@elektro.dtu.dk 1 - An Elasticity Model for Joint Gas-grid Expansion Planning Optimization Pascal Van Hentenryck, University of Michigan, 1813 IOE Building, 1205 Beal Avenue, Ann Arbor, MI, 48108-2117, United States, pvanhent@umich.edu, Russell Bent, Seth Blumsack Recent trends in gas-fired power plant installation has increased the connections between the electric power and natural gas industries. As a result, undesired situations may arise, such as those experienced by both systems during the winter of 2013/2014 in the northeastern US. This talk considers these challenges and present a Combined Electricity and Gas Expansion (CEGE) planning model. The CEGE model minimizes the cost of meeting gas and electricity demand during high-stress conditions and introduces an elasticity model for analysis of gas-price volatility caused by congestion. We conduct an in-depth analysis on a case-study that include the New England area. 2 - Merchant Storage Investment in a Deregulated Electricity Industry Afzal Siddiqui, Stockholm University, Stockholm, Sweden, afzal.siddiqui@ucl.ac.uk, Ramteen Sioshansi, Antonio J. Conejo We develop a bi-level programming model of an imperfectly competitive electricity industry with electricity generation and storage operations at the lower level and storage investment at the upper level. Our analytical results demonstrate that a relatively high (low) amount of market power by generators leads to low (high) storage capacity adoption by a profit-maximising merchant relative to a welfare-maximising ISO. Using a charge on generation ramping between off-peak and peak periods, we illustrate how to incentivise socially optimal storage investment even with a merchant. 3 - Stochastic Programming for Fuel Supply Planning of Combined Heat and Power Plants Daniela Guericke, Technical University of Denmark, Kgs. Lyngby, Denmark, dngk@dtu.dk, Ignacio Blanco, Henrik Madsen, Juan M. Morales The consumption of biomass to produce power and heat has increased due to the carbon neutral policies. Combined heat and power (CHP) plants often combine biomass with other fuels, e.g., natural gas. The negotiation process for supply contracts involves many uncertainties due to the long planning horizon. The demand for biomass is uncertain, and heat demand and electricity prices vary during the planning period. We propose a method using stochastic optimization to support the biomass and natural gas supply planning for CHP plants including short-term decisions for optimal market participation. 4 - An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers Christos Ordoudis, Technical University of Denmark, Technical University of Denmark, Lyngby, Denmark, chror@elektro.dtu.dk, Pierre Pinson, Juan Miguel Morales Gas-fired power plants can serve as a flexible component to ensure security of supply in energy systems with high shares of fluctuating renewables. In view of a tighter coupling between electricity and natural gas systems, we propose an integrated model that jointly optimizes their operation under uncertain power supply by applying two-stage stochastic programming. We use a formulation that properly models the dynamics of the natural gas system, which is essential in short-term operations to take full advantage of the flexibility of the integrated energy system. Our analysis shows the efficiency of the proposed model in accommodating high shares of renewables in a realistic case study. Afzal Siddiqui, University College London, Department of Statistical Science, Gower Street, London, WC1E 6BT, United Kingdom, afzal.siddiqui@ucl.ac.uk

382A Chance-Constrained, Stochastic and Robust MIP and Applications Sponsored: Optimization, Optimization Under Uncertainty Sponsored Session Chair: Kibaek Kim, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL, 60439, United States, kimk@anl.gov 1 - Cardinality Constrained Robust MIP and Submodular Polyhedron Seulgi Joung, Korea Advanced Institute of Science & Technology, Daejeon, Korea, Republic of, sgjoung22@gmail.com, Sungsoo Park We consider valid inequalities for the cardinality constrained robust knapsack problem, which can be used to solve the cardinality constrained robust mixed 0-1 programming problems. We define valid inequalities using submodularity of the cardinality constrained robust knapsack set function. We prove that these inequalities define the convex hull of robust continuous knapsack problem with a single continuous variable. In addition, the most violated inequality can be separated in polynomial time using a greedy algorithm. The computational experiments on the robust binary knapsack problem and the robust knapsack problem with continuous variables exhibit the effect of proposed inequalities. 2 - Parallel Dual Decomposition with Branch-and-Bound Method Kibaek Kim, Assistant Computational Mathematician, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Lemont, IL, 60439, United States, kimk@anl.gov We present a parallel dual decomposition that decouples a large-scale MIP problem into tractable smaller subproblems by the Lagrangian relaxation of coupling constraints in the problem. On top of it, we also develop a branch-and- bound method that ensures integer feasibility with respect to the primal integer variables. Unlike the one in Caroe and Schultz, our method branches on the primal integer variables characterized in the Dantzig-Wolfe decomposition space. Computational results are presented for various problem instances. 3 - A Stochastic Optimization Approach to Maintenance Turnaround Planning in Integrated Chemical Sites Sreekanth Rajagopalan, Carnegie Mellon University, 5000 Forbes Avenue, Doherty Hall 4200, Pittsburgh, PA, 15213, United States, sreekanth@cmu.edu, Nikolaos Sahinidis, Satyajith Amaran, Scott Bury An integrated chemical site is a network of processing plants that consume and produce a variety of chemical products. A maintenance turnaround is a capital and resource intensive maintenance project that requires a long planned outage. We investigate enterprise-wide approaches to plan turnarounds in an integrated chemical site on a medium-term horizon; specifically, we propose a stochastic optimization model that incorporates production reliability for simultaneous production and turnaround planning. We compare different policies for heuristics within a decomposition scheme. 4 - A Hybrid Constraint Removal Scheme in Scenario Approach for Chance-constrained Programs Roya Karimi, University of Arizona, 1725 N.park Ave, #1E, Tucson, AZ, 85719, United States, royakarimi1993@email.arizona.edu In this talk, we consider the constraint removal method in the scenario approach for solving chance constrained programming problems.We introduce two constraint removal schemes: one model-free and the other model-based, as well as the ideas behind them. Furthermore, to show the strength of our proposed methods, a comparison with other existing approaches is provided in an extensive numerical study on a semidefinite optimization problem in control theory.In addition, the proposed methods can be applied to solve a wide range of practical problems.

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