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MPC-based framework incorporating pre-disaster and post-disaster actions and transportation network constraints for weather-resilient power distribution networks

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APPLIED ENERGY 2024年 362卷

作者： Souto, Laiz Parisio, Alessandra Taylor, Philip C. Univ Bristol Dept Elect & Elect Engn Queens Bldg Bristol BS8 1TR England Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL England

Enhancing power system resilience to extreme weather requires an effective coordination of pre-disaster preventive response actions and post-disaster repair and restoration services. In this context, this article presents a two -stage framework to enhance resilience of power distribution networks to severe weather events. It considers operating and resource constraints in interdependent power and transportation networks based on official guidance from transport appraisal studies. It also incorporates sources of information available in a smart city context, such as energy measurements, traffic flows, and geographical information systems. In the first stage, a pre -disaster operational planning strategy is defined to prepare the grid for a high -risk outage scenario with the objective of minimizing the value of lost loads, formulated as a mixed -integer quadratic programming model. In the second stage, a post -disaster corrective strategy is implemented over a receding time horizon to compensate for deviations between the actions computed in the first stage and the actions required to minimize power outages, embedded into a model predictive control scheme. The effectiveness of the proposed framework is demonstrated on a real -world large-scale distribution network in the United Kingdom over a range of outage scenarios. Simulation results show that the proposed framework is capable of effectively minimizing first -stage operational costs and second -stage deviations between the projected and actual load demand supply. Numerical results obtained with the proposed framework indicate that the load energy unserved is at least twice smaller than with typical practices adopted by distribution network operators and computational times take two minutes or less. Therefore, it can be effectively used by distribution network operators to ensure an appropriate level of preparedness to power outages caused by extreme weather along with prompt restoration and repair services.

关键词： Extreme weather mixed-integer quadratic programming Model predictive control Power distribution networks Power system resilience Power system restoration Smart grids

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On handling indicator constraints in mixed integer programming

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2016年第3期65卷 545-566页

作者： Belotti, Pietro Bonami, Pierre Fischetti, Matteo Lodi, Andrea Monaci, Michele Nogales-Gomez, Amaya Salvagnin, Domenico FICO Birmingham W Midlands England IBM Corp Madrid Spain Univ Padua Padua Italy Univ Bologna Bologna Italy Ecole Polytech Montreal Montreal PQ Canada Huawei France R&D Math & Algorithm Sci Lab Paris France IBM Corp Milan Italy

mixed integer programming (MIP) is commonly used to model indicator constraints, i.e., constraints that either hold or are relaxed depending on the value of a binary variable. Unfortunately, those models tend to lead to weak continuous relaxations and turn out to be unsolvable in practice;this is what happens, for e.g., in the case of Classification problems with Ramp Loss functions that represent an important application in this context. In this paper we show the computational evidence that a relevant class of these Classification instances can be solved far more efficiently if a nonlinear, nonconvex reformulation of the indicator constraints is used instead of the linear one. Inspired by this empirical and surprising observation, we show that aggressive bound tightening is the crucial ingredient for solving this class of instances, and we devise a pair of computationally effective algorithmic approaches that exploit it within MIP. One of these methods is currently part of the arsenal of IBM-Cplex since version 12.6.1. More generally, we argue that aggressive bound tightening is often overlooked in MIP, while it represents a significant building block for enhancing MIP technology when indicator constraints and disjunctive terms are present.

关键词： mixed-integer linear programming mixed-integer quadratic programming Indicator constraints

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A branch-and-cut algorithm for mixed-integer Bilinear programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期282卷 506-514页

作者： Fischetti, Matteo Monaci, Michele Univ Padua DEI Via Gradenigo 6-A I-35100 Padua Italy Univ Bologna DEI Guglielmo Marconi Viale Risorgimento 2 I-40136 Bologna Italy

In this paper, we consider the mixed-integer Bilinear programming problem, a widely-used reformulation of the classical mixed-integer quadratic programming problem. For this problem we describe a branch and -cut algorithm for its exact solution, based on a new family of intersection cuts derived from bilinearspecific disjunctions. We also introduce a new branching rule that is specifically designed for bilinear problems. We computationally analyze the behavior of the proposed algorithm on a large set of mixed-integer quadratic instances from the MINLPIib problem library. Our results show that our method, even without intersection cuts, is competitive with a state-of-the-art mixed-integer nonlinear solver. As to intersection cuts, their extensive use at each branching node tends to slow down the solver for most problems in our test bed, but they are extremely effective for some specific instances. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization mixed-integer quadratic programming Bilinear programming Branch-and-cut algorithms Intersection cuts

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Online Voltage Regulation of Active Distribution Networks: A Deep Neural Encoding-Decoding Approach

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第2期39卷 4574-4586页

作者： Sang, Linwei Xu, Yinliang Wu, Wenchuan Long, Huan Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

Voltage regulation plays a vital role in active distribution networks, which can be posed as mixed-integer quadratic programming (MIQP). To alleviate the real-time MIQP solution burden, this article proposes a deep neural encoding-decoding approach for online voltage regulation, including offline encoding and online decoding processes. Offline encoding extracts the integer variables and active constraints of the MIQP regulation problem and reduces the MIQP into quadratic programming (QP). The KKT-based solution matrices derived from the reduced QP are stored via the compact triangular matrix factorization way. Deep neural network (DNN) is trained to encode the optimal voltage regulation solution into the regulation strategy. Online decoding utilizes the trainedDNNto identify the optimal regulation strategies, which selects the top-k most likely strategies under various scenarios. Then it decodes the k strategies into precomputed KKT-based linear solution matrices for real-time regulation. The above online voltage regulation can be calculated efficiently without any solver. Case studies verify the proposed approach's voltage regulation effectiveness and calculation efficiency.

关键词： Deep learning voltage regulation online optimization mixed-integer quadratic programming

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Compute-and-Forward on a Multiaccess Relay Channel: Coding and Symmetric-Rate Optimization

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2014年第4期13卷 1932-1947页

作者： El Soussi, Mohieddine Zaidi, Abdellatif Vandendorpe, Luc Catholic Univ Louvain ICTEAM B-1348 Louvain Belgium Univ Paris Est Marne La Vallee F-77454 Marne La Vallee 2 France

We consider a system in which two users communicate with a destination with the help of a half-duplex relay. Based on the compute-and-forward scheme, we develop and evaluate the performance of coding strategies that are of network coding spirit. In this framework, instead of decoding the users' information messages, the destination decodes two integer-valued linear combinations that relate the transmitted codewords. Two decoding schemes are considered. In the first one, the relay computes one of the linear combinations and then forwards it to the destination. The destination computes the other linear combination based on the direct transmissions. In the second one, accounting for the side information available at the destination through the direct links, the relay compresses what it gets using lattice-based Wyner-Ziv compression and conveys it to the destination. The destination then computes the two linear combinations, locally. For both coding schemes, we discuss the design criteria, and derive the allowed symmetric-rate. Next, we address the power allocation and the selection of the integer-valued coefficients to maximize the offered symmetric-rate;an iterative coordinate descent method is proposed. The analysis shows that the first scheme can outperform standard relaying techniques in certain regimes, and the second scheme, while relying on feasible structured lattice codes, can at best achieve the same performance as regular compress-and-forward for the multiaccess relay network model that we study. The results are illustrated through some numerical examples.

关键词： Compute-and-forward network coding lattice codes relay channel geometric programming mixed-integer quadratic programming

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A deterministic method for the unit commitment problem in power systems

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COMPUTERS & OPERATIONS RESEARCH 2016年 66卷 241-247页

作者： Zheng, Haiyan Jian, Jinbao Yang, Linfeng Loan, Ran Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Yulin Normal Univ Guangxi Coll & Univ Key Lab Complex Syst Optimiza Coll Math & Informat Sci Yulin 537000 Peoples R China Guangxi Univ Coll Comp Elect & Informat Nanning 530004 Peoples R China Henan Univ Technol Coll Sci Zhengzhou 450001 Peoples R China

The unit commitment (UC) problem in power systems is generally formulated as a large-scale nonlinear mixed-integer combinatorial optimization problem, which is difficult to solve. This paper presents a deterministic method named cut-and-branch for solving UC, which is based on cuts and branch-and-bound search as well as heuristic rounding technique. First, a suitable mixed-integer quadratic programming (MIQP) model of UC is presented by some linearization technique, then the MIQP is solved by the proposed cut-and-branch method. In the proposed method, two classes of cuts are introduced to give a stronger representation of the corresponding continuous relaxed problem: one is the approximate integer cut derived from a natural understanding of the problem which is simple but highly efficient, and the other is the generalized flow cover inequality. Furthermore, the continuous relaxed problem incorporating the proposed cuts can obtain some better initial feasible solutions and reduce the numbers of nodes during the branch-and-bound search. The simulation results for six systems with up to 100 units and 24 h show that the proposed method has nice convergence, which can find global optimal solution in theory. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Unit commitment mixed-integer quadratic programming Approximate integer cut Generalized flow cover inequality Cut-and-branch method

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A two-stage approach to the UCITS-constrained index-tracking problem

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COMPUTERS & OPERATIONS RESEARCH 2019年 103卷 167-183页

作者： Strub, O. Trautmann, N. Univ Bern Dept Business Adm Schutzenmattstr 14 CH-3012 Bern Switzerland

Undertakings for Collective Investments in Transferable Securities (UCITS) are investment funds that are regulated by the European Union. UCITS have become increasingly popular, resulting in a total corresponding amount of assets under management of (sic) 8.5 trillion by the end of 2016. We present a two-stage approach to the problem of how to construct a portfolio of assets for a UCITS that aims to replicate the returns of a financial index subject to the constraints imposed by the UCITS regulations. In the first stage, we apply a genetic algorithm that treats subsets of the index constituents as individuals to construct a good feasible solution in a short CPU time. In this genetic algorithm, we use a new representation of subsets, which is the first to exhibit all of the following four desirable properties: feasibility, efficiency, locality, and heritability. In the second stage, we apply local branching based on a new mixed-integer quadratic programming formulation to improve the best solution obtained in the first stage. In a numerical experiment on real-world data, the approach yields very good feasible solutions in a short CPU time. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Portfolio management Index tracking mixed-integer quadratic programming Heuristics

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A Polynomial-Time Algorithm with Tight Error Bounds for Single-Period Unit Commitment Problem

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Journal of the Operations Research Society of China 2023年第1期11卷 1-28页

作者： Ruo-Tian Gao Shu-Cherng Fang Cheng Lu Wen-Xun Xing Big Data Center of State Grid Corporation of China Beijing 100053China Department of Industrial and System Engineering North Carolina State UniversityRaleigh 27695-7906USA School of Economics and Management North China Electric Power UniversityBeijing 102206China Department of Mathematical Sciences Tsinghua UniversityBeijing 100084China

This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and proven to be *** theoretical bounds for the absolute errors and relative errors of the approximate solutions generated by the proposed algorithm are *** results support the effectiveness and efficiency of the proposed algorithm for solving large-scale problems.

关键词： Nonlinear programming Lagrangian dual Unit commitment problem mixed-integer quadratic programming Convex relaxation

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Disassembly line optimization with reinforcement learning

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2024年第4期32卷 1115-1142页

作者： Kegyes, Tamas Sule, Zoltan Abonyi, Janos Univ Pannonia HUN REN PE Complex Syst Monitoring Res Grp Egyet Utca 10 H-8200 Veszprem Hungary Univ Pannonia Dept Comp Sci & Syst Technol Egyet Utca 10 H-8200 Veszprem Hungary

As the environmental aspects become increasingly important, the disassembly problems have become the researcher's focus. Multiple criteria do not enable finding a general optimization method for the topic, but some heuristics and classical formulations provide effective solutions. By highlighting that disassembly problems are not the straight inverses of assembly problems and the conditions are not standard, disassembly optimization solutions require human control and supervision. Considering that Reinforcement learning (RL) methods can successfully solve complex optimization problems, we developed an RL-based solution for a fully formalized disassembly problem. There were known successful implementations of RL-based optimizers. But we integrated a novel heuristic to target a dynamically pre-filtered action space for the RL agent (dlOptRL algorithm) and hence significantly raise the efficiency of the learning path. Our algorithm belongs to the Heuristically Accelerated Reinforcement Learning (HARL) method class. We demonstrated its applicability in two use cases, but our approach can also be easily adapted for other problem types. Our article gives a detailed overview of disassembly problems and their formulation, the general RL framework and especially Q-learning techniques, and a perfect example of extending RL learning with a built-in heuristic.

关键词： Disassembly line optimization Reinforcement learning Q-learning mixed-integer quadratic programming Heuristically accelerated reinforcement learning dlOptRL algorithm

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Integrated Energy Management Scheme Considering Transportation Flexibility for Highway Service Zones

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第1期60卷 1357-1368页

作者： Ding, Zhaohao Zhang, Yu Yang, Dechang Dai, Liang Wang, Dawei Xu, Hongke Zhao, Long Dehghanian, Payman North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China South Dakota Sch Mines & Technol Rapid City SD 57702 USA George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

The highway transportation system exhibits immense potential for adaptive functioning due to its distinctive operational attributes. This capability permits to harness more renewable energy, leading to a reduction in energy consumption costs. In this article, an integrative energy system optimization model is introduced from the standpoint of highway service zone operators. To simulate the behavior of electric vehicles on highways, an expanded network flow model is formulated considering driving, queueing, and charging actions. This model comprehensively integrates dimensions of time, space, and the state of charge of the vehicles. Consequently, the proposed model is proficient in representing the spatiotemporal and flexible operational traits inherent to electric vehicles. Through a series of numerical case studies executed on a real-world highway system, the effectiveness of the proposed model is demonstrated. These findings substantiate the potential utility and efficacy of our model in enhancing the operation optimization of energy systems within highway transportation systems.

关键词： Distributed renewable resource electric vehicles highway system mixed-integer quadratic programming network flow model

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