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IEEE Power and Energy Society General Meeting

作者： Bananeh Ansari Salman Mohagheghi Electrical Engineering and Computer Science Department Colorado School of Mines

ISBN: (纸本)9781479964161

A Microgrid-assisted methodology is proposed in this paper for electric service restoration in distribution networks. Success of restoration algorithms is closely tied with the availability of sufficient capacity on the restoration path(s). When the restoration path is at or near full capacity, some capacity relief may be achieved through the usage of Microgrids. By increasing the generation level of its micro-generators, reducing the demand of its demand responsive loads, or, as the last resort, by islanding from the grid, a Microgrid can help alleviate the congestion on the restoration path, and therefore assist the service restoration algorithm. The methodology put forth in this paper considers a potential restoration circuit equipped with Microgrids, and provides the most cost-effective operational solution while achieving the targeted restoration capacity. Network operational constraints, demand variations, and fluctuations in energy pricing are all taken into account. The problem is formulated as a mixed-integer nonlinear programming problem, and is implemented on a modified version of the IEEE 123-node test distribution system.

关键词： Distribution network Electric service restoration Demand response Distributed energy resources Microgrid mixed-integer nonlinear programming

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Globally Optimal Antenna Selection and Power Allocation for Energy Efficiency Maximization in Downlink Distributed Antenna Systems

Globally Optimal Antenna Selection and Power Allocation for ...

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IEEE Global Communications Conference

作者： Yuzhou Li Min Sheng Xijun Wang Yan Shi Yan Zhang Information Science Institute State Key Laboratory of ISN Xidian University

ISBN: (纸本)9781479935130

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

关键词： antenna arrays energy conservation integer programming iterative methods nonlinear programming tree searching DAS MINLP antenna selection constraints branch and bound methods computation complexity downlink distributed antenna systems energy efficiency maximization future wireless network design globally optimal antenna selection green communications iterative antenna selection mixed-integer nonlinear programming nonlinear fractional programming theory power allocation algorithm transmit power Antennas Optimized production technology programming Resource management Upper bound Wireless communication

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A discretization-based approach for the optimization of the multiperiod blend scheduling problem

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COMPUTERS & CHEMICAL ENGINEERING 2013年 53卷 122-142页

作者： Kolodziej, Scott P. Grossmann, Ignacio E. Furman, Kevin C. Sawaya, Nicolas W. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA ExxonMobil Upstream Res Co Houston TX 77098 USA ExxonMobil Gas & Power Mkt Co Houston TX 77002 USA

In this paper, we introduce a new generalized multiperiod scheduling version of the pooling problem to represent time varying blending systems. A general nonconvex MINLP formulation of the problem is presented. The primary difficulties in solving this optimization problem are the presence of bilinear terms, as well as binary decision variables required to impose operational constraints. An illustrative example is presented to provide unique insight into the difficulties faced by conventional MINLP approaches to this problem, specifically in finding feasible solutions. Based on recent work, a new radix-based discretization scheme is developed with which the problem can be reformulated approximately as an MILP, which is incorporated in a heuristic procedure and in two rigorous global optimization methods, and requires much less computational time than existing global optimization solvers. Detailed computational results of each approach are presented on a set of examples, including a comparison with other global optimization solvers. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Pooling problem mixed-integer nonlinear programming Bilinear programming Global optimization Petroleum operations

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Integrated Scheduling and Dynamic Optimization of Sequential Batch Processes with Online Implementation

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AICHE JOURNAL 2013年第7期59卷 2379-2406页

作者： Chu, Yunfei You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

An efficient decomposition method to solve the integrated problem of scheduling and dynamic optimization for sequential batch processes is proposed. The integrated problem is formulated as a mixed-integer dynamic optimization problem or a large-scale mixed-integer nonlinear programming (MINLP) problem by discretizing the dynamic models. To reduce the computational complexity, we first decompose all dynamic models from the integrated problem, which is then approximated by a scheduling problem based on the flexible recipe. The recipe candidates are expressed by Pareto frontiers, which are determined offline by using multiobjective dynamic optimization to minimize the processing cost and processing time. The operational recipe is then optimized simultaneously with the scheduling decisions online. Because the dynamic models are encapsulated by the Pareto frontiers, the online problem is a mixed-integer programming problem which is much more computationally efficient than the original MINLP problem, and allows the online implementation to deal with uncertainties. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 2379-2406, 2013

关键词： integrated scheduling and dynamic optimization decomposition mixed-integer dynamic optimization mixed-integer nonlinear programming multiobjective dynamic optimization

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Generalized orienteering problem with resource dependent rewards

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NAVAL RESEARCH LOGISTICS 2013年第4期60卷 294-312页

作者： Pietz, Jesse Royset, Johannes O. USN Dept Operat Res Postgrad Sch Monterey CA USA

We introduce a generalized orienteering problem (OP) where, as usual, a vehicle is routed from a prescribed start node, through a directed network, to a prescribed destination node, collecting rewards at each node visited, to maximize the total reward along the path. In our generalization, transit on arcs in the network and reward collection at nodes both consume a variable amount of the same limited resource. We exploit this resource trade-off through a specialized branch-and-bound algorithm that relies on partial path relaxation problems that often yield tight bounds and lead to substantial pruning in the enumeration tree. We present the smuggler search problem (SSP) as an important real-world application of our generalized OP. Numerical results show that our algorithm applied to the SSP outperforms standard mixed-integer nonlinear programming solvers for moderate to large problem instances. We demonstrate model enhancements that allow practitioners to represent realistic search planning scenarios by accounting for multiple heterogeneous searchers and complex smuggler motion. (c) 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013

关键词： orienteering problem military operations research search and surveillance route planning mixed-integer nonlinear programming

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Second-order cone programming for solving unit commitment strategy of thermal generators

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ENERGY CONVERSION AND MANAGEMENT 2013年 76卷 20-25页

作者： Yuan, Xiaohui Tian, Hao Zhang, Shuangquan Ji, Bin Hou, Yanhong Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

The short-term unit commitment (UC) problem of hydrothermal generation systems is a mixed-integer nonlinear programming (MINLP), which is difficult to solve efficiently, especially for large-scale instances. The perspective relaxation (PR) is an effective approach to constructing tight approximations to MINLP with semi-continuous variables. In this paper, the PR of UC problem is formulated as a mixed integer second-order cone programming (SOCP) model because the quadratic polynomial cost function of the UC problem is SOCP-representable. The proposed model is implemented by using the commercial optimization software IBM CPLEX 12.4. Extensive numerical studies have been conducted to verify the advantages of our proposed method. Instances of the test system vary from 10 to 1000 units. Our results indicate that the proposed method performs better than the existing methods in terms of production cost savings and faster computational times, especially for large systems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： mixed-integer nonlinear programming Second-order cone programming Separable convex function Unit commitment

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A decomposition approach for a new test-scenario in complex problem solving

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JOURNAL OF COMPUTATIONAL SCIENCE 2013年第4期4卷 245-254页

作者： Engelhart, Michael Funke, Joachim Sager, Sebastian Heidelberg Univ Interdisciplinary Ctr Sci Comp IWR D-69120 Heidelberg Germany Heidelberg Univ Dept Psychol D-69117 Heidelberg Germany Univ Magdeburg Inst Math Optimizat D-39106 Magdeburg Germany

Over the last years, psychological research has increasingly used computer-supported tests, especially in the analysis of complex human decision making and problem solving. The approach is to use computerbased test scenarios and to evaluate the performance of participants and correlate it to certain attributes, such as the participant's capacity to regulate emotions. However, two important questions can only be answered with the help of modern optimization methodology. The first one considers an analysis of the exact situations and decisions that led to a bad or good overall performance of test persons. The second important question concerns performance, as the choices made by humans can only be compared to one another, but not to the optimal solution, as it is unknown in general. Additionally, these test-scenarios have usually been defined on a trial-and-error basis, until certain characteristics became apparent. The more complex models become, the more likely it is that unforeseen and unwanted characteristics emerge in studies. To overcome this important problem, we propose to use mathematical optimization methodology not only as an analysis and training tool, but also in the design stage of the complex problem scenario. We present a novel test scenario, the IWR Tailorshop, with functional relations and model parameters that have been formulated based on optimization results. We also present a tailored decomposition approach to solve the resulting mixed-integer nonlinear programs with nonconvex relaxations and show some promising results of this approach. (C) 2012 Elsevier B.V. All rights reserved.

关键词： mixed-integer nonlinear programming Complex problem solving Decomposition approach

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Evaluating shortfalls in mixed-integer programming approaches for the optimal design and dispatch of distributed generation systems

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APPLIED ENERGY 2013年 102卷 386-398页

作者： Pruitt, Kristopher A. Braun, Robert J. Newman, Alexandra M. Colorado Sch Mines Div Econ & Business Golden CO 80401 USA Colorado Sch Mines Coll Engn & Computat Sci Dept Mech Engn Golden CO 80401 USA

The distributed generation (DG) of combined heat and power (CHP) for commercial buildings is gaining increased interest, yet real-world installations remain limited. This lack of implementation is due, in part, to the challenging economics associated with volatile utility pricing and potentially high system capital costs. Energy technology application analyses are also faced with insufficient knowledge regarding how to appropriately design (i.e., configure and size) and dispatch (i.e., operate) an integrated CHP system. Existing research efforts to determine a minimum-cost-system design and dispatch do not consider many dynamic performance characteristics of generation and storage technologies. Consequently, we present a mixed-integer nonlinear programming (MINLP) model that prescribes a globally minimum cost system design and dispatch, and that includes off-design hardware performance characteristics for CHP and energy storage that are simplified or not considered in other models. Specifically, we model the maximum turn-down, start up, ramping, and part-load efficiency of power generation technologies, and the time-varying temperature of thermal storage technologies. The consideration of these characteristics can be important in applications for which system capacity, building demand, and/or utility guidelines dictate that the dispatch schedule of the devices varies over time. We demonstrate the impact of neglecting system dynamics by comparing the solution prescribed by a simpler, linear model with that of our MINLP for a case study consisting of a large hotel, located in southern Wisconsin, retrofitted with solid-oxide fuel cells (SOFCs) and a hot water storage tank. The simpler model overestimates the SOFC operational costs and, consequently, underestimates the optimal SOFC capacity by 15%. Published by Elsevier Ltd.

关键词： Optimization mixed-integer nonlinear programming Distributed generation Combined heat and power Fuel cells Building energy

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Bunker consumption optimization methods in shipping: A critical review and extensions

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2013年第1期53卷 49-62页

作者： Wang, Shuaian Meng, Qiang Liu, Zhiyuan Univ Wollongong Sch Math & Appl Stat Wollongong NSW 2522 Australia Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Monash Univ Inst Transport Studies Dept Civil Engn Clayton Vic 3800 Australia

It is crucial nowadays for shipping companies to reduce bunker consumption while maintaining a certain level of shipping service in view of the high bunker price and concerned shipping emissions. After introducing the three bunker consumption optimization contexts: minimization of total operating cost, minimization of emission and collaborative mechanisms between port operators and shipping companies, this paper presents a critical and timely literature review on mathematical solution methods for bunker consumption optimization problems. Several novel bunker consumption optimization methods are subsequently proposed. The applicability, optimality, and efficiency of the existing and newly proposed methods are also analyzed. This paper provides technical guidelines and insights for researchers and practitioners dealing with the bunker consumption issues. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Shipping Sailing speed Bunker consumption optimization mixed-integer nonlinear programming

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Sustainable Design and Synthesis of Algae-Based Biorefinery for Simultaneous Hydrocarbon Biofuel Production and Carbon Sequestration

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AICHE JOURNAL 2013年第5期59卷 1599-1621页

作者： Gebreslassie, Berhane H. Waymire, Randall You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

The superstructure optimization of algae-based hydrocarbon biorefinery with sequestration of CO2 from power plant flue gas is proposed. The major processing steps include carbon capture, algae growth, dewatering, lipid extraction and power generation, and algal biorefinery. We propose a multiobjective mixed-integer nonlinear programming (MINLP) model that simultaneously maximizes the net present value (NPV) and minimizes the global warming potential (GWP) subject to technology selection constraints, mass balance constraints, energy balance constraints, technoeconomic analysis constraints, and environmental impact constraints. The model simultaneously determines the optimal decisions that include production capacity, size of each processing unit, mass flow rates at each stage of the process, utility consumption, economic, and environmental performances. We propose a two-stage heuristic solution algorithm to solve the non-convex MINLP model. Finally, the bicriteria optimization problem is solved with e-constraint method, and the resulting Pareto-optimal curve reveals the trade-off between the economic and environmental criteria. The results show that for maximum NPV, the optimal process design uses direct flue gas, a tubular photobioreactor for algae growth, a filtration dewatering unit, and a hydroprocessing pathway leading to 47.1 MM gallons of green diesel production per year at $6.33/gal corresponding to GWP of 108.7 kg CO2-eq per gallon. (C) 2013 American Institute of Chemical Engineers AIChE J, 59: 1599-1621, 2013

关键词： sustainability biorefinery mixed-integer nonlinear programming superstructure multiobjective

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