Article
Management
Andrea Visentin, Steven Prestwich, Roberto Rossi, S. Armagan Tarim
Summary: The study introduces a hybrid approach combining tree search and stochastic dynamic programming to compute (R, s, S) policy parameters. By pruning up to 99.8% of the search tree using a branch-and-bound technique with bounds generated by dynamic programming, the method can solve instances of realistic size in a reasonable time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Mechanical
Wen-Yu He, Yi-Lin Li, Jing-Xian Yi, Wei-Xin Ren
Summary: This paper proposes a time-domain moving load identification method considering the uncertainty in the finite element model. The effectiveness and superiority of the proposed method are verified through numerical and experimental examples, and the influence of various factors is investigated. The outcomes indicate that the method has higher identification accuracy and calculation efficiency compared with other methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Thermodynamics
Hamidreza Hamidpour, Jamshid Aghaei, Sasan Pirouzi, Taher Niknam, Ahmad Nikoobakht, Matti Lehtonen, Miadreza Shafie-khah, Joao P. S. Catalao
Summary: In recent years, the power system has entered a new technological era with increased commitment to wind farms and energy storage systems, requiring disruptive changes to existing power system structures and procedures. This paper proposes a flexible coordinated power system expansion planning model that considers local wind farms, energy storage systems, and incentive-based demand response programs.
Article
Automation & Control Systems
Siyuan Wang, Guangchao Geng, Quanyuan Jiang, Rui Bo
Summary: The article compares discrete storage model (DSM) and continuous storage model (CSM) in power system planning and finds that DSM provides more reasonable storage sizing decisions than CSM. However, when considering DSM in interval optimization, the discrete status variables and temporal coupling pose challenges. To address this, a tailored interval optimization approach is proposed to incorporate both DSM and renewable energy uncertainty in generation expansion planning (GEP). The approach covers all worst cases in a given uncertainty set without requiring iterations and also introduces a bi-interval policy to balance investment cost and system security.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Operations Research & Management Science
Emilio Carrizosa, Frederic Messine
Summary: In this paper, a Branch and Bound algorithm based on interval arithmetic is designed to solve nonconvex robust optimization problems, with a code developed in MatLab for numerical studies. The results show the effectiveness of this approach in providing global solutions for such difficult problems.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Computer Science, Artificial Intelligence
Yiru Wang, Yinlong Liu, Xuechen Li, Chen Wang, Manning Wang, Zhijian Song
Summary: The study reintroduces the concept of simple bounds in interval arithmetic and significantly extends it, demonstrating its practicality. By detailed derivation, practical methods are proposed to solve robust model fitting problems, and practical validations are conducted in high-dimensional problems.
PATTERN RECOGNITION
(2021)
Article
Engineering, Electrical & Electronic
Mohammed El-Meligy, Ahmed M. El-Sherbeeny, Amjad Anvari-Moghaddam
Summary: This paper proposes an adaptive robust optimization framework for addressing the issue of uncertain resistance in electricity network planning. The uncertain resistance is modeled using ellipsoidal uncertainty set, and a data-driven selection method for the ellipsoidal uncertainty set is proposed. A case study demonstrates the effectiveness of the proposed method.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Information Systems
Reinaldo T. Zoppei, Marcos A. J. Delgado, Leonardo H. Macedo, Marcos J. Rider, Ruben Romero
Summary: The article discusses the limitations of using traditional BB algorithm for non-convex MINLP problems and proposes an efficient BB algorithm tailored to address these issues, with promising performance shown in experiments.
Article
Computer Science, Software Engineering
Amitabh Basu, Michele Conforti, Marco Di Summa, Hongyi Jiang
Summary: This study investigates the theoretical complexity of branch-and-bound (BB) and cutting plane (CP) algorithms for mixed-integer optimization. The research finds that for convex 0/1 problems, CP performs at least as well as BB, while for other types of problems, the advantage of CP over BB disappears. However, when the dimension is considered a fixed constant, BB performs at least as well as CP.
MATHEMATICAL PROGRAMMING
(2023)
Article
Acoustics
Murat Kara, Neil S. Ferguson
Summary: Polynomial Chaos Expansion (PCE) is a method for analyzing uncertain vibratory structures with lower computational effort by curve fitting with orthogonal basis terms. However, high polynomial order is required in PCE to accurately estimate statistical moments of the frequency response function in resonance regions of lightly damped and uncertain structures. Different transformation techniques have been reported to address this issue, but they introduce additional mathematical operations and may require high order polynomials again for accuracy.
JOURNAL OF SOUND AND VIBRATION
(2023)
Review
Green & Sustainable Science & Technology
Abdollah Rastgou
Summary: This study provides a comprehensive investigation of the distribution network expansion planning (DNEP) problem, examining various aspects such as objective functions, constraints, design variables, and addressing issues like uncertainty, distributed generation, and storage. Future research directions, including conflict resolution and solving approaches, are also suggested.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2024)
Article
Computer Science, Interdisciplinary Applications
Claudio Contardo, Andrea Lodi, Andrea Tramontani
Summary: In this paper, it is argued that the standard approach of using very little cutting plane generation in the branch-and-bound tree by modern mixed-integer linear programming solvers is too conservative and results in missed opportunities. The authors support their argument with computational investigation on instances from the MIPlib 2010 collection.
INFORMS JOURNAL ON COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Vinayak S. Ahluwalia, Lauren N. Steimle, Brian T. Denton
Summary: Markov decision processes (MDPs) are models for sequential decision-making in various fields, but the optimal policy can be sensitive to uncertain parameters. To address this, incorporating multiple parameter models into the solution process has been proposed. The proposed policy based branch-and-bound approach in this article outperforms existing methods in different MDP scenarios.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Yufan Zhang, Honglin Wen, Qiuwei Wu, Qian Ai
Summary: Prediction intervals (PIs) are effective tool for quantifying uncertainty in distribution systems. Traditional central PIs are not suitable for skewed distributions and their offline training is vulnerable to unforeseen changes. We propose an optimal online estimation approach that adapts to different data distributions by adaptively determining probability proportion pairs for quantiles. The approach uses reinforcement learning to integrate adaptive selection and quantile predictions, improving PIs' quality. Case studies show that the proposed method outperforms traditional methods in adapting to data distribution and is more robust against concept drift.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Energy & Fuels
Khalid A. Alnowibet, Mohammed A. El-Meligy
Summary: This paper presents a transmission expansion planning model that uses robust optimization to find the most desirable configuration of the transmission grid. Unlike existing approaches, this model considers multiple uncertainty sets and introduces a new criterion for uncertainty budget. The model demonstrates good performance in experiments.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Engineering, Multidisciplinary
J. Xing, C. Chen, P. Wu
BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES
(2012)
Article
Engineering, Electrical & Electronic
Jie Xing, Chen Chen, Peng Wu
ELECTRIC POWER COMPONENTS AND SYSTEMS
(2010)
Article
Green & Sustainable Science & Technology
Jie Xing, Peng Wu
Summary: An improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed in this paper to enhance the estimation accuracy and stability of SOC in lithium-ion batteries, by adaptively estimating and correcting system noise statistics. Experimental results demonstrate that IAUKF has higher accuracy and stability in SOC estimation compared to conventional methods.
Article
Energy & Fuels
Jie Xing, Peng Wu
Summary: This study focuses on a combined optimization planning method for an electricity-natural gas coupling system with power-to-gas facilities. The optimization model aims to minimize annual investment and operation costs, and an immune algorithm is proposed to solve the model. The rationality and effectiveness of the model are verified through a case study involving a seven-node natural gas system and a nine-node power system.
Article
Computer Science, Information Systems
Ruilin Cao, Jie Xing, Bingyan Sui, Hongyan Ma
Summary: This paper proposes an improved integrated method for power systems with wind generation (WG) by pre-identifying probability distribution, considers the influence of DFIG control strategy on reactive power, and validates the accuracy and efficiency of the proposed method.
Article
Mathematics, Interdisciplinary Applications
Jie Xing, Wanqing Song, Francesco Villecco
Summary: This article develops a new stochastic sequence forecasting model, known as the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process, described by Hurst parameter H and fractal dimension D, is used to more flexibly describe various LRD processes. The forecasting model parameters are estimated by statistical methods and validated using real wind speed data.
FRACTAL AND FRACTIONAL
(2021)
