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Multi-timescale risk-averse restoration for interdependent water-power networks with joint reconfiguration and diverse uncertainties

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Yang, Yesen Li, Zhengmao Lo, Edmond Y. Nanyang Technol Univ Inst Catastrophe Risk Management Interdisciplinary Grad Programme Singapore Singapore Aalto Univ Dept Elect Engn & Automat Espoo Finland Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore

The growing interdependencies between water and power systems have increased the risk of cascading disruptions and widespread blackouts. Such interdependencies, together with different operational characteristics and multiple uncertainties, introduce additional complexities to service restoration. To address these issues, this paper proposes a coordinated multi-timescale restoration strategy for interdependent water-power networks (IWPNs). First, we model the IWPN as network-based with physical mechanisms, incorporating component-wise interdependencies and varying consumer demands. Features comprising pipe damage (water leakage) and storage as well as renewable generations are modelled to better reflect restoration better. Specifically, the joint reconfigurability of water and power networks is first applied for adjustment of topologies and leverages off backup components by coordinated setting of valves and switches. Then, an updated estimation for multiple uncertainties during restoration is utilized, which offers increasing clarity to support better decision-making. These uncertainties arise from renewable generations and water and power demands. A multi-timescale decision framework is developed to capture these effects and tune restoration measures based on response speeds to facilitate efficient and reliable restoration. Finally, the method is implemented by combining robust optimization and risk-averse stochastic programming and applied to a community-scale test system with 25 water nodes and 33 power buses. The proposed method is compared with five conventional methods with numerical results demonstrating the improvements arising from an interdependent restoration, joint reconfigurability, and multi-timescale optimizations.

关键词： Interdependent water-power systems Restoration Joint reconfiguration Robust optimization risk-averse stochastic programming

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Extended cross decomposition for mixed-integer linear programs with strong and weak linking constraints

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COMPUTERS & CHEMICAL ENGINEERING 2018年 119卷 237-257页

作者： Ogbe, Emmanuel Li, Xiang Queens Univ Dept Chem Engn 19 Div St Kingston ON K7L 3N6 Canada

Large-scale mixed-integer linear programming (MILP) problems, such as those from two-stage stochastic programming, usually have a decomposable structure that can be exploited to design efficient optimization methods. Classical Benders decomposition can solve MILPs with weak linking constraints (which are decomposable when linking variables are fixed) but not strong linking constraints (which are not decomposable even when linking variables are fixed). In this paper, we first propose a new rigorous bilevel decomposition strategy for solving MILPs with strong and weak linking constraints, then extend a recently developed cross decomposition method based on this strategy. We also show how to apply the extended cross decomposition method to two-stage stochastic programming problems with conditional-value-at-risk (CVaR) constraints. In the case studies, we demonstrate the significant computational advantage of the proposed extended cross decomposition method as well as the benefit of including CVaR constraints in stochastic programming. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Cross decomposition Benders decomposition Dantzig-Wolfe decomposition risk-averse stochastic programming Mixed-integer linear programming

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Redundancy Design and Preventive Maintenance for a Load-Sharing Multiasset System Considering Uncertain Environmental Conditions

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9308-9319页

作者： Hao, Yaqian Zhu, Xiaoyan Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci China Mobile Res Inst Ctr Artificial Intelligence & Intelligent Operat R Beijing 100053 Peoples R China

In industry, many systems exhibit load-sharing characteristics. In a load-sharing system, failure of an asset, in addition to affect system reliability, increases the workloads of remaining surviving assets and so their failure rates. When managing such the assets in a system, it is important for decision makers to ensure overall performance of the system, by determining redundancy of assets and a preventive maintenance plan with consideration of load sharing and uncertain environmental conditions. This article proposes an approach for synthetically optimizing redundancy design and age-based preventive maintenance for a load-sharing system with identical assets. A two-stage stochastic programming model with recourse is established, which incorporates risk-aversion preference of decision makers. A decomposition algorithm is developed to solve the joint optimization model, incorporating analytical properties of system failure rate functions and models. A comparative study with deterministic optimization and robust optimization is conducted to demonstrate the advantages of the proposed risk-averse stochastic programming approach. Finally, a numerical study on an effluent treatment system is conducted to analyze the optimal redundancy design and maintenance plan and practical insights.

关键词： Reliability Load modeling Redundancy Costs Planning programming Optimization Load-sharing multiasset k -out-of- n : G system preventive maintenance redundancy design risk-averse stochastic programming uncertain environmental conditions

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risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach

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APPLIED ENERGY 2024年 357卷

作者： Wang, Zhuo Hou, Hui Zhao, Bo Zhang, Leiqi Shi, Ying Xie, Changjun Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Wuhan Univ Technol Shenzhen Res Inst Shenzhen 518000 Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Peoples R China

The increasing penetration of renewable energy with stochastic characteristics and continuous refinement of carbon emission policies put forward higher requirements for the construction of new power systems. This paper proposes a risk-averse stochastic capacity planning and peer-to-peer (P2P) trading collaborative optimization method for multi-energy microgrids (MEMGs) considering carbon emission limitations. First, a cooperative operation model for MEMGs considering the capacity planning of distributed generation units, uncertainty from renewable energy generations, carbon emission limitations and P2P electricity trading among MEMGs is formulated. Second, a risk-averse stochastic programming method is applied to avoid the potential risk losses caused by randomness and intermittence of renewable energy. Third, an asymmetric Nash bargaining approach is adopted to ensure the fair allocation of benefits and maintain the willingness of individual MEMGs to participate in cooperation. Then, to protect the privacy of individual MEMGs belonging to different stakeholders, the alternating direction method of multipliers is used to solve the two subproblems in a distributed manner. Meanwhile, to further alleviate the computation burdens, a diagonal quadratic approximation method is applied to linearize the quadratic penalty term in the augmented Lagrangian function and realize the parallel solution of all optimization subproblems. Moreover, the influence of different carbon emission targets on the optimal resource combination strategy of the system is investigated by introducing carbon emission factors. Simulations on different models, strategies, and distributed algorithms are conducted to verify the effectiveness and superiority of the proposed method.

关键词： Multi-energy microgrids (MEMGs) Peer-to-peer (P2P) trading Asymmetric Nash bargaining risk-averse stochastic programming Carbon emissions

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Decomposition Algorithms and Parallel Computing for Chance-Constrained and stochastic Integer Programs with Applications

Decomposition Algorithms and Parallel Computing for Chance-C...

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作者： Deng, Yan University of Michigan

学位级别：Ph.D.

The focus of this dissertation is to develop solution methods for stochastic programs with binary decisions and risk-averse features such as chance constraint or risk-minimizing objective. We approach these problems through scenario-based reformulations, which are often of intractable scale due to the use of a large number of scenarios to represent the uncertainty. Our goal is to develop specialized decomposition algorithms for solving the problem in reasonable time. We first study a surgery planning problem with uncertainty in surgery durations. A common practice is to first assign operating rooms to surgeries and then to develop schedules. We propose a chance-constrained model that integrates these two steps. A branch-and-cut algorithm is developed, which exploits valid inequalities derived from a bin packing problem and single-machine scheduling problems. We also discuss models and solutions given ambiguous distributional information. Computational results demonstrate the efficacy of the proposed algorithm and provide insights into enhancing performance by the proposed model. Next, we study general chance-constrained 0-1 programs, where decisions made before realization of uncertainty are binary. We develop dual decomposition algorithms that find solutions through bounds and cuts efficiently. We derive a proposition about computing the Lagrangian dual whose application substantially reduces the number of subproblems to solve, and deploy cut aggregation that accelerates the solution of subproblems. We also explore parallel schemes to implement our algorithms in a distributed system. All of them improve the efficacy effectively. We then study dual decomposition for risk-averse stochastic 0-1 programs, which minimize the risk of some random outcome measured by a coherent risk function. Using generic dual representations for coherent risk measures, we derive equivalent risk-neutral minimax reformulations, to which dual decomposition methods apply. We investigate how

关键词： stochastic integer programming Decomposition algorithms Chance-constrained programming risk-averse stochastic programming Parallel computing Thesis

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risk-averse restoration of coupled power and water systems with small pumped-hydro storage and stochastic rooftop renewables

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APPLIED ENERGY 2023年第1期339卷

作者： Yang, Yesen Li, Zhengmao V. Mandapaka, Pradeep Lo, Edmond Y. M. Nanyang Technol Univ Interdisciplinary Grad Programme Singapore 639798 Singapore Nanyang Technol Univ Inst Catastrophe Risk Management Singapore 639798 Singapore Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore

Modern coupled power and water (CPW) systems exhibit increasing integration and interdependence, which challenges system performance to disasters and makes service restoration complex during post-disruption. Meanwhile, new technologies, such as small pumped-hydro storage (PHS) and rooftop renewables, are being widely installed and further deepen the interdependencies. To capture these features and improve overall performance, this paper proposes a coordinated restoration framework for a CPW system to respond to disruptions. The proposed CPW model comprises physical networks and mechanisms, considering available units, such as water desalination/treatment plants, pump stations and small PHS, in the water system, and rooftop renewables, distributed generators, in power system. The interdependencies are modeled through component-wise connections and consumer behavior, then grouped into three phases: production, distribution, and consumption. Aggregate service loss with respect to different consumer loads and time periods, is chosen as performance metric and to be minimized using network reconfiguration, energy/water dispatching, load curtailment, and operation management of components. A two-stage risk-averse stochastic programming is applied for reliable restoration and manage risks, to tackle the uncertainties in renewable power generations and water/power demands that affect method effectiveness. Finally, the method is implemented on a modified 33-bus/25-node CPW system, and the results demonstrate the effectiveness of the proposed restoration framework.

关键词： Restoration Interdependency Critical infrastructure Optimization model Power-water systems risk-averse stochastic programming

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Sample average approximation for risk-averse problems: A virtual power plant scheduling application

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2021年 9卷

作者： Lima, Ricardo M. Conejo, Antonio J. Giraldi, Loic Le Maitre, Olivier Hoteit, Ibrahim Knio, Omar M. KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Ohio State Univ Integrated Syst Engn Elect & Comp Engn Columbus OH USA Ecole Polytech INRIA CNRS Ctr Mathemat Appliquees Palaiseau France KAUST Phys Sci & Engn Div Thuwal Saudi Arabia CEA DES IRESNE DECCadarache F-13108 St Paul Les Durance France

In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.

关键词： Sample average approximation risk-averse stochastic programming Virtual power plant

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