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International Conference on Power System Technology (POWERCON)

作者： Chen Yongjin Chen Xiaoming Wu Zhaolong Gao Yifang Zhang Shijian Guangdong Power Grid Co Shaoguan Power Supply Bur Shaoguan 512028 Peoples R China Guangzhou Power Elect Technol Co Ltd Guangzhou 511475 Guangdong Peoples R China

ISBN: (纸本)9781538664612

This paper proposes a mixed integer convex programming based method for the coordination of distribution system reconfiguration (DSR) and reactive power optimization (RPO). Firstly, this paper establishes a coordination model for DSR and RPO. Then, the original model is converted into a mixed integer convex programming (MICP) model. A polyhedral approximation for the MICP model, which can be solved by widely available mixed integer linear programming (MILP) optimization software is also presented. Finally, simulations on the benchmark system and real distribution system verifies the validity of the proposed method.

关键词： coordination optimization distribution system reconfiguration reactive power optimization mixed integer convex programming

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Reliability Aware Service Placement Using a Viterbi-Based Algorithm

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2020年第1期17卷 622-636页

作者： Karimzadeh-Farshbafan, Mohammad Shah-Mansouri, Vahid Niyato, Dusit Univ Tehran Sch Elect & Comp Engn Tehran *** Iran Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Network function virtualization (NFV) is referred to as the deployment of software functions running on commodity servers, instead of hardware middleboxes. It is an inevitable technology for agile service provisioning in next-generation telecommunication networks. A service is defined as a chain of software functions, named virtual network functions (VNFs), where each VNF can be placed on different host servers. The task of assigning the VNFs to the host servers is called service placement. A significant challenge in service placement is meeting the reliability requirement of a service. In the literature, the problem of service placement and providing the required reliability level are considered separately. First, the main server is selected, and then, the backup servers are deployed to meet the reliability requirement of the service. In this paper, we consolidate these two steps and perform them jointly and simultaneously. We consider a multi-infrastructure network provider (InP) environment where InPs offer general purpose commodity servers with different reliability levels. Then, we propose a programming problem for main and backup server selection jointly minimizing the cost of resources of the InPs and maximizing the reliability of the service. We reformulate this problem as a mixed integer convex programming (MICP) problem. Since MICPs are known to be NP-hard in general, we propose a polynomial time sub-optimal algorithm named Viterbi-based Reliable Service Placement (VRSP). Using numerical evaluations, we investigate the performance of the proposed algorithm compared to the optimal solution resulting from the MICP model and also with three heuristic algorithms.

关键词： Network function virtualization reliable service placement mixed integer convex programming Viterbi algorithm

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Identification of Outages in Power Systems With Uncertain States and Optimal Sensor Locations

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2014年第6期8卷 1140-1153页

作者： Zhao, Yue Chen, Jianshu Goldsmith, Andrea Poor, H. Vincent Stanford Univ Dept Elect Engn Stanford CA 94305 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

Joint outage identification and state estimation in power systems is studied. A Bayesian framework is employed, and a Gaussian prior distribution of the states is assumed. The joint posterior of the outage hypotheses and the network states is developed in closed form, which can be applied to obtain the optimal joint detector and estimator under any given performance criterion. Employing the minimum probability of error as the performance criterion in identifying outages with uncertain states, the optimal detector is obtained. Efficiently computable performance metrics that capture the probability of error of the optimal detector are developed. Under simplified model assumptions, closed-form expressions for these metrics are derived, and these lead to a mixed integer convex programming problem for optimizing sensor locations. Using convex relaxations, a branch and bound algorithm that finds the globally optimal sensor locations is developed. Significant performance gains from using the optimal detector with the optimal sensor locations are observed from simulations. Furthermore, performance with greedily selected sensor locations is shown to be very close to that with globally optimal sensor locations.

关键词： Joint detection and estimation smart grid outage identification state estimation PMU sensor placement Chernoff bound mixed integer convex programming branch and bound

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Covariance-based hardware selection-part III: Distributed parameter systems

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AICHE JOURNAL 2012年第9期58卷 2705-2713页

作者： Ahmed, Syed K. Peng, Jui-Kun Chmielewski, Donald J. IIT Dept Chem & Biol Engn Chicago IL 60616 USA

In this article, we propose a new method for the selection of control system hardware (sensors and actuators) for distributed parameter systems. The proposed design scheme seeks to minimize the capital cost of hardware while satisfying predefined performance constraints. Within this minimum capital cost framework, three design scenarios will be discussed;the closed-loop full state information actuator selection problem, the open-loop partial state information sensor selection problem, and the closed-loop partial state information simultaneous sensor and actuator selection problem. The proposed method will be illustrated through application to a nonisothermal tubular reactor example. (c) 2011 American Institute of Chemical Engineers AIChE J, 2012

关键词： sensor selection actuator selection process control optimization linear matrix inequalities mixed integer convex programming

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Exact and Heuristic Approaches for Directional Sensor Control

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IEEE SENSORS JOURNAL 2015年第11期15卷 6633-6639页

作者： Mittelmann, Hans D. Salvagnin, Domenico Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA Univ Padua Dept Informat Engn I-35131 Padua Italy

Directional sensors are gaining importance due to applications, including surveillance, detection, and tracking. Such sensors have a limited field of view and a discrete set of directions they can be pointed to. The directional sensor control problem (DSCP) consists in assigning a direction of view to each sensor. The location of the targets is known with uncertainty given by a joint a priori Gaussian distribution, while the sensor locations are known exactly. In this paper, we study the exact and heuristic approaches for the DSCP with the goal of maximizing information gain on the location of a given set of immobile target objects. In particular, we propose an exact mixed integer convex programming (MICP) formulation to be solved by a black-box MICP solver and several metaheuristic approaches based on local search. A computational evaluation shows the very good performance of both methods.

关键词： mixed integer convex programming metaheuristics directional sensors Benders decomposition

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Generating convex polynomial inequalities for mixed 0-1 programs

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JOURNAL OF GLOBAL OPTIMIZATION 2002年第3期24卷 311-332页

作者： Stubbs, RA Mehrotra, S Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA

We develop a method for generating valid convex quadratic inequalities for mixed 0-1 convex programs. We also show how these inequalities can be generated in the linear case by defining cut generation problems using a projection cone. The basic results for quadratic inequalities are extended to generate convex polynomial inequalities.

关键词： mixed integer programming convex programming semidefinite programming mixed integer nonlinear programming mixed integer convex programming

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Chance-constrained Service Restoration for Distribution Networks with Renewables

Chance-constrained Service Restoration for Distribution Netw...

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IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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

ISBN: (纸本)9781538635964

Power systems operation is facing great challenges from natural disasters and cyber-attacks. It is critical but also difficult to enhance the reliability and resilience against extreme events. To better response to inevitable outages or blackouts, service restoration in distribution networks is important to minimize the disastrous impacts of catastrophic events. The increasing penetration of distributed energy resources (DERs) provides new opportunities to expedite the restoration process. However, the coordination with conventional distribution system control devices and the uncertainty and variability of intermittent renewable energy resources requires new operation and control strategies for distribution service restoration (DSR). This paper develops an optimal bottom-up DSR strategy by coordinating DERs with voltage regulators and capacitor banks. The chanceconstrained (CC) programming approach is used to model the probabilistic output limit of solar radiation and PV generation. The Markov's inequality and Latin hypercube sampling techniques are applied to convert and incorporate the chance constraints into the DSR optimization problem. The CC-DSR problem is formulated as a mixed integer convex programming problem, considering various operational cost functions and bidirectional three-phase unbalanced load flow. Simulation results on the modified IEEE 13-node test feeder system demonstrate the effectiveness and flexibility of the bottom-up DSR strategy.

关键词： Chance constrained optimization Distribution service restoration Markov's inequality mixed integer convex programming PV generation uncertainty

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Hub location problem considering spoke links with incentive-dependent capacities

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COMPUTERS & OPERATIONS RESEARCH 2022年 148卷

作者： Zheng, Yujing Ji, Yuxiong Shen, Yu Liu, Bing Du, Yuchuan Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China

This study proposes a hub-and-spoke (HS) network in which the capacity of a spoke link depends on the incentive provided to the transporters. The incentives influence the capacities of the spoke links, which in turn affect the hub selections and flow assignment. A mixed integer convex programming model is developed to optimize the proposed HS network. The model minimizes total transport cost by locating p hubs, routing flows between origin-destination (OD) pairs and determining the incentives on spoke links. A customized outer-approximation algorithm is presented to provide exact solutions to the developed model, which is difficult to solve optimally within acceptable computational times for large-scale instances. An approximation algorithm based on the stepwise linearization method is then developed to provide suboptimal solutions. The developed model and algorithms are validated in computational experiments with different problem scales. The experiments demonstrate that the proposed HS network tends to route the flows on more spoke links, balance the transport costs on the potential paths between OD pairs and serve more demands with lower transport costs, when compared with the HS network with fixed capacities.

关键词： Location Hub-and-spoke network mixed integer convex programming Outer-approximation Stepwise linearization

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A Solution Method for Many-Objective Security-Constrained Unit Commitment Considering Flexibility

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Lin, Shunjiang Wu, Hui Liu, Jie Liu, Wanbin Liu, Yanghua Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China Guangdong Polytech Normal Univ Sch Electromech Engn Guangzhou Peoples R China

With the increasing penetration of wind power into power systems and the random fluctuation of the wind farm (WF) output, system flexibility must be considered in the optimal generation dispatch. Based on the extreme scenarios of the WF output, we proposed a flexibility risk index to evaluate the system flexibility of each time interval. We established a five-objective security-constrained unit commitment (SCUC) model of a power system with WFs, thermal generation plants, battery energy storage stations, and pumped storage hydro stations. The objectives were to minimize the system flexibility risk, total network loss, operation cost, power purchase cost, and pollutant gas emissions. To obtain the Pareto optimal solutions of the model, based on the objective selection and epsilon-constraint methods, we proposed two methods to reduce the dimension of objectives and transformed the five-objective optimization model into a series of three-objective optimization models. Then, we used the normalized normal constraint method to solve the Pareto frontier surface of each three-objective optimization model. We used a color column to represent the value change of the two upper layer objectives and visualized the Pareto frontier of the five-objective SCUC model in the three-dimensional ordinate space. Case studies on the modified IEEE-9 bus system and an actual power grid demonstrated the effectiveness and high computational efficiency of the proposed method.

关键词： flexibility many-objective optimization mixed integer convex programming normalized normal constraint method security-constrained unit commitment epsilon-constraint method

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SOLVING MULTIOBJECTIVE mixed integer convex OPTIMIZATION PROBLEMS

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SIAM JOURNAL ON OPTIMIZATION 2020年第4期30卷 3122-3145页

作者： De Santis, Marianna Eichfelder, Gabriele Niebling, Julia Rocktaeschel, Stefan Sapienza Univ Roma Dept Comp Control & Management Engn Via Ariosto 25 I-00185 Rome Italy Tech Univ Ilmenau Inst Math Po 10 05 65 D-98684 Ilmenau Germany

Multiobjective mixed integer convex optimization refers to mathematical programming problems where more than one convex objective function needs to be optimized simultaneously and some of the variables are constrained to take integer values. We present a branch-and-bound method based on the use of properly defined lower bounds. We do not simply rely on convex relaxations, but we build linear outer approximations of the image set in an adaptive way. We are able to guarantee correctness in terms of detecting both the efficient and the nondominated set of multiobjective mixed integer convex problems according to a prescribed precision. As far as we know, the procedure we present is the first non-scalarization-based deterministic algorithm devised to handle this class of problems. Our numerical experiments show results on biobjective and triobjective mixed integer convex instances.

关键词： multiobjective optimization mixed integer convex programming

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