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robust optimization for hurricane preparedness

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2020年 221卷 107464-107464页

作者： Wang, Xinfang (Jocelyn) Paul, Jomon A. Georgia Southern Univ Dept Logist & Supply Chain Management Coll Business Adm Statesboro GA 30460 USA Kennesaw State Univ Dept Econ Finance & Quantitat Anal Kennesaw GA 30144 USA

This paper applies the concept of social cost (i.e., logistic, deprivation, and fatality costs) to analyze the optimal deployment time, location and capacities of stockpiles for Points of Distribution (PODs) in hurricane preparedness. We first propose a single-stage, adaptive robust model to determine the optimal deployment time, given the time-variant characteristics of hurricanes. The model is nested in an optimal stopping-time framework that captures the trade-offs between increasing deployment costs and reduced uncertainty as the hurricane approaches landfall. Once the optimal deployment time has been determined, we then propose a less conservative, two-stage robust optimization model with recourse actions to determine the PODs' location, stockpile capacities and flow. Tested on a case study, results show that 1) a non-adaptive model leads to poor decisions about the optimal deployment time;2) improperly modeled deprivation costs pose significant hidden risks to decisionmakers;3) deprivation costs increase $3-5 for every dollar cut when the available budget is strictly binding;and 4) significant savings in social cost result from the wait-and-see strategy implemented through the two-stage robust model.

关键词： Disaster planning Location-allocation Deprivation costs Adaptive robust optimization two-stage robust optimization Recourse actions

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Multi-Objective robust optimization of Hybrid AC/DC Distribution Networks Considering Flexible Interconnection Devices

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IEEE ACCESS 2021年 9卷 166048-166057页

作者： Wang, Xiaoxue Yang, Wenquan Liang, Dong Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China

Hybrid AC/DC distribution networks with the high-efficiency consumption and high-proportion access of new energy have become a crucial trend for future modern distribution networks. High penetration of renewable energy brings various controllable resources along with uncertainties to hybrid AC/DC distribution networks, making conventional single objective optimization fail to meet optimal operation requirements of flexibility and reliability. In this paper, considering uncertainties of distribution generations and loads, a multi-objective robust optimization model based on various controllable devices is proposed. To increase resource utilization, the proposed method comprehensively and properly models the full variety of possible control means (i.e., flexible distribution switch, voltage source converter, energy storages, et al). Utilizing the abundant control means, a multi-objective optimization which minimizes network losses, voltage deviations and operation cost simultaneously is modeled. Then, based on the multi-objective model, a two-stage robust optimization based on second order cone method and column constraint generation algorithm is proposed to achieve the solution, which can deal with the fluctuation of loads and renewable energy quickly and effectively. Outdoing traditional single objective robust optimization, our multi-objective robust optimization based on various controllable resources can decease the network losses, increase the profits and improve voltage profiles significantly, which obviously improves the safety, flexibility and economy simultaneously. Finally, compared with existing robust optimization, the proposed method is tested in numerous case studies to verify its effectiveness and advantages in a modified IEEE 33-node system.

关键词： optimization Distribution networks Costs Uncertainty Reactive power Voltage control Renewable energy sources Hybrid AC DC distribution networks multi-objective optimization two-stage robust optimization flexible interconnection devices column constraint generation algorithm

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robust planning-operation co-optimization of energy hub considering precise model of batteries’ economic efficiency

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Energy Procedia 2019年 158卷 6496-6501页

作者： Cong Chen Xinwei Shen Qinglai Guo Hongbin Sun Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen People’s Republic of China The Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing People’s Republic of China

The increasing penetration of renewable energy in integrated energy system (IES) makes it necessary to consider the uncertainty and an appropriate investment and operation for energy storage systems (ESS). In this paper, we propose a robust planning-operation co-optimization method for Energy Hub (EH) considering the sizing problem and precise model of batteries’ economic efficiency, in which batteries’ total cost and the lifetime loss cost is mainly considered. The problem is formulated as a two-stage robust optimization model, in which the second-stage problem has integer variables. To solve the problem, dual norms are used to transform the uncertain constraints into robust counterparts, then the problem is rearranged and can be solved by Benders Decomposition. Through case studies, the benefits on the flexibility and economy of EH brought by precise ESS model is analyzed. And the consideration of lifetime factor and robust optimization strengthen the economy and reliability of EH, respectively.

关键词： Energy hubs energy storage systems two-stage robust optimization lifetime of batteries planning-operation co-optimization

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Adjustable robust optimization with Discrete Uncertainty

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INFORMS JOURNAL ON COMPUTING 2024年第1期36卷 1-304, C2页

作者： Lefebvre, Henri Malaguti, Enrico Monaci, Michele Trier Univ Dept Math D-54296 Trier Germany Univ Bologna Dipartimento Ingn Energia Elettr & Informaz Guglie I-40136 Bologna Italy

In this paper, we study adjustable robust optimization (ARO) problems with discrete uncertainty. Under a very general modeling framework, we show that such two-stage robust problems can be exactly reformulated as ARO problems with objective uncertainty only. This reformulation is valid with and without the fixed recourse assumption and is not limited to continuous wait-and-see decision variables unlike most of the existing literature. Additionally, we extend an enumerative algorithm akin to a branch-and-cut scheme for which we study the asymptotic convergence. We discuss how to apply the reformulation on two variants of well-known optimization problems, a facility location problem in which uncertainty may affect the capacity values and a multiple knapsack problem with uncertain weights, and we report extensive computational results demonstrating the effectiveness of the approach.

关键词： two-stage robust optimization discrete uncertainty reformulation branch and cut computational experiments

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Economic Schedule of a Microgrid with Load Flexibility Based on Rolling robust optimization

Economic Schedule of a Microgrid with Load Flexibility Based...

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International Conference on Power System Technology (POWERCON)

作者： Chen, Xianfeng Chen, Qingheng Yuan, Xiaojie He, Yujun Chen, Qixin Huizhou Power Supply Bur Co Ltd Huizhou 516001 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China

ISBN: (纸本)9781538664612

This paper investigates a robust economic schedule scheme for a grid-connected microgrid with high penetration of distributed energy resources (DER) and flexible loads. The schedule scheme seeks to minimize the overall costs of load consumption by controlling the resources of flexibility including an EV fleet, an aggregation of cooling appliances, a battery storage system, and the power exchange with public grid. The demand in the microgrid is first fulfilled by the local DER, and the net demand is balanced through an intraday market with real time pricing. A two-stage robust optimization (RO) formulation is proposed to take account of the uncertainty due to forecast errors of load consumption and renewable energy generation. Instead of the RO approach considering the worst scenario, a weak robust optimization (WRO) is proposed and solved using a modified constraint-and-column generation (C&CG) algorithm. A case study is conducted on a test microgrid to validate the efficiency and robustness of the proposed method.

关键词： Demand response renewable energy microgrid flexible load two-stage robust optimization

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Design of robust Var Reserve Contract for Enhancing Reactive Power Ancillary Service Market Efficiency

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 767-771页

作者： Yunyang Zou Yan Xu School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore

In a deregulated Var market, market power issue is more serious than in an energy market since reactive power cannot be transmitted over long distances. This letter designs a multi-timescale Var market framework, where market power that may arise in the hourly-ahead Var support service market due to system configuration deficiency and market structure flaws can be eliminated by day-ahead contract-based Var reserve service market. Settlement of day-ahead Var reserve contract is formulated as a two-stage robust optimization (TSRO) model considering worst case of uncertainty realization and potential market power that may arise in hourly-ahead market. TSRO with integer recourses is then solved by a new column and constraint generation algorithm. Results show a robust Var reserve contract can fully eliminate market power, and prevent suppliers from manipulating market prices.

关键词： Market efficiency market power reactive power ancillary service two-stage robust optimization Var reserve

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Co-optimization of Supply and Demand Resources for Load Restoration of Distribution System Under Extreme Weather

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IEEE ACCESS 2021年 9卷 122907-122923页

作者： Zhu, Xi Zeng, Bo Li, Yonggang Liu, Jiaomin State Grid Beijing Elect Power Co Econ & Technol Beijing 100055 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

To deal with the issue of the distribution system (DS) resilience enhancement under extreme weather events, this paper presents a new methodological framework for enhancing the load restoration of DS during a hurricane. As distinct from existing studies, this approach integrates mobile emergency generators (MEGs) and prosumer communities (PCs) incorporating combined heat and power (CHP) units, electric boilers (EBs), photovoltaic (PV) sources, and demand response (DR) resources and comprehensively explores the coordination and flexibility of supply-side and demand-side resources to boost the post-disaster DS recovery. The discussed problem is formulated by using a two-stage robust optimization model. In the first stage, the MEGs are pre-positioned prior to the hurricane with the objective of minimizing the outage cost of the load to promote the capability of DS to resist extreme disturbance. The second stage determines the real-time allocation of MEGs, output power of CHP units and EBs, and the power consumption of electric loads after the hurricane to maximize DS's load recovery considering the worst-case of the uncertainty realization. Since the fragility analysis of network elements has an essential impact on the efficacy of the optimal strategy, we introduce the Z-number-based approach to scientifically determine the failure probability of components considering the effect of the aging of components and the credibility of the failure information obtained from the fragility. The effectiveness of the proposed methodology is examined based on an IEEE 123-bus distribution test system, and the obtained results confirm the validity of the proposed approach in actual implementations.

关键词： Cogeneration Meteorology Hurricanes Resilience Renewable energy sources Couplings Resists Extreme weather events resilience demand response two-stage robust optimization Z-number

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Vulnerability Assessment and Reduction for Intermodal Freight Transportation Networks

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 245卷

作者： Wei, Xiaoyang Wang, Mengtong Meng, Qiang ASTAR Inst High Performance Comp Singapore 138632 Singapore Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore

Intermodal freight transportation networks (IFTNs) are critical for global logistics, facilitating the efficient transportation of cargo within containers by integrating diverse transportation modes. However, such integration exposes IFTNs to a variety of disruptions, may leading to degradation in transportation capabilities. Thus, vulnerability assessment and reduction (VA&R) for IFTNs are of practical significance. Despite its importance, this research problem has received little attention in the literature. To fill the research gap, we propose the VA&R problem for IFTNs, considering uncertain disruptions that impact IFTN nodes and links, along with cascading failures and influence spreading. We further build models incorporating two-stage robust optimization to assess and reduce the impact of uncertain disruptions, and build exact solution methods for VA&R utilizing columnand-constraint generation and speedup strategies to identify non-conservative solutions. The effectiveness and efficiency of our modeling and solution methods are demonstrated by comparing them to benchmark modeling and solution methods, through numerical experiments for a real-world international IFTN. Furthermore, managerial insights and recommendations for IFTN operations are achieved based on comprehensive sensitivity analyses to address uncertain disruptions in IFTNs.

关键词： Intermodal freight transportation network Vulnerability assessment Vulnerability reduction Max-min optimization two-stage robust optimization Column-and-constraint generation

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robust Energy and Reserve Scheduling Under Wind Uncertainty Considering Fast-Acting Generators

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第4期10卷 2142-2151页

作者： Cobos, Noemi G. Arroyo, Jose M. Alguacil, Natalia Street, Alexandre Univ Castilla La Mancha ETSI Ind Dept Ingn Elect Elect Automat & Comunicac E-13071 Ciudad Real Spain Pontifical Catholic Univ Rio de Janeiro Elect Engn Dept BR-22451045 Rio De Janeiro Brazil

The large-scale integration of wind power generation has significantly boosted the role played by conventional fast-acting generating units in power system operation. Due to their short response time, these relatively expensive devices are particularly suited to cope with the intermittency and volatility featured by uncertain wind-based generation. Within the context of network-constrained generation scheduling underwind-related uncertainty, this paper presents a new robust approach wherein the operation of both slow-and fast-acting generating units is precisely characterized in a day-ahead co-optimized energy and reserve market. As salient modeling features, the ramping-related effect of dispatch nonanticipativity and nonspinning reserve offers are both accounted for. The resulting robust counterpart is formulated as a challenging trilevel program with lower-level binary variables that is effectively solved by an exact nested column-and-constraint generation algorithm. Numerical results including a practical out-of-sample validation procedure demonstrate the operational advantages and the computational effectiveness of the proposed approach.

关键词： Energy and reserve scheduling fast-acting generating units nonanticipativity nonspinning reserve offers ramping capability two-stage robust optimization wind uncertainty

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Reliable dynamic wireless charging infrastructure deployment problem for public transport services

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期313卷 747-766页

作者： Wang, Yun Zhou, Yu Yan, Xuedong Beijing Jiaotong Univ Sch Traff & Transportat MOT Key Lab Transport Ind Big Data Applicat Techno Beijing 100044 Peoples R China Beihang Univ Key Lab lntelligent Transportat Technol & Syst Minist Educ Beijing 100191 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China

Dynamic wireless power transfer (DWPT) technology offers the promise of eliminating the limited driving range of electric buses (EBs), providing greater convenience and safety benefits through the ability to charge EBs in motion. It may be possible to effectively circumvent the drawbacks of EBs such that it becomes mainstream in the future EB market. In this paper, we study the robust DWPT deployment problem for an EB system, aiming to determine the DWPT facility deployment, battery size, and en route charging schedule simultaneously. We first formulate the deterministic problem as a novel mixed integer linear programming (MILP) model. Next, we consider the energy consumption uncertainty and travel time uncertainty to develop a two-stage robust model. We further analyze the structural properties of our proposed model, and implement the column-and-constraint generation (C&CG) method to solve it optimally. Finally, a series of experiments based on a test bus network and the National University of Singapore internal bus network are carried out to verify the efficiency and effectiveness of our models and algorithms. Some managerial insights are also provided that public transit (PT) operators could use to enhance the efficiency of the EB system in the future.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Electric buses Dynamic wireless power transfer Charging facility deployment two-stage robust optimization Column-and-constraint generation

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