Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MB44

solutions. This presentation will consider a number of heuristic schemes for producing better solutions quickly. We’ll present computational results for the most effective approaches. 2 - Develop Smart Heuristics to Generate Hints for Solving Security Constrained Unit Commitment Yonghong Chen, Midwest ISO, 720 City Center Drive, Carmel, IN, 46032, United States This presentation discuses using problem specific smart heuristics to generate hints to speed up the process to solve security constrained unit commitment. These heuristics can generate hints to reduce large number of variables and constraints. Using parallel computing to improve the hints will also be discussed. 3 - HIPPO - A HPC Solver for Security Constrained Unit Commitment Problem Feng Pan, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99352, United States, Jesse Holzer, Yonghong Chen, Edward Rothberg, Jie Wan, Ralf Elbert HIPPO is a software library for solving the security constrained unit commitment (SCUC) problem. The goal of HIPPO is to reduce the solution time for solving SCUC in ISO/RTO day-ahead energy markets. The team is developing a concurrent optimization solver consisting of several algorithmic approaches such as branch-and-bound, decomposition methods and market based heuristics. These algorithms are implemented to Leverage high-performance computing clusters to further improve the algorithm performance. This talk will provide an overview of the HIPPO project, funded by ARPA-E, and the HIPPO software which includes SCUC models, algorithms and their performance. 4 - Performance Comparison of Security Constrained Unit Commitment Formulations Yanan Yu, University of Florida, 321 University Village, Apt 3, Gainesville, FL, 32603, United States, Yongpei Guan Following a significant expansion of power systems, the large-scale security constrained unit commitment (SCUC) has been an important and challenging problem in recent years. Several formulations have been developed in existing literature, characterizing SCUC problems in various aspects and resulting in diverse computational efficiency. In this presentation, we aim to find the key factors to improve the tightness of SCUC formulation by performance comparison. The relationship of decision variables in alternative formulations are derived and their tightness is analyzed. A suggested formulation is given at last. n MB46 North Bldg 228B Energy Markets Sponsored: Energy, Natural Res & the Environment/Energy Sponsored Session Chair: Felipe A. Feijoo, Pontificia Universidad Catolica de Valparaiso, Chile, 1 - Compressor Optimization in Gas Networks The talk presents an optimization approach to the optimal control of compressors in gas networks. The optimization model captures the gas dynamics in these networks and is shown to be applicable to medium-sized networks, while producing results that have been verified by state-of-the-art simulations. 2 - The Effects of Possible Tax Credit Policy Withdrawal on Timing and Size of Green Investment Roel Nagy, Norwegian University of Science & Technology, Trondheim, Norway This paper analyses the effect of policy uncertainty on investment timing and investment size of a renewable energy project, and the interaction between both. We find that increasing the probability of withdrawal increases the incentive to invest now and decreases the optimal investment size. A firm that invests at the timing threshold value invests at larger scale when the policy is not in effect than when it is in effect. An investor maximising social welfare has the same timing as the profit-maximizing monopolist, but invests at twice the investment size. 3 - Optimizing the Operations and Design of Concentrating Solar Power for Day Ahead Markets Michael J. Wagner, National Renewable Energy Laboratory, 15013 Denver West Pkwy, MS-RSF033, Golden, CO, 80401, United States Concentrating solar power (CSP) systems include thermal storage, which can be dispatched on demand to a power cycle to produce electricity during desirable times of the day. Developers of CSP plan new facilities that will operate in a range of international markets, each having unique characteristics that often variably incentivize power production on a diurnal or seasonal basis. A CSP plant can successfully exploit market features, given an holistic dispatch scheduling program and favorable design characteristics. We present final results from a multi-year project that has developed techniques and software for concurrently optimizing CSP plant design, dispatch, and operations. Pascal Van Hentenryck, University of Michigan, 1813 IOE Building, 1205 Beal Avenue, Ann Arbor, MI, 48108-2117, United States

n MB44 North Bldg 227C Joint Session ENRE/Practice Curated: Applications of Conic Optimization for Energy Systems Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Ramtin Madani, The University of Texas at Arlington, Arlington, TX, 76015, United States 1 - Multiplier-based Observer Design for Large-scale Lipschitz Systems Ming Jin, UC Berkeley, Cory 406, Berkeley, CA, 94720, United States Observer design for nonlinear systems with incomplete state observations is of practical significance. To this end, this study presents a multiplier-based approach that is capable of determining an asymptotically stable observer for a large class of highly nonlinear and large-scale systems. Both the present and the state-of-the art methods are evaluated in a benchmark example and a case study on the dynamic power system state estimation, where the proposed approach exhibits an imperative trade-off between non-conservatism and computational tractability, establishing its viability for real-world large-scale nonlinear systems. 2 - Sequential Convex Relaxation for Optimal Power Flow and Unit Commitment Problems Ramtin Madani, The University of Texas at Arlington, Arlington, TX, 76015, United States, Fariba Zohrizadeh, Mohsen Kheirandishfard, Adnan Nasir, Edward Quarm This talk is concerned with the optimal power flow (OPF) and unit commitment (UC) problems. We propose a novel convex relaxation, which transforms non- convex AC power flow equations into convex quadratic inequalities. Additionally, we propose a penalization technique which guarantees the recovery of feasible solutions for the original non-convex problem, under certain assumptions. The proposed penalized convex relaxation scheme can be used sequentially in order to find feasible and near-globally optimal solutions. By solving a few rounds of penalized convex relaxation, fully feasible solutions are obtained for challenging benchmark test cases with as many as 13659 buses. 3 - Convex Relaxation of Bilinear Matrix Inequalities with Applications to Optimal Control Synthesis Mohsen Kheirandishfard, The University of Texas at Arlington, Arlington, TX, United States, Fariba Zohrizadeh, Muhammad Adil, Ramtin Madani This talk is concerned with the problem of minimizing a linear objective function subject to a bilinear matrix inequality (BMI) constraint. We introduce a family of convex relaxations which transform BMI optimization problems into polynomial- time solvable surrogates. The efficacy of the proposed convex relaxation methods are demonstrated on benchmark instance of optimal control synthesis problems. 4 - A Mixed-integer Programming Solution to AC Optimal Transmission Switching for Load Shed Prevention William Eric Brown, Texas A&M University, College Station, TX, United States, Erick Moreno-Centeno Optimal transmission switching (OTS) has garnered recent consideration for its value in leveraging the flexibility of the power grid. However, solving the AC-OTS problem remains quite difficult in practice. We propose a mixed-integer linear programming (MILP) model for the AC-OTS to maximize post-contingency load shed prevention (LSP). Unlike the widely-used DC-OTS, which is based on the standard DC linearizations (i.e., the DCOPF), our model produces LSP values which are highly correlated with the ACOPF. In thorough computational tests, our approach identifies the AC optimal switch in over 92% of studied instances. n MB45 North Bldg 228A Solving Deterministic Security Constrained Unit Commitment Problem Sponsored: Energy, Natural Res & the Environment/Electricity Sponsored Session Chair: Feng Pan, Pacific Northwest National Laboratory, Richland, WA, 99352, United States Co-Chair: Yonghong Chen, Midcontinent ISO, 720 City Center Drive, Carmel, IN, 46032, United States 1 - Heuristics for Unit Commitment Models Zonghao Gu, Gurobi Optimization, Houston, TX, United States, Edward Rothberg When solving unit commitment MIP models, the majority of the optimality gap in the early part of the search process is due to the quality of the initial feasible

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