Article
Engineering, Electrical & Electronic
Bining Zhao, Jesse Bukenberger, Mort Webster
Summary: A multi-stage and multi-scale stochastic generation expansion planning (GEP) model is proposed to represent uncertainties in load and renewable generation. The study finds that scenario partitioning methods are more effective in determining appropriate investment levels, while covariance-based approximations perform the best overall.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Sanjula Kammammettu, Zukui Li
Summary: Scenario-based stochastic programming is widely used for optimization under uncertainty. This study focuses on optimal transport and proposes algorithms for scenario reduction and multistage scenario tree generation using entropy-regularized optimal transport. The use of the Sinkhorn-Knopp algorithm decreases solution time and memory burden. The proposed approach generates high-quality scenarios for more accurate solutions in stochastic programming.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Tohid Akbari, Saeed Zolfaghari Moghaddam
Summary: This paper proposes a new mathematical framework for distribution expansion planning (DEP), which models the uncertainties associated with electric demand and wind production using plausible ellipsoidal uncertainty sets. A hybrid model combining stochastic programming and robust optimization is constructed to tackle the problem. Numerical simulations demonstrate the superiority of the hybrid model compared to existing ones.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Thermodynamics
Elena Raycheva, Blazhe Gjorgiev, Gabriela Hug, Giovanni Sansavini, Christian Schaffner
Summary: Power systems globally are undergoing changes due to the rapid transformation of the generation mix. These changes in the power system configuration may increase the risk of failures and lower security of supply. To ensure secure development while considering costs, it is necessary to have adequate tools for assessing and preparing for future energy transition scenarios. This paper presents a risk-informed approach for generation and transmission expansion planning that integrates cost-based planning with risk-based transmission expansion planning, resulting in cost-effective solutions that guarantee system security and account for the risk implications of changes.
Article
Thermodynamics
Kedi Zheng, Huiyao Chen, Yi Wang, Qixin Chen
Summary: This paper proposes a data-driven framework to solve the financial transmission right (FTR) portfolio construction problem by using k-means clustering and quantile regression to predict price distributions. The method is tested on real market data and shows steady performance in node selection and price scenario generation, outperforming other methods.
Article
Computer Science, Software Engineering
Jamie Fairbrother, Amanda Turner, Stein W. Wallace
Summary: Scenario generation is the process of constructing a discrete random vector to represent uncertain parameters in stochastic programming. Most methods are distribution-driven, but this paper proposes an analytic approach that is problem-driven and can better represent tail risk.
MATHEMATICAL PROGRAMMING
(2022)
Article
Computer Science, Interdisciplinary Applications
Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen
Summary: This paper presents a method for generating scenarios using forecast information, particularly in the context of wind power generation. The method utilizes normalizing flows and tailors the scenarios to specific days using wind speed forecasts. The generated scenarios are applied to a stochastic day-ahead bidding problem and are found to yield profitable decisions, with the normalizing flow method consistently achieving the highest profits.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Energy & Fuels
Stian Backe, Mohammadreza Ahang, Asgeir Tomasgard
Summary: This paper examines the importance of including short-term stochastic operational scenarios in long-term capacity expansion models with high shares of variable renewables, evaluating different sampling-based scenario generation routines. The results show that stochastic modeling with over 80% variable renewables leads to more investments in both dispatchable and variable renewable capacity, compared to deterministic modeling.
Article
Computer Science, Software Engineering
Andres Ramos, Erik Quispe, Sara Lumbreras
Summary: OpenTEPES is an open-access tool designed to determine investment plans for new power facilities to meet future demand at minimum cost. It considers planning decisions across different time scopes and multiple criteria, and has been applied flexibly in European planning projects.
Article
Green & Sustainable Science & Technology
Jinfan Chen, Chengxiong Mao, Zhe Liu, Chunyan Ma, Guanglin Sha, Qing Duan, Hua Fan, Shushan Qiu, Dan Wang
Summary: The main goal of future integrated energy service providers (IESP) is to achieve profitability and reduce carbon emissions while ensuring sufficient energy supply. However, there are various challenges in planning the integrated energy system (IES) based on energy hubs (EH) due to uncertainties with renewable energy resources and multi-energy consumers. This study develops a mathematical model considering device temperature limitations to optimize the IES planning.
IET RENEWABLE POWER GENERATION
(2023)
Article
Thermodynamics
Pernille Seljom, Lisa Kvalbein, Lars Hellemo, Michal Kaut, Miguel Munoz Ortiz
Summary: This paper presents a method using stochastic programming to model variable renewable electricity generation in long-term energy system models, demonstrated on a Norwegian TIMES model. The research shows that the number of stable scenarios required varies between different scenario generation methods, and the generated scenarios have different errors compared to the actual dataset.
Article
Engineering, Multidisciplinary
Tushar Rathi, Rishabh Gupta, Jose M. M. Pinto, Qi Zhang
Summary: Stochastic programming is a well-studied framework for modeling optimization problems under uncertainty. However, its solutions are often difficult to understand and not trusted by users, which hinders its adoption in industrial practice. This work proposes scenario and recourse reduction methods to enhance the explainability of stochastic programming solutions.
OPTIMIZATION AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
R. Dominguez, M. Carrion, A. J. Conejo
Summary: The article investigates the capacity expansion problem of renewable sources as a long-term multistage decision-making issue, comparing four different approaches. Through case studies and computational models, the impact of long-term uncertainty factors is explored, analyzing the pros and cons of each method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Energy & Fuels
Spyridon Chapaloglou, Damiano Varagnolo, Francesco Marra, Elisabetta Tedeschi
Summary: This study addresses the problem of generating uncertainty scenarios for energy storage sizing in isolated power systems. It proposes a data-driven scenarios selection strategy that learns the distribution of uncertainties and generates an optimal set of scenarios, mitigating computational issues and ensuring optimal solutions.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Management
Chandra Ade Irawan, Dylan Jones, Peter S. Hofman, Lina Zhang
Summary: This paper proposes a combination of two optimization models for strategic energy planning at national and regional levels. The first model determines the electricity production configuration in the future based on available generation sources, while the second model designs a generation expansion plan considering uncertain parameters over the time horizon. The practical use of these models has been evaluated in China's electricity generation system.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Energy & Fuels
Didem Sari, Youngrok Lee, Sarah Ryan, David Woodruff
Article
Energy & Fuels
Yonghan Feng, Sarah M. Ryan
Article
Management
Esmaeil Keyvanshokooh, Sarah M. Ryan, Elnaz Kabir
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2016)
Article
Operations Research & Management Science
Ali Haddad-Sisakht, Sarah M. Ryan
ANNALS OF OPERATIONS RESEARCH
(2018)
Article
Engineering, Electrical & Electronic
Anmar Arif, Shanshan Ma, Zhaoyu Wang, Jianhui Wang, Sarah M. Ryan, Chen Chen
IEEE TRANSACTIONS ON POWER SYSTEMS
(2018)
Article
Engineering, Industrial
Ali Haddadsisakht, Sarah M. Ryan
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2018)
Article
Engineering, Electrical & Electronic
Dan Hu, Sarah M. Ryan
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2019)
Article
Computer Science, Interdisciplinary Applications
Gorkem Emirhuseyinoglu, Sarah M. Ryan
ENVIRONMENTAL MODELLING & SOFTWARE
(2020)
Article
Business, Finance
Xiaoshi Guo, Sarah M. Ryan
Summary: Stochastic programming models for portfolio optimization rely on scenario paths for returns derived from stochastic process models. This paper investigates a variant of the geometric Brownian motion process for stock index returns that incorporates index momentum. Based on this model, three different processes for generating scenarios on a rolling basis are devised, which differ according to how frequently the momentum parameter is updated and whether it is estimated according to a simple moving average or an exponentially weighted moving average of returns.
INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS
(2021)
Article
Energy & Fuels
Dan Hu, Sarah M. Ryan
ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS
(2020)
Article
Social Sciences, Mathematical Methods
Didem Sari Ay, Sarah M. Ryan
COMPUTATIONAL MANAGEMENT SCIENCE
(2019)
Article
Energy & Fuels
Narges Kazemzadeh, Sarah M. Ryan, Mahdi Hamzeei
ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS
(2019)
Proceedings Paper
Energy & Fuels
Sarah M. Ryan, Jinchi Li, Narges Kazemzadeh
2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)
(2018)
Article
Energy & Fuels
Didem Sari, Sarah M. Ryan
ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS
(2018)
Proceedings Paper
Energy & Fuels
Dan Hu, Sarah M. Ryan
2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING
(2017)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)