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Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network

ELECTRIC POWER SYSTEMS RESEARCH 2021年 195卷

作者： Ji, Xingquan Yin, Ziyang Zhang, Yumin Xu, Bo Liu, Qi Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China State Grid Energy Res Inst Co Beijing 102209 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China

In this work, a novel real-time autonomous dynamic reconfiguration (ADR) method is proposed to reduce the cost of power loss and switch action of distribution network based on the deep learning (DL) algorithm. The proposed ADR method can be decision-making from the historical control dataset and the real-time system state. To obtain the historical control dataset efficiently, the heuristic rule is first introduced to improve the mixed integer convex programming method for making reconfiguration strategies to support grid operate in cost effective manner. To learn the reconfiguration strategy from the historical dataset, a long-short term memory network (LSTM) is presented to provide the effective training on the historical control dataset, and a switch action function considering real-time differences of operation cost is formulated, which can be combined with the trained LSTM model to achieve ADR. Case studies on the IEEE 33-bus system and a realistic Taiwan power company (TPC) 84-bus system verify that the ADR can obtain an ideal reconfiguration solution in the order of milliseconds and has high robustness.

关键词： Distribution network reconfiguration Deep learning Long-short term memory network mixed-integer convex programming

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Optimal Resource Allocation for NOMA-Enabled Cache Replacement and Content Delivery 30

Optimal Resource Allocation for NOMA-Enabled Cache Replaceme...

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30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Lei, Lei Vu, Thang X. Xiang, Lin Zhang, Xingjun Chatzinotas, Symeon Ottersten, Bjorn Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781538681107

In a content-delivery network, files' popularity and users' requests change fast. Conventional caching schemes, e.g., caching (re)placement once per day during the off-peak hours, may not capture the up-to-date popularity. In this case, the contents in caches have to be regularly updated to prevent information becoming outdated, and at the same time users' requested files must be delivered. These two tasks are challenging in practical heavy-traffic and multi-user scenarios when the network resources are limited. In this paper, we apply non-orthogonal multiple access (NOMA) to facilitate concurrent caching replacement and content delivery in downlink transmission. We formulate a resource allocation problem to investigate how to efficiently push proactive files to the cache at the small base station and deliver the requested files to users. The resource-allocation problem is formulated as a mixed-integer exponential conic optimization problem. To enable a computationally-efficient optimal solution with finite convergence, we develop an iterative algorithm based on polyhedral outer approximation, where a polyhedral relaxation subproblem and a convex subproblem are constructed and iteratively solved to tighten the lower and upper bounds for the optimum, respectively. The numerical results demonstrate significant performance gains of the NOMA-enabled data transmission scheme in power and resource savings compared to the baseline scheme.

关键词： NOMA caching resource allocation mixed-integer convex programming

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Integrated Transmission and Distribution Systems Restoration with Distributed Generation Scheduling

Integrated Transmission and Distribution Systems Restoration...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Nejad, Reza Roofegari Golshani, Amir Sun, Wei Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9781538677032

The self-healing smart grids enable system operators to quickly and efficiently recover transmission and distribution systems from outages and blackouts. The current restoration procedure solves the transmission and distribution system restoration problems separately without considering their impacts on each other, which may prolong the restoration process. However, with the emerging active distribution networks, distributed generators can contribute into the bottom-up restoration strategy. The interaction between the restoration of two interconnected systems should be taken into account. In this paper, a hybrid model of transmission and distribution systems restoration (HTDSR) is proposed by exchanging data of power and voltage between two separate optimization problems. The effectiveness of the HTDSR algorithm is demonstrated through case studies of the integrated IEEE 13-bus distribution network and IEEE 39-bus transmission grid. Simulation results demonstrate that the integrated restoration strategy with optimal scheduling of distributed generation sources can expedite the restoration process.

关键词： Distributed generation Integrated transmission and distribution systems restoration Load pickup mixed-integer convex programming Self-healing

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A disjunctive convex programming approach to the pollution-routing problem

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2016年 94卷 61-79页

作者： Fukasawa, Ricardo He, Qie Song, Yongjia Univ Waterloo Fac Math Dept Combinator & Optimizat Waterloo ON N2L 3G1 Canada Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA Virginia Commonwealth Univ Dept Stat Sci & Operat Res Richmond VA 23284 USA

The pollution-routing problem (PRP) aims to determine a set of routes and speed over each leg of the routes simultaneously to minimize the total operational and environmental costs. A common approach to solve the PRP exactly is through speed discretization, i.e., assuming that speed over each arc is chosen from a prescribed set of values. In this paper, we keep speed as a continuous decision variable within an interval and propose new formulations for the PRP. In particular, we build two mixed-integer convex optimization models for the PRP, by employing tools from disjunctive convex programming. These are the first arc-based formulations for the PRP with continuous speed. We also derive several families of valid inequalities to further strengthen both models. We test the proposed formulations on benchmark instances. Some instances are solved to optimality for the first time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Pollution-routing problem Speed optimization Green transportation mixed-integer convex programming

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Benders decomposition without separability: A computational study for capacitated facility location problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第3期253卷 557-569页

作者： Fischetti, Matteo Ljubic, Ivana Sinnl, Markus Univ Padua Dept Informat Engn I-35100 Padua Italy ESSEC Business Sch Paris Paris France Univ Vienna Dept Stat & Operat Res A-1010 Vienna Austria

Benders is one of the most famous decomposition tools for Mathematical programming, and it is the method of choice e.g., in mixed-integer stochastic programming. Its hallmark is the capability of decomposing certain types of models into smaller subproblems, each of which can be solved individually to produce local information (notably, cutting planes) to be exploited by a centralized "master" problem. As its name suggests, the power of the technique comes essentially from the decomposition effect, i.e., the separability of the problem into a master problem and several smaller subproblems. In this paper we address the question of whether the Benders approach can be useful even without separability of the subproblem, i.e., when its application yields a single subproblem of the same size as the original problem. In particular, we focus on the capacitated facility location problem, in two variants: the classical linear case, and a "congested" case where the objective function contains convex but non-separable quadratic terms. We show how to embed the Benders approach within a modern branch-and-cut mixed-integer programming solver, addressing explicitly all the ingredients that are instrumental for its success. In particular, we discuss some computational aspects that are related to the negative effects derived from the lack of separability. Extensive computational results on various classes of instances from the literature are reported, with a comparison with the state-of-the-art exact and heuristic algorithms. The outcome is that a clever but simple implementation of the Benders approach can be very effective even without separability, as its performance is comparable and sometimes even better than that of the most effective and sophisticated algorithms proposed in the previous literature. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Benders decomposition Congested capacitated facility location Perspective reformulation Branch-and-cut mixed-integer convex programming

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CLOSEDNESS OF integer HULLS OF SIMPLE CONIC SETS

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SIAM JOURNAL ON DISCRETE MATHEMATICS 2016年第1期30卷 70-99页

作者： Moran, Diego A. Dey, Santanu S. Virginia Polytech Inst & State Univ Dept Ind & Syst Engn Blacksburg VA 24061 USA Univ Adolfo Ibanez Sch Business Diagonal Las Torres 2640 Santiago Chile Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Let C be a full-dimensional pointed closed convex cone in R-m obtained by taking the conic hull of a strictly convex set. Given A is an element of Q(mxn1), B is an element of Q(mxn2), and b is an element of Q(m), a simple conic mixed-integer set (SCMIS) is a set of the form {(x, y) is an element of Z(n1) x R-n2 vertical bar Ax + By - b is an element of C}. In this paper, we give a complete characterization of the closedness of convex hulls of SCMISs. Under certain technical conditions on the cone C, we show that the closedness characterization can be used to construct a polynomial-time algorithm to check the closedness of convex hulls of SCMISs. Moreover, we also show that the Lorentz cone satisfies these technical conditions. In the special case of pure integer problems, we present sufficient conditions, which can be checked in polynomial time, to verify the closedness of intersection of SCMISs.

关键词： closedness polynomial-time algorithm mixed-integer convex programming

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A perspective-based convex relaxation for switched-affine optimal control

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SYSTEMS & CONTROL LETTERS 2015年 86卷 34-40页

作者： Moehle, Nicholas Boyd, Stephen Stanford Univ Dept Mech Engn Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

We consider the switched-affine optimal control problem, i.e., the problem of selecting a sequence of affine dynamics from a finite set in order to minimize a sum of convex functions of the system state. We develop anew reduction of this problem to a mixed-integer convex program (MICP), based on perspective functions. Relaxing the integer constraints of this MICP results in a convex optimization problem, whose optimal value is a lower bound on the original problem value. We show that this bound is at least as tight as similar bounds obtained from two other well-known MICP reductions (via conversion to a mixed logical dynamical system, and by generalized disjunctive programming), and our numerical study indicates it is often substantially tighter. Using simple integer-rounding techniques, we can also use our formulation to obtain an upper bound (and corresponding sequence of control inputs). In our numerical study, this bound was typically within a few percent of the optimal value, making it attractive as a stand-alone heuristic, or as a subroutine in a global algorithm such as branch and bound. We conclude with some extensions of our formulation to problems with switching costs and piecewise affine dynamics. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Switched-affine systems Optimal control mixed-integer convex programming Hybrid systems Disjunctive programming

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A mixed-integer convex FORMULATION FOR PRODUCTION OPTIMIZATION OF GAS-LIFTED OIL FIELDS WITH ROUTING AND PRESSURE CONSTRAINTS

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BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2014年第2期31卷 439-455页

作者： Aguiar, M. A. S. Camponogara, E. Silva, T. L. Univ Fed Santa Catarina Dept Automat & Syst Engn BR-88040900 Florianopolis SC Brazil

Production optimization of gas-lifted oil fields under facility, routing, and pressure constraints has attracted the attention of researchers and practitioners for its scientific challenges and economic impact. The available methods fall into one of two categories: nonlinear or piecewise-linear approaches. The nonlinear methods optimize simulation models directly or use surrogates obtained by curve fitting. The piecewise-linear methods represent the nonlinear functions using a convex combination of sample points, thereby generating a mixed-integer Linear programming (MILP) problem. The nonlinear methods rely on compact models, but can get stuck in local minima, whereas the piecewise-linear methods can reach globally optimal solutions, but their models tend to get very large. This work combines these methods, whereby piecewise-linear models are used to approximate production functions, which are then composed with convex-quadratic models that approximate pressure drops. The end result is a mixed-integer convex programming (MICP) problem which is more compact than the MILP model and for which globally optimal solutions can be reached.

关键词： MINLP MILP mixed-integer convex programming Oil Production Optimization

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A Novel Solution Methodology for Solving Large-scale Thermal Unit Commitment Problems

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2010年第14期38卷 1615-1634页

作者： Xie, Yu-Guang Chiang, Hsiao-Dong Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Elect Engn Shanghai 200030 Peoples R China

An effective solution methodology for solving large-scale unit commitment problems is proposed. In order to model the line losses, the second-order network model is introduced into the conventional unit commitment problem. The proposed solution methodology consists of three key steps: (1) a transformed problem formulation that is based on a mixed-integer convex programming problem, (2) an improved solution method that is a combination of a modified branch-and-bound method and an interior point method, and (3) some accelerating strategies to speed up the proposed solution method. During the branch-and-bound process, the best first search and depth first search strategy are combined to expedite the required computation. In order to achieve the goal of obtaining a fast, near-optimal feasible solution of large-scale unit commitment problems, some accelerating strategies are developed. An effective scheme to partition the study period is also presented. The proposed solution methodology is applied to several test systems, including a 10-unit system and modified IEEE 118-bus system with 54 thermal units. Numerical study results indicate that the proposed solution methodology is fast and effective in dealing with various constraints and is promising for solving large-scale unit commitment problems. The proposed solution methodology finds better unit commitment solutions than most other methods. In addition, the solutions obtained by the proposed solution methodology are close to the optimal total operational cost. Especially for 60-unit and 80-unit unit commitment problems, the proposed solution methodology finds the best solutions.

关键词： unit commitment second-order network model branch and bound interior point method mixed-integer convex programming

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