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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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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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Sequential Clearing of Network-Aware Local Energy and Flexibility Markets in Community-Based Grids

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IEEE TRANSACTIONS ON SMART GRID 2024年第1期15卷 405-417页

作者： Talari, Saber Birk, Sascha Ketter, Wolfgang Schneiders, Thorsten Univ Cologne Fac Management Econ & Social Sci D-50923 Cologne Germany Univ Appl Sci Cologne Cologne Inst Renewable Energy D-50679 Cologne Germany

In this paper, network-aware clearing algorithms for local energy markets (LEMs) and local flexibility markets (LFM) are proposed to be sequentially run and coordinate assets and flexible resources of energy communities (ECs) in distribution networks. In the proposed LEM clearing algorithm, EC managers run a two-stage stochastic programming while considering random events by scenario generation and network constraints using linearized DistFlow. As one of outcomes, maximum available up- and down-regulations provided by ECs are estimated in LEM and communicated to LFM. In the distributed LFM clearing algorithm, an iterative auction is designed using a dual-decomposition technique (Augmented Lagrangian) which is solved by consensus alternating direction method of multipliers. The LFM algorithm efficiently dispatches the flexibility provided by ECs in operating time while considering flexibility local marginal price as pricing method. Network constraints are included in the algorithm with an AC distribution optimal power flow for dynamic network topology in which branches and buses are decomposed to solve the problem in distributed fashion. The designed LFM algorithm can respond to exogenous and endogenous signals for flexibility requests. The simulation results in a test case display effectiveness of two proposed LEM and LFM algorithms for an efficient provision of flexibility.

关键词： Dual-decomposition energy communities flexibility provision iterative auction market design stochastic programming

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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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Design of micromachines under uncertainty with the sample-average approximation method

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2024年第2期18卷 1-14页

作者： Guaracao, Jorge Mario Monsalve Langa, Sergiu Stolz, Michael Mrosk, Andreas Kaiser, Bert Schenk, Harald Fraunhofer IPMS Maria Reiche Str 2 D-01109 Dresden Germany Brandenburg Univ Technol Cottbus Senftenberg Pl De Chair Micro & Nano Syst Pl Deutsch Einheit 1 D-03046 Cottbus Germany

Variability in the features produced by microfabrication processes, as well as uncertainty in some material properties, may cause a significant deviation in the performance of micromachines within the same fabrication run. Based on an estimation of the expected process variations, the design of such devices can be optimised to achieve the design goals, even under this uncertainty. Learning from previous works on the design of microresonators, we formulate this design problem as a case of chance -constrained optimisation and expand it to a general case where both the dynamic sensitivity ought to be maximised and the natural frequency should be close to a given target. Constraints to ensure a safe operation under both static and dynamic conditions are included by means of penalty functions. We implement the 'Sample -Average Approximation' (SAA), known in the field of stochastic programming, to solve the problem with a single -objective genetic algorithm (CMA-ES), requiring only a numerical evaluation of the objective function-no computation of its gradient is required nor a specific analytic form. We apply this optimisation strategy to the design case of an ultrasonic transducer-'lateral CMUT'-, using optical measurements of trench variability to estimate process variations in a hypothetical design. Comparison of different optimisation results reveals that the implementation of SAA enables the choice of a more conservative design that meets the targets in spite of variability in its features.

关键词： Robust design Parameter uncertainty Micro Electro-Mechanical Systems (MEMS) stochastic programming Covariance Matrix Adaptation - Evolution Strategy (CMA-ES)

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PRAGMATIC DISTRIBUTIONALLY ROBUST OPTIMIZATION FOR SIMPLE INTEGER RECOURSE MODELS

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SIAM JOURNAL ON OPTIMIZATION 2024年第2期34卷 1755-1783页

作者： Van Beesten, E. Ruben Romeijnders, Ward Morton, David P. Erasmus Univ Econometr Inst Erasmus Sch Econ NL-3000 DR Rotterdam Netherlands Norwegian Univ Sci & Technol NTNU Dept Ind Econ & Technol Management N-7491 Trondheim Norway Univ Groningen Fac Econ & Business NL-9700 AB Groningen Netherlands Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA

Inspired by its success for their continuous counterparts, the standard approach to deal with mixed -integer recourse (MIR) models under distributional uncertainty is to use distributionally robust optimization (DRO). We argue, however, that this modeling choice is not always justified since DRO techniques are generally computationally challenging when integer decision variables are involved. That is why we propose an alternative approach for dealing with distributional uncertainty for the special case of simple integer recourse (SIR) models, which is aimed at obtaining models with improved computational tractability. We show that such models can be obtained by pragmatically selecting the uncertainty set. Here, we consider uncertainty sets based on the Wasserstein distance and also on generalized moment conditions. We compare our approach with standard DRO both numerically and theoretically. An important side result of our analysis is the derivation of performance guarantees for convex approximations of SIR models. In contrast to the literature, these error bounds are not only valid for a continuous distribution but hold for any distribution.

关键词： stochastic programming distributional uncertainty distributionally robust optimization simple integer recourse convex approximations

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New Exact Algorithm for the integrated train timetabling and rolling stock circulation planning problem with stochastic demand

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期316卷 906-929页

作者： Pan, Hanchuan Yang, Lixing Liang, Zhe Yang, Hai Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Shanghai Univ Engn Sci Sch Urban Rail Transportat Shanghai 201620 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

This paper studies an integrated train timetabling and rolling stock circulation planning problem with stochastic demand and flexible train composition (TRSF). A novel stochastic integer programming model, which is formulated on a space-time underlying network to simultaneously optimize the train timetable and rolling stock circulation plan with flexible train composition, is proposed by explicitly considering the random feature of passenger distribution on an urban rail transit line. To solve this problem efficiently, the proposed model is decomposed into a master problem and a series of sub-problems regarding different stochastic scenarios. We further prove that each sub-problem model is equivalent to its linear programming relaxation problem, by proving that the coefficient matrix of each linear programming relaxation model is totally unimodular. Then, the classical Benders decomposition algorithm is applied to the studied problem. Based on the model characteristics, both single-cut and multi-cut methods with some speed-up techniques are developed to solve the proposed model in a novel and effective way. Numerical experiments are conducted on small-scale cases and large-scale cases derived from Shanghai Metro Line 17, and the results show that solving the stochastic problem can extract gains in efficiency and the value of stochastic solution tends to be high.

关键词： Random passenger demand Flexible train composition Rolling stock circulation stochastic programming

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Improving disaster relief plans for hurricanes with social media

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第10期75卷 1965-1978页

作者： Paul, Jomon A. Zhang, Minjiao Yang, Muer Xu, Chong Kennesaw State Univ Dept Econ Finance & Quantitat Anal Kennesaw GA USA Kennesaw State Univ Kennesaw GA USA Univ St Thomas Minneapolis MN USA Kennesaw State Univ Dept Econ Finance & Quantitat Anal 560 Parliament Garden Way Kennesaw GA 30144 USA

Decisions on humanitarian responses to natural disasters are subject to considerable epistemic uncertainty. This paper advocates for postponing the decision point of pre-positioning relief supplies as close to landfall as possible and searching social media right post-landfall so that the demands can be estimated more accurately. We use a realistic hurricane preparedness case to demonstrate the effectiveness of our models and parametric estimation using social media data. The optimal timing to deploy relief supplies before hurricane landfall is noted to be 12 h in advance, which reduces the total cost by 13% more than if relief supplies are deployed 18+ hours in advance. Meanwhile, utilizing social media information can reduce the total cost as well as all kinds of specific costs being considered, excluding the point of dispensing (POD) sites setup cost, by approximately 15%. As the attitude toward risk goes from optimistic, to neutral, and to pessimistic, the number of PODs increases from 3 to 7, and to 8. A similar pattern can be noted in the total costs incurred by these decision-makers. Further, as the aversion to risk increases, locations tend to be chosen farther from landfall with these farther locations serving the less severe patients.

关键词： Disaster management humanitarian logistics social media epistemic uncertainty stochastic programming practice of OR optimization location

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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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STOCHOS: stochastic opportunistic maintenance scheduling for offshore wind farms

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IISE TRANSACTIONS 2024年第1期56卷 1-15页

作者： Papadopoulos, Petros Coit, David W. Ezzat, Ahmed Aziz Rutgers State Univ Dept Ind & Syst Engn New Brunswick NJ 08901 USA

Despite the promising outlook, the numerous economic and environmental benefits of offshore wind energy are still compromised by its high Operations and Maintenance (O&M) expenditures. On one hand, offshore-specific challenges such as site remoteness, harsh weather, transportation requirements, and production losses, significantly inflate the O&M costs relative to land-based wind farms. On the other hand, the uncertainties in weather conditions, asset degradation, and electricity prices largely constrain the farm operator's ability to identify the time windows at which maintenance is possible, let alone optimal. In response, we propose STOCHOS, short for the stochastic holistic opportunistic scheduler-a maintenance scheduling approach tailored to address the unique challenges and uncertainties in offshore wind farms. Given probabilistic forecasts of key environmental and operational parameters, STOCHOS optimally schedules the offshore maintenance tasks by harnessing the opportunities that arise due to favorable weather conditions, on-site maintenance resources, and maximal operating revenues. STOCHOS is formulated as a two-stage stochastic mixed-integer linear program, which we solve using a scenario-based rolling horizon algorithm that aligns with the industrial practice. Tested on real-world data from the U.S. North Atlantic where several offshore wind farms are in-development, STOCHOS demonstrates considerable improvements relative to prevalent maintenance benchmarks, across various O&M metrics, including total cost, downtime, resource utilization, and maintenance interruptions.

关键词： Maintenance optimization offshore wind energy stochastic programming

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