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

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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Development of a fuzzy-stochastic programming with Green Z-score criterion method for planning water resources systems with a trading mechanism

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2016年第24期23卷 25245-25266页

作者： Zeng, X. T. Huang, G. H. Li, Y. P. Zhang, J. L. Cai, Y. P. Liu, Z. P. Liu, L. R. Capital Univ Econ & Business Beijing 10070 Peoples R China Univ Regina Inst Energy Environm & Sustainable Commun Regina SK S4S 0A2 Canada Beijing Normal Univ Coll Environm Beijing 100875 Peoples R China North China Elect Power Univ MOE Key Lab Reg Energy Syst Optimizat Beijing 102206 Peoples R China

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin's social and economic growth with protecting the region's ecosystems.

关键词： Fuzzy programming two-stage stochastic programming Green Z-score criterion Strategic analysis Trading-mechanism Arid basin

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Energy Rationalization in Water Supply Networks via stochastic programming

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IEEE LATIN AMERICA TRANSACTIONS 2015年第8期13卷 2742-2756页

作者： De La Vega, J. Alem, D. Fac Engn Sorocaba FACENS Sao Paulo Brazil Univ Fed Sao Carlos UFSCar Sao Paulo Brazil

This paper presents a two-stage stochastic programming model applied to energy rationalization in urban networks. The proposed model encompasses decisions regarding collection, transfer and storage of water, while minimizing the electricity costs associated to the pumping operations. To cope with the uncertainty nature of water-demands, we use the scenario-based approach within the two-stage stochastic paradigm. In order to mitigate both the variability of the recourse decisions and the infeasible solutions in the presence of multiple scenarios, we also analyze risk averse and robust policies. Numerical results show that it is possible to improve energy consumption by reducing water collection in critical periods, as well as by carrying out optimal levels of water in reservoirs before critical periods. Moreover, the analysis of EVPI and VSS evidence the importance of using the stochastic model over simpler expected approaches.

关键词： Energy rationalization Urban Networks stochastic Water-Demand two-stage stochastic programming Robustness Analysis

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stochastic programming approach for unidirectional quay crane scheduling problem with uncertainty

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JOURNAL OF SCHEDULING 2021年第2期24卷 137-174页

作者： Ma, Shoufeng Li, Hongming Zhu, Ning Fu, Chenyi Tianjin Univ Inst Syst Engn Coll Management & Econ Tianjin 300072 Peoples R China

Quay crane scheduling is a key aspect of container terminal operation, which can be regarded as a decision-making process with uncertainty. Each task involves stochastic loading and unloading operation times owing to the existence of uncertainty. In this study, we investigate the unidirectional quay crane scheduling problem for a stochastic processing time, which requires that all the quay cranes move in the same direction either from bow to stern, or vice versa, throughout the planning horizon. The problem is formulated as a two-stage stochastic mixed-integer programming model, where the binary first-stage decision variables correspond to the assignment of tasks to quay cranes, and the mixed-integer second-stage decision variables are related to the generation of detailed schedules. To make the model solvable, we develop an alternative equivalent reformulation with a special structure that involves binary variables in the first stage and continuous variables in the second stage. To solve this reformulated model, an integer L-shaped method is presented for small-size instances, and a simulated annealing algorithm is presented for large-size instances to obtain near-optimal solutions. Numerical experiments show that the integer L-shaped method and simulated annealing algorithm could efficiently solve the unidirectional quay crane scheduling problem with uncertainty. The results also indicate that the stochastic model has distinct advantages in terms of shortening the completion time of vessels and improving the service level of container terminals compared with the expected value problem solutions.

关键词： Unidirectional quay crane scheduling Uncertainty two-stage stochastic programming Integer L-shaped method Simulated annealing algorithm

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Supplier Selection for Agriculture Industry Under Uncertainty: Machine Learning Based Sample Average Approximation Method 2nd

Supplier Selection for Agriculture Industry Under Uncertaint...

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2nd International Conference on Artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024

作者： Yılmaz, Ömer Faruk Yılmaz, Beren Gürsoy Department of Industrial Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Industrial and Systems Engineering University of Florida Gainesville32608 United States

ISBN: (纸本)9783031813689

This paper addresses the problem of supplier selection for a hazelnut producer in the agriculture industry under the uncertainty of product quality, with the objective of cost minimization. To mathematically represent the problem, a two-stage stochastic programming model is developed to make decisions both before and after the realization of uncertainty. The first stage involves supplier selection decisions, while the second stage determines the amount of product received from each supplier. After the hazelnuts are produced, if they do not meet quality standards, customers may return the products, causing significant customer satisfaction issues and increased costs. Product quality is assessed based on four features: (I) Aflatoxin, (II) Acidity, (III) Moisture, and (IV) Sifter. To address this problem within the context of intelligent agriculture, four different scenario reduction techniques are employed: (I) Sample Average Approximation (SAA), (II) SAA combined with Machine Learning-1 (SAA + ML1), (III) SAA + ML2, and (IV) SAA + ML3. Each method utilizes the K-means clustering algorithm with specific problem-related characteristics. Computational results indicate that when the SAA approach is combined with K-means clustering and scenarios are reduced based on clustering results, better outcomes are achieved in reducing costs associated with low-quality products. Specifically, higher numbers of suppliers and higher probabilities of generating scenarios from relevant clusters contribute to improved cost reductions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intelligent Agriculture K-means clustering Sample average approximation two-stage stochastic programming

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Optimization of train schedule with uncertain maintenance plans in high-speed railways: A stochastic programming approach

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2024年 124卷

作者： Ji, Hangyu Wang, Rui Zhang, Chuntian Yin, Jiateng Ma, Lin Yang, Lixing Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beijing Jiaotong Univ Natl Engn Res Ctr Rail Transportat Operat & Contro Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ 3 ShangYuanCun Beijing 100044 Peoples R China

In high-speed railways, unexpected disturbances on maintenance activities may cause serious delays of the scheduled trains and greatly affect the service quality for traveling passengers. In contrast to most existing studies that focused on deterministic maintenance activities, this paper develops a two-stage stochastic programming approach to address the optimization of train schedules under uncertain maintenance plans. Specifically, in the first stage, we aim to determine the departure times of trains from the origin station, since this information needs to be public to passengers in advanced. The objective function is to minimize the expected travel time of trains under uncertain duration time of maintenance activities. In the second stage, given the specific information of maintenance activities, we generate the train schedule by adjusting the stop patterns, train orders and the assignment of tracks at key stations. Due to the computational difficulties arising from the large number of discrete decision variables, we particularly develop a dual decomposition based solution approach to solve the two-stage stochastic model. Our approach decomposes the original problem into a set of scenario-dependent subproblems with much fewer number of variables, which greatly improves the computational efficiency. Finally, we conduct several sets of real-world instances based on the Beijing-Guangzhou high-speed railway corridor to verify the effectiveness of the proposed model and solution approach. The results demonstrate that our approach evidently outperforms state-of-art solvers (Gurobi), especially for large-scale instances that Gurobi cannot even return feasible solutions.

关键词： Train scheduling Maintenance disturbance two-stage stochastic programming Dual decomposition

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A stochastic programming APPROACH FOR SHELTER LOCATION AND EVACUATION PLANNING

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RAIRO-OPERATIONS RESEARCH 2018年第3期52卷 779-805页

作者： Bayram, Vedat Yaman, Hande TED Univ Dept Ind Engn Ankara Turkey Bilkent Univ Dept Ind Engn Ankara Turkey

Shelter location and traffic allocation decisions are critical for an efficient evacuation plan. In this study, we propose a scenario-based two-stage stochastic evacuation planning model that optimally locates shelter sites and that assigns evacuees to nearest shelters and to shortest paths within a tolerance degree to minimize the expected total evacuation time. Our model considers the uncertainty in the evacuation demand and the disruption in the road network and shelter sites. We present a case study for a potential earthquake in Istanbul. We compare the performance of the stochastic programming solutions to solutions based on single scenarios and mean values.

关键词： Disaster management evacuation traffic management shelter location constrained system optimal two-stage stochastic programming second order cone programming

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A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期277卷 699-718页

作者： Bhuiyan, Tanveer Hossain Moseley, Maxwell C. Medal, Hugh R. Rashidi, Eghbal Grala, Robert K. Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Santa Clara Univ Dept Informat Syst & Analyt Santa Clara CA 95053 USA Mississippi State Univ Dept Forestry Mississippi State MS 39762 USA

Reducing the potential damage caused by a wildfire is a problem of significant importance to land and fire managers. Fuel reduction treatment is a well-known method of reducing the risk of fire occurrence and spread on landscapes. However, officials seeking fuel reduction treatments on privately owned lands can only encourage it through incentive programs such as cost-share programs. This research developed a methodology that provides the basis for a decision-making tool to help managers allocate limited cost-share resources among a set of landowners to maximize wildfire risk reduction by implementing a hazardous fuel reduction treatment. A key feature of the methodology is that it incorporates uncertainty in the landowners' decision of whether or not to implement treatment on their lands. The methodology is based on a stochastic programming model with endogenous uncertainty where the probability that a landowner accepts a cost-share offer to implement a fuel reduction treatment on their land depends on the offer amount. To estimate the probability that a landowner accepts a given cost-share offer amount, we used a predictive modeling technique to analyze landowner survey data. The results provide insight about the effects of different cost-share allocation strategies on the expected damage. Numerical experiments show that the risk-based allocation provides up to 37.3% more reduction in damage compared to other strategies that allocate equal cost-share amounts among landowners. Additionally, the results show that the solution quality is substantially sensitive to changes in the number of resource allocation levels. (C) 2019 Elsevier B.V. All rights reserved.

关键词： OR in natural resources two-stage stochastic programming Predictive modeling Monte Carlo simulation Wildfire hazard

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A stochastic programming Approach for Electric Vehicle Charging Network Design

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第5期20卷 1870-1882页

作者： Faridimehr, Sina Venkatachalam, Saravanan Chinnam, Ratna Babu Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA

The advantages of electric vehicles (EVs) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community, considering uncertainties in the arrival and dwell times of vehicles, the state of charge of arriving vehicles' batteries, drivers' walking ranges and charging preferences, demand during weekdays and weekends, and the community's rate of EV adoption. We conducted studies using the sample average approximation method, which asymptotically converges to an optimal solution for a two-stage stochastic problem. However, this method is computationally expensive for large-scale instances. Therefore, we also developed a heuristic to produce nearly optimal solutions quickly for our data instances. We conducted computational experiments using various publicly available data sources and evaluated the benefits of the solutions for a given community, both quantitatively and qualitatively.

关键词： two-stage stochastic programming electric vehicle charging network sample average approximation

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stochastic programming with equilibrium constraints

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2006年第1期128卷 223-243页

作者： Shapiro, A Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

In this paper, we discuss here-and-now type stochastic programs with equilibrium constraints. We give a general formulation of such problems and study their basic properties such as measurability and continuity of the corresponding integrand functions. We discuss also the consistency and rate of convergence of sample average approximations of such stochastic problems.

关键词： equilibrium constraints two-stage stochastic programming variational inequalities complementarity conditions statistical inference exponential rates

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