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Analysis of the pavement deterioration uncertainty scenarios on pavement maintenance and rehabilitation planning optimization

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年第3期21卷 507-524页

作者： Fani, Amirhossein Golroo, Amir Fahmani, Mohammadsadegh Naseri, Hamed Nejad, Fereidoon Moghadas Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran

This study aims to compare the results of stochastic and deterministic models and their corresponding optimal M&R actions considering different budget and deterioration rate uncertainty scenarios. Moreover, the impacts of uncertainty on network's scale are investigated. Therefore, this study applies Multi-Stage stochastic programming to model the uncertainty of these parameters in pavement M&R optimization. A new approach is proposed to investigate the effects of different uncertainty cases on the mentioned problem. Moreover, two pavement networks, including a large-scale and a small-scale, are utilized to evaluate the role of network size in the optimal solution to the pavement M&R optimization in the uncertainty conditions. Due to the high complexity of the large-scale M&R problem, progressive hedging algorithm as an effective decomposition technique is applied. Three different uncertainty cases, including low, medium, and high are considered for the deterioration rate. Furthermore, two scenarios are taken into account for the budget: low reduction and high reduction. The results show that the probability of selecting preventive maintenance in the optimal M&R plan is increased by increasing the severity of uncertainty cases. Therefore, preventive maintenance is the most effective pavement treatment to reduce the adverse effects of budget and pavement deterioration uncertainty.

关键词： Maintenance and rehabilitation uncertainty risk pavement deterioration stochastic programming progressive hedging algorithm sensitivity analysis

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Optimal Configuration of Mobile-Stationary Hybrid Energy Storage Considering Seismic Hazards

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ENERGIES 2025年第8期18卷 2052-2052页

作者： Deng, Chengcheng Shen, Xiaodong Tang, Xisheng Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China

The occurrence of extreme disasters, such as seismic hazards, can significantly disrupt transportation and distribution networks (DNs), consequently impacting the post-disaster recovery process. Restoring load using distributed generation represents an important approach to improving the resilience of DNs. However, using these resources to provide resilience is not enough to justify having them installed economically. Therefore, this paper proposes a two-stage stochastic mixed-integer programming (SMIP) model for the configuration of stationary energy storage systems (SESSs) and mobile energy storage systems (MESSs) during earthquakes. The proposed model comprehensively considers both normal and disaster operation scenarios of DNs, maximizing the grid's economic efficiency and security. The first stage is to make decisions about the location and size of energy storage, using a hybrid configuration scheme of second-life batteries (SLBs) for SESSs and fresh batteries for MESSs. In the second stage, the operating costs of DNs are evaluated by minimizing normal operating costs and reducing load loss during seismic events. Additionally, this paper proposes a scenario reduction method based on hierarchical sampling and distance reduction to generate representative fault scenarios under varying earthquake magnitudes. Finally, the progressive hedging algorithm (PHA) is employed to solve the model. The case studies of the IEEE 33-bus and 12-node transportation network are conducted to validate the effectiveness of the proposed method.

关键词： seismic hazards mobile energy storage second-life batteries two-stage stochastic programming progressive hedging algorithm

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A multi-stage stochastic programming model for multi-mission selective maintenance optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 254卷

作者： Ghorbani, Milad Nourelfath, Mustapha Gendreau, Michel Interuniv Res Ctr Enterprise Networks Logist & Tra Quebec City PQ Canada Laval Univ Fac Sci & Engn Dept Mech Engn Quebec City PQ Canada Polytech Sch Montreal Dept Math & Ind Engn Montreal PQ Canada

This research introduces a novel selective maintenance model in the case of systems undergoing multiple consecutive missions. The model considers uncertainties related to future operating conditions during each mission. Within each maintenance break, various optional actions ranging from replacements which are perfect to imperfect and also minimal repairs can be chosen for individual components. Evaluating the probabilities of successful future mission accounts for uncertainties associated with component operational conditions. The selective maintenance problem is formulated as a nonlinear mixed-integer model for optimization, and computational challenges are addressed using the progressive hedging algorithm. Numerical examples validate the new proposed model and illustrate the benefits of the model by estimating a more realistic reliability level and lower maintenance cost.

关键词： Selective maintenance Stochastic programming Uncertain operating conditions progressive hedging algorithm

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A Stochastic Sequential Service Restoration Model for Distribution Systems Considering Microgrid Interconnection

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IEEE TRANSACTIONS ON SMART GRID 2024年第3期15卷 2396-2409页

作者： Cai, Sheng Xie, Yunyun Zhang, Menglin Jin, Xiaolong Wu, Qiuwei Guo, Jian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 30072 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Sequential service restoration (SSR) is essential to restore critical loads after the distribution system outage through gradually forming microgrids (MGs). However, high penetration of renewables induces low-inertia issues, especially in small-scale MGs, which could impede a large-size load restoration for ensuring the MG dynamic stability. To expedite the restoration process, we propose the interconnection of MGs as part of SSR. A developed SSR model is proposed to generate restoration sequence and time of MG interconnection operation. An auxiliary variable is introduced to indicate the system topology before and after MG interconnection, and then the maximum permissive step load in each MG is quantified. By this means, isolated MGs are formed gradually, and adjacent MGs can be reconnected to improve the primary response when picking up a large load. Moreover, to address uncertainties of renewables, the stochastic approach is used. And an improved progressive hedging algorithm (PHA) is utilized to solve the stochastic model. The original model is decomposed by bundles of scenarios, and penalty factors are adaptively updated to accelerate the PHA convergence. Simulation results show the effectiveness of the proposed SSR method in improving the restoration efficiency of distribution systems and reducing computational burdens.

关键词： Load modeling Renewable energy sources Topology Stochastic processes Uncertainty Switches Stability analysis Microgrid interconnection sequential service restoration renewables stochastic programming progressive hedging algorithm

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TWO-STAGE QUADRATIC GAMES UNDER UNCERTAINTY AND THEIR SOLUTION BY progressive hedging algorithmS

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SIAM JOURNAL ON OPTIMIZATION 2019年第3期29卷 1799-1818页

作者： Zhang, Min Sun, Jie Xu, Honglei Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Curtin Univ Sch EECMS Perth WA 6845 Australia

A model of a two-stage N-person noncooperative game under uncertainty is studied, in which at the first stage each player solves a quadratic program parameterized by other players' decisions and then at the second stage the player solves a recourse quadratic program parameterized by the realization of a random vector, the second-stage decisions of other players, and the first-stage decisions of all players. The problem of finding a Nash equilibrium of this game is shown to be equivalent to a stochastic linear complementarity problem. A linearly convergent progressive hedging algorithm is proposed for finding a Nash equilibrium if the resulting complementarity problem is monotone. For the nonmonotone case, it is shown that, as long as the complementarity problem satisfies an additional elicitability condition, the progressive hedging algorithm can be modified to find a local Nash equilibrium at a linear rate. The elicitability condition is reminiscent of the sufficient second-order optimality condition in nonlinear programming. Various numerical experiments indicate that the progressive hedging algorithms are efficient for mid-sized problems. In particular, the numerical results include a comparison with the best response method that is commonly adopted in the literature.

关键词： multistage noncooperative game under uncertainty progressive hedging algorithm stochastic linear complementarity problem stochastic variational inequality

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On Multistage Pseudomonotone Stochastic Variational Inequalities

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2023年第1期199卷 363-391页

作者： Cui, Xingbang Sun, Jie Zhang, Liping Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Natl Univ Singapore Singapore 119245 Singapore Curtin Univ Perth WA 6845 Australia

This paper focuses on the solvability of multistage pseudomonotone stochastic variational inequalities (SVIs). On the one hand, some known solvability results of pseudomonotone deterministic variational inequalities cannot be directly extended to multistage pseudomonotone SVIs, so we construct the isomorphism between both and then establish theoretical results on the existence, convexity, boundedness and compactness of the solution set for multistage pseudomonotone SVIs via such an isomorphism. On the other hand, there does not exist a special algorithm for solving the multistage pseudomonotone SVIs so far, so we propose some sufficient conditions on the elicitability of multistage pseudomonotone SVIs, which opens the door for applying Rockafellar's elicited progressive hedging algorithm to solve such SVIs. Numerical results on solving a two-stage stochastic market optimization problem and randomly generated two-stage pseudomonotone linear complementarity problems are presented.

关键词： Pseudomonotonicity Multistage stochastic optimization Stochastic variational inequalities progressive hedging algorithm Elicited monotonicity

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Collaborative stochastic expansion planning of cyber-physical system considering extreme scenarios

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第10期17卷 2419-2434页

作者： Zhang, Yiwei Li, Chengze Wan, Haiyang Shi, Qingxin Liu, Wenxia Xu, Yanhui North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

In this paper, a collaborative stochastic expansion planning model of cyber-physical system (CPS) with resilience constraints is proposed. The model can reduce the coupling risk and enhance the resilience under extreme scenarios, from the perspective of the structural/functional coupling between the physical and cyber systems. The model is to collaboratively optimize expansion transmission line siting, expansion communication fibre siting, and service routing distribution to minimize total investment cost. Constraints are divided into conventional constraints and resilience constraints. In the resilience constraints, the impact of cyber failures on the dispatch strategy is analyzed, and the load shedding in stochastic scenarios is limited to guarantee the resilience of the planning scheme. Particularly, to reach a stable solution for the stochastic planning, a mixed scenario set is proposed based on the probabilistic typhoon model and the complex network theory. Finally, the model is solved by the progressive hedging (PH) algorithm. The case studies of the IEEE RTS-79 test system demonstrate that, compared with the traditional independent planning model, the model proposed in this paper performs better in limiting load loss and enhancing resilience under extreme scenarios.

关键词： collaborative planning cyber physical system progressive hedging algorithm resilience stochastic planning cyber-physical systems power system planning stochastic programming

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Strategic reliable supply chain network design: determining tradeoffs between cost and risk

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第11期61卷 3621-3633页

作者： Meng, Lingpeng He, Junliang Hu, Shaolong Han, Chuanfeng Shanghai Maritime Univ China Inst FTZ Supply Chain Room 427Container Supply Chain Bldg Shanghai Peoples R China Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China Tongji Univ Sch Econ & Management Shanghai Peoples R China

The design of a supply chain network (SCN) should address two conflicting objectives, i.e. inexpensive yet reliable operation. This paper presents models to choose supply facilities and determine distribution options while considering the expected risk of not serving customers attributed to supply shortages. The goal is to design a reliable network with a lower cost under traditional cost objectives. We formulate a stochastic mixed-integer biobjective programming model considering the protection of facilities and solve this problem with a progressive hedging algorithm (PHA). We examine the tradeoff curve between the cost and risk through a case study involving Shanghai in China. The results indicate that substantial improvements in reliability can be reached with a limited increase in the total cost.

关键词： Supply chain network disruption stochastic mixed-integer programming multiobjective optimisation progressive hedging algorithm

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On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2023年第1期86卷 117-161页

作者： Ding, Kuang-Yu Lam, Xin-Yee Toh, Kim-Chuan Natl Univ Singapore Dept Math 10 Lower Kent Ridge Rd Singapore 119076 Singapore Natl Univ Singapore Inst Operat Res & Analyt 3 Res Link Singapore 117602 Singapore

We design inexact proximal augmented Lagrangian based decomposition methods for convex composite programming problems with dual block-angular structures. Our methods are particularly well suited for convex quadratic programming problems arising from stochastic programming models. The algorithmic framework is based on the application of the abstract inexact proximal ADMM framework developed in [Chen, Sun, Toh, Math. Prog. 161:237-270] to the dual of the target problem, as well as the application of the recently developed symmetric Gauss-Seidel decomposition theorem for solving a proximal multi-block convex composite quadratic programming problem. The key issues in our algorithmic design are firstly in designing appropriate proximal terms to decompose the computation of the dual variable blocks of the target problem to make the subproblems in each iteration easier to solve, and secondly to develop novel numerical schemes to solve the decomposed subproblems efficiently. Our inexact augmented Lagrangian based decomposition methods have guaranteed convergence. We present an application of the proposed algorithms to the doubly nonnegative relaxations of uncapacitated facility location problems, as well as to two-stage stochastic optimization problems. We conduct numerous numerical experiments to evaluate the performance of our method against state-of-the-art solvers such as Gurobi and MOSEK. Moreover, our proposed algorithms also compare favourably to the well-known progressive hedging algorithm of Rockafellar and Wets.

关键词： Dual block angular structure progressive hedging algorithm Symmetric Gauss-Seidel decomposition ADMM Proximal augmented Lagrangian method Convex composite programming

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Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 883-893页

作者： Weixin Zhang Bo Hu Kaigui Xie Changzheng Shao Tao Niu Jiahao Yan Lvbin Peng Maosen Cao Yue Sun State Key Laboratory of Power Transmission Equipment&System Security and New Technology at Chongqing University Chongqing 400044China

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind ***,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power *** paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control *** problem is formulated as a two-stage stochastic mixed-integer programming *** first stage arranges the maintenance periods of transmission *** second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed *** proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both ***,the progressive hedging algorithm is *** results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.

关键词： Mixed-integer linear programming network topology optimization progressive hedging algorithm stochastic optimization transmission maintenance scheduling wind curtailment

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