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A comprehensive stochastic-based adaptive robust model for transmission expansion planning

ELECTRIC POWER SYSTEMS RESEARCH 2024年 234卷

作者： Alnowibet, Khalid A. Alrasheedi, Adel F. Alshamrani, Ahmad M. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

To address various uncertainties appearing in the transmission expansion planning (TEP) problem, appropriate tools such as stochastic programming (SP) or robust optimization (RO) are required. On the other hand, both long-term (LT) and short-term (ST) uncertainties must be well captured to acquire the most reliable and robust solution. The current works model the LT uncertainty by RO and the ST uncertainty by SP, forming a two -stage adaptive robust optimization problem. In contrast, this paper presents a novel approach in which the SP and RO are simultaneously utilized to cope with each type of uncertainty, i.e. , LT and ST uncertainty. Unlike the current methods with bilinear terms appearing in the subproblems, the trilinear terms appear in the objective function of the proposed approach, which are linearized accordingly. In addition, we propose a mixed -integer bilinear programming (MIBP) model to specify the rectangles used as uncertainty sets, which is further solved using Konno ' s algorithm. A standard column -and -constraint (CCG) generation solves the established tri-level structure. Two case studies are used to implement the formulation developed in this work. Results signify the effectiveness of the proposed method.

关键词： Column -and -constraint generation Long and short-term uncertainty Mixed -integer bilinear programming Robust optimization stochastic programming Transmission expansion planning

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Privacy-preserving adaptive traffic signal control in a connected vehicle environment

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 158卷

作者： Tan, Chaopeng Yang, Kaidi Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using partially connected vehicle data. Specifically, we proposed a privacy-preserving mechanism to protect CV data against three types of attacks: CV collusion attacks, database attacks, and inference attacks. The mechanism leverages secure Multi-Party Computation and differential privacy to aggregate individual-level CV data to calculate key traffic parameters without compromising the privacy of CV users. For seamless integration with the privacy-preserving mechanism, we develop a traffic signal optimization model and an arrival rate estimator relying only on aggregated CV data, being applied to both undersaturated and oversaturated traffic conditions. The optimization model is further extended to a stochastic programming problem to explicitly handle the noises added by the privacy-preserving mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy.

关键词： Traffic signal control Privacy Connected vehicles Secure multi-party computation Differential privacy stochastic programming

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A Unifying Framework for the Capacitated Vehicle Routing Problem Under Risk and Ambiguity

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OPERATIONS RESEARCH 2024年第2期72卷 425页

作者： Ghosal, Shubhechyya Ho, Chin Pang Wiesemann, Wolfram Imperial Coll London Business Sch Imperial Coll London SW7 2AZ England City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

We propose a generic model for the capacitated vehicle routing problem (CVRP) under demand uncertainty. By combining risk measures, satisficing measures, or disutility functions with complete or partial characterizations of the probability distribution governing the demands, our formulation bridges the popular but often independently studied paradigms of stochastic programming and distributionally robust optimization. We characterize when an uncertainty-affected CVRP is (not) amenable to a solution via a popular branch-and-cut scheme, and we elucidate how this solvability relates to the interplay between the employed decision criterion and the available description of the uncertainty. Our framework offers a unified treatment of several CVRP variants from the recent literature, such as formulations that optimize the requirements violation or the essential riskiness indices, and it, at the same time, allows us to study new problem variants, such as formulations that optimize the worst case expected disutility over Wasserstein or phi-divergence ambiguity sets. All of our formulations can be solved by the same branch-and-cut algorithm with only minimal adaptations, which makes them attractive for practical implementations.

关键词： capacitated vehicle routing problem stochastic programming distributionally robust optimization branch-and-cut

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Mitigating long-term financial risk for large customers via a hybrid procurement strategy considering power purchase agreements

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ENERGY 2024年 295卷

作者： Yang, Haolin Xu, Siqi Gao, Weijun Wang, Yafei Li, You Wei, Xindong Qingdao Univ Technol iSMART Qingdao 266033 Peoples R China Univ Kitakyushu Fac Environm Engn Kitakyushu 8080135 Japan Beijing Normal Univ Coll Water Sci Beijing 100085 Peoples R China Ritsumeikan Univ Asia Japan Res Inst Ibaraki Japan Jilin Jianzhu Univ Sch Int Exchange Changchun 130118 Peoples R China Zhejiang Univ Sci & Technol Sch Civil Engn & Architecture Hangzhou 310023 Peoples R China

In facing urgent climate issues, large electricity customers committed to the RE100 initiative, aiming to transition entirely to renewable energy sources (RES). However, they encounter significant challenges in managing the unpredictability of RES generation and the volatility of market prices. This study unveils a groundbreaking hybrid procurement model that integrates Power Purchase Agreements (PPAs) with Battery Energy Storage Systems (BESS) to mitigate these financial risks through a novel method. Employing a sophisticated Mixed Integer Linear programming (MILP) model alongside an innovative deep learning forecast for long-term PPAs planning, we present a unique solution that significantly boosts financial returns and enhances risk mitigation for large electricity customers. Validated with real-world data across three distinct customer profiles, our model demonstrates a notable increase in expected Net Present Value (NPV) by up to 13.58% compared to traditional strategies and improved earnings stability under adverse market conditions. Our proposed study not only charts a path toward more effective long-term RES procurement strategies but also provides large electricity customers with a strategic framework to skillfully navigate the complexities of the electricity market in alignment with their sustainability commitments.

关键词： Renewable energy sources Power purchase agreements stochastic programming Risk -averse optimal operational strategy Deep learning networks

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Distributed Computing and Networking Coordination for Task Offloading Under Uncertainties

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 5280-5294页

作者： Xia, Shichao Yao, Zhixiu Li, Yun Xing, Zhitong Mao, Shiwen Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

The multi-access edge computing (MEC) and ultra-dense network (UDN) are regarded as essential and complementary technologies in the age of Internet of Things (IoT). Deploying MEC servers at the macro-cell and small-cell stations can significantly improve user experience as well as increase network capacity. Nevertheless, there still remain many obstacles in practical MEC-enabled UDNs. Among them, a unique challenge is how to coordinate computing and networking to fit the diverse offloading demands of IoT applications in dynamic network environments. To this end, this paper first investigates a distributed delay-constrained computation offloading methodology based on computing and networking coordination in the UDN. An extended game-theoretic approach based on the Lyapunov optimization theory is designed to achieve adaptive task offloading and computing power management in time-varying environments. Furthermore, considering the uncertainty in users' mobility and limited edge resources, distributed two-stage and multi-stage stochastic programming algorithms under various uncertainties are proposed. The proposed algorithms take posterior recourse actions to compensate for inaccurate predicted network information. Extensive simulations validate the effectiveness and rationality of the proposed algorithms and their superior performance over several benchmark schemes.

关键词： Task analysis Servers Optimization Delays Resource management Internet of Things stochastic processes Multi-access edge computing (MEC) ultra-dense network (UDN) distributed game stochastic programming uncertainty

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Computation of multi-objective two-stage fuzzy probabilistic programming problem

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SOFT COMPUTING 2022年第1期26卷 271-282页

作者： Ranarahu, Narmada Dash, J. K. Siksha O Anusandhan Deemed Univ Dept Math Inst Tech Educ & Res Khandagiri Sq Bhubaneswar Odisha India

This paper presents a mathematical model that combines the mathematical models of stochastic programming (SP), namely two-stage stochastic (TSS) programming and chance-constrained programming in fuzzy environment. The complexity of the proposed model is due to multiple objective functions and presence of fuzzy random variables. Since it is difficult to solve the proposed model directly, the mathematical model is converted into an equivalent multi-objective TSS programming crisp model using alpha-cut technique. Then, using the concept of epsilon-constraint method, multi-objective deterministic TSS programming problem is converted into single objective deterministic mathematical model. The transformed model is solved using the existing methodology. Lastly, a numerical example is provided for illustrating the methodology.

关键词： stochastic programming Multi-objective programming epsilon-Constraint method Fuzzy random variable Two-stage programming problem

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Coordinated day-ahead reactive power dispatch in distribution system considering spatial-temporal correlation of wind power

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 41卷

作者： Xie, Yunyun Cai, Sheng Qin, Xiaohui Wu, Hao Zhou, Qian Zhu, Dandan Wu, Qiuwei Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China State Grid Zhoushan Power Supply Co Zhoushan 316000 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin Peoples R China State Grid Jiangsu Elect Power Co Ltd Res Inst Nanjing 211103 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT8 5BN North Ireland

The increasing penetration level of distributed wind power results in significant fluctuations and poses a great challenge to the voltage security of distribution systems. This paper proposes a coordinated day-ahead reactive power dispatch (RPD) method to improve voltage quality. First, a scenario generation method based on the copula autoregressive moving average (copula-ARMA) model is proposed to describe the spatial-temporal correlation of wind power and capture the fluctuations in wind power more accurately. Based on the constructed scenario set, the day-ahead RPD optimization is formulated as a two-stage stochastic programming model. A novel objective function, which minimizes the maximum voltage deviation over all buses and total power losses during the RPD process, is proposed to improve the voltage stability margin of distribution systems. The proposed RPD method coordinates multiple power sources and voltage regulators, i.e., on-load tap changer, and capacitor banks. Moreover, the location and hourly charging shcedule of mobile energy storage units are also optimized to provide flexible voltage support. The proposed day-ahead RPD method was simulated in the modified IEEE 33-bus distribution system. The results show that, in contrast with a traditional day-ahead RPD model, the proposed method reduces the rate of voltage violation by 14% when unexpected scenarios occur.

关键词： Day-ahead RPD Wind power Spatial - temporal correlation stochastic programming Voltage security

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stochastic green profit-maximizing hub location problem

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第1期75卷 99-121页

作者： Rahmati, Reza Neghabi, Hossein Bashiri, Mahdi Salari, Majid Ferdowsi Univ Mashhad Dept Ind Engn Mashhad Iran Coventry Univ Ctr Business Soc Coventry England

This article proposes a two-stage stochastic profit-maximizing hub location problem (HLP) with uncertain demand. Additionally, the model incorporates several carbon regulations, such as carbon tax policy (CTP), carbon cap-and-trade policy (CCTP), carbon cap policy (CCP), and carbon offset policy (COP). In the proposed models, an enhanced sample average approximation (ESAA) method was used to obtain a suitable number of scenarios. To cluster similar samples, k-means clustering and self-organizing map (SOM) clustering algorithms were embedded in the ESAA. The L-shaped algorithm was employed to solve the model inside the ESAA method more efficiently. The proposed models were analyzed using the well-known Australian Post (AP) data set. Computational experiments showed that all of the carbon regulations could reduce overall carbon emissions. Among carbon policies, CCTP could achieve better economic results for the transportation sector. The results also demonstrated that the SOM clustering algorithm within the ESAA method was superior to both k-means inside ESAA and classical SAA algorithms according to the %gap and standard deviation measures. In addition, the results showed that the L-shaped algorithm performed better than the commercial solver in large-scale instances.

关键词： Location stochastic programming sustainability transport integer programming

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Catastrophe insurance and flexible planning for supply chain disruption management: a stochastic simulation case study

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第4期62卷 1108-1125页

作者： Fan, Yingjie Schwartz, Frank Voss, Stefan Woodruff, David L. L. Leiden Univ Leiden Inst Adv Comp Sci Fac Sci Leiden Netherlands Univ Hamburg Inst InformationSystems Fac Business Adm Hamburg Germany Univ Calif Davis Grad Sch Management Davis CA USA Leiden Univ Leiden Inst Adv Comp Sci Fac Sci NL-2333 CA Leiden Netherlands

Catastrophe-related insurance (e.g. business interruption insurance) is an effective financing tool for global corporations to reduce economic losses caused by high impact events. Flexible operational planning is an often-used tool enabling rapid adjustment of operational plans for reducing catastrophe-related damage costs. The interaction between catastrophe insurance and flexible operations planning has rarely been studied. In this paper, we develop a stochastic programming model for a multi-echelon global supply chain network that we solve to investigate the impact of purchasing catastrophe insurance on supply chain operational planning in a catastrophe-prone environment. Computational simulations are developed for evaluating solution quality and measuring catastrophe-related damage costs. We find that it may be optimal for supply chains to scrap redundant products in catastrophes when customer demand falls below the expected level. Purchasing catastrophe insurance may encourage supply chains to scrap more products, which results in more catastrophe-related damages. From analysing supply chain costs, catastrophe-related damage costs, and operational plans, we find that a higher compensation rate of catastrophe insurance triggers more production activities being planned at the vulnerable node just before the vulnerable time period, especially for low residual value products. Finally, we give managerial insights to help reduce unnecessary damages in catastrophes.

关键词： Supply chain risk management operational plan stochastic programming catastrophe insurance disruption

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Modeling collaborative data service provision around an open source platform under uncertainty with stochastic provision games

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2024年 129卷

作者： Pisciella, Paolo Gaivoronski, Alexei A. Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Alfred Getz vei 3 N-7491 Trondheim Norway

This paper uses concepts taken from Cooperative Game Theory to model the incentives to join forces among a group of agents involved in collaborative provision of a mobile app under uncertainty around an open source platform. Demand uncertainty leads the agents to reach a noncooperative equilibrium by offering low quality apps. This can be avoided by introducing a coordination scheme through a common platform that eliminates the effects of lack of information. Coordination is achieved by providing a revenue sharing scheme enforcing the stability of the collaboration but also defined in a "fair"way, depending on the importance of the resources that each provider supplies to the app. To this aim, we introduce the concept of stochastic Provision Games. . This coordination leads both to higher app quality and improved profitability for the participants.

关键词： stochastic programming Cooperative game theory stochastic provision games Newsvendor problem Collaborative service provision Mobile apps

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