Article
Engineering, Chemical
Ariel A. Boucheikhchoukh, Christopher L. E. Swartz, Eric Bouveresse, Pierre Lutran, Anna Robert
Summary: Uncertainty in refinery planning poses challenges to the day-to-day operations of an oil refinery. Stochastic programming framework can incorporate parameter uncertainty and provide robust solutions, which is more effective than deterministic modeling techniques.
Article
Engineering, Electrical & Electronic
Mohammed A. El-Meligy, Ahmed M. El-Sherbeeny
Summary: The aim of this paper is to propose a hybrid robust/stochastic model for transmission expansion planning. The uncertainties related to demand and wind are modelled via stochastic programming, while the uncertainties associated with the generators' offer prices are modelled via robust optimization. The proposed model is divided into a master problem and several independent subproblems using a tailored implementation of Bender's decomposition. The results show that generators' offer price and their correlation can considerably affect the optimal plan of the transmission planning.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Weilun Wang, Mingqiang Wang, Xueshan Han, Ming Yang, Qiuwei Wu, Ran Li
Summary: This paper proposes a distributionally robust transmission expansion planning model that takes into account the uncertainty of contingency probability, and demonstrates the feasibility and effectiveness of the proposed model through experiments on the IEEE RTS system and the IEEE 118-bus system.
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaofan Lai, Xiaolong Lu, Xinyao Yu, Ning Zhu
Summary: This study introduces a new vaccination station location model that takes into account the planning of medical professionals, vaccine procurement, and inventory decisions. A two-stage stochastic mixed integer linear program is used to address the uncertain demands for multiple types of vaccines over multiple periods. By developing a heuristic algorithm based on Benders decomposition, the effectiveness and efficiency of the model and new heuristics are demonstrated through numerical experiments and sensitivity analysis.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Gonzalo E. Constante-Flores, Antonio J. Conejo, Ricardo M. Lima
Summary: This article proposes a solution method for the large-scale stochastic unit commitment problem that includes weekly energy storage and significant weather-dependent stochastic generating capacity. By decomposing the problem into a mixed-integer linear master problem and linear and continuous subproblems, and using conditional value-at-risk as a risk measure, we improve the standard Benders decomposition method with the column-and -constraint generation algorithm. Our computational experiments demonstrate the effectiveness of the proposed decomposition method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Management
Xuedong Zhu, Junbo Son, Xi Zhang, Jianguo Wu
Summary: The integrated process planning and scheduling (IPPS) problem is crucial for achieving desirable performance in complex manufacturing systems. Although some approaches have been proposed, optimal solutions for benchmark datasets are still difficult to obtain in a reasonable time, and few methods can address both types of IPPS problems. In this study, a constraint programming (CP) model is developed and two logic-based Benders decomposition (LBBD) algorithms are proposed for each type of IPPS problem, ensuring computational efficiency.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Management
Jesus A. Rodriguez, Miguel F. Anjos, Pascal Cote, Guy Desaulniers
Summary: The maintenance scheduling problem for hydroelectric generators involves uncertainty in water flows and nonlinearity in hydroelectric production, solved using a two-stage stochastic program and parallelized Benders decomposition algorithm. By approximating hydroelectric production with linear inequalities and indicator variables, tailoring and testing various acceleration techniques successfully sped up the algorithm fourfold. Industrial results confirm high scalability of the parallelized Benders implementation in various scenarios.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Operations Research & Management Science
Denise D. Tonissen, Joachim J. Arts, Zuo-Jun Max Shen
Summary: This paper presents a column-and-constraint generation algorithm for two-stage stochastic programming problems, which does not assume fixed recourse and can be used to trade-off computational speed and memory requirements. The algorithm outperforms existing models in both computational time and memory requirements, and the use of adaptive relative tolerance further reduces computational time.
Article
Multidisciplinary Sciences
S. Wogrin, D. Tejada-Arango, A. Downward, A. B. Philpott
Summary: In this study, the JuDGE optimization package is applied to a multistage stochastic leader-follower model in order to maximize the social welfare of consumers and producers. The model is formulated as a large-scale mixed integer program and applied to a 5-bus instance over scenario trees of varying size. The computational effort of JuDGE is compared with state-of-the-art integer programming package for solving the deterministic equivalent mixed integer program.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Article
Management
Niels van der Laan, Ward Romeijnders
Summary: We propose a new solution method for two-stage mixed-integer recourse models that can handle general mixed-integer variables in both stages. Our method is based on Benders' decomposition, where we iteratively construct tighter approximations of the expected second stage cost function using a new family of optimality cuts derived from extended formulations of the second stage problems. We show convergence of our method by proving that the optimality cuts recover the convex envelope of the expected second stage cost function. Finally, we demonstrate the potential of our approach through numerical experiments on investment planning and capacity expansion problems.
OPERATIONS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Yu-Hong Wang, Sheng-Jie Hu, Yu-Yan Song, Qi Zeng, Zong-Sheng Zheng
Summary: This paper proposes a three-layer power grid planning model considering the characteristics of SMES and system robustness, which can optimize the construction location and capacity of SMES, and it is solved by Benders decomposition. The effectiveness of the model is verified in the IEEE 24-RTS system.
IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
(2021)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Electrical & Electronic
Tian Lan, Zhangxin Zhou, Wenzong Wang, Garng M. Huang
Summary: This study proposes a novel stochastic optimization method to solve AC optimal transmission switching problems with grid uncertainties. Through a generalized Benders decomposition algorithm, the optimal switching plan and system cost can be found iteratively while maintaining accuracy. The scalability analysis confirms the effectiveness of the proposed approach in dealing with a large number of scenarios.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Management
Xavier Blanchot, Francois Clautiaux, Boris Detienne, Aurelien Froger, Manuel Ruiz
Summary: This paper presents a new exact algorithm for solving two-stage stochastic linear programs. The algorithm, based on the multicut Benders reformulation, divides the subproblems into batches and solves only a small proportion of them in each iteration. A general framework is proposed to stabilize the algorithm, and its finite convergence and exact behavior are demonstrated. Computational experiments on large-scale stochastic optimization instances show the efficiency of the proposed algorithm compared to nine alternative algorithms in the literature. Additional computational time savings are obtained using primal stabilization schemes.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Luiz Carlos da Costa, Fernanda Souza Thome, Joaquim Dias Garcia, Mario V. F. Pereira
Summary: This study presents a methodology to incorporate reliability constraints in optimal power systems expansion planning, using risk measures VaR and CVaR commonly used in financial markets. To minimize computational effort, the planning problem is split into an investment problem and two subproblems, solved using SDDP and Monte Carlo simulation.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Xiaoyu Cao, Jianxue Wang, Jianhui Wang, Bo Zeng
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Engineering, Electrical & Electronic
You Lin, Yishen Wang, Jianhui Wang, Siqi Wang, Di Shi
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Automation & Control Systems
Mahdi Khodayar, Jianhui Wang
Summary: This study proposes a new deep generative architecture (DGA) based on the LSTM network for probabilistic time-varying parameter identification. By learning the continuous probability density function, composite load modeling is achieved, showing accurate estimation of uncertain power resources.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Review
Green & Sustainable Science & Technology
S. Yin, J. Wang, Z. Li, X. Fang
Summary: With the increasing penetration of solar energy in the energy systems, the correct modeling of solar generation in the market and addressing uncertainty-based operational problems have become crucial issues. Unlike other renewable resources, solar power can be easily integrated in a distributed manner on the demand side with potential for significant future expansion. The electricity markets are transitioning from a deterministic and centralized framework to a stochastic and decentralized one, driven by the development of deregulated power markets.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Automation & Control Systems
Liudong Chen, Nian Liu, Chenchen Li, Jianhui Wang
Summary: This interdisciplinary P2P energy sharing framework proposed in this article considers both technical and sociological aspects, based on prospect theory and stochastic game theory. Under this framework, producers and consumers participate with different roles and strategies, addressing problems arising from social attributes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Siyuan Wang, Chaoyue Zhao, Lei Fan, Rui Bo
Summary: As the penetration of intermittent renewable energy increases, flexible generation resources are becoming more important in addressing power imbalance. This study proposes a two-stage distributionally robust unit commitment framework to adjust the unit commitment decisions for flexible generation resources and accommodate renewable energy intermittency. The proposed approach reduces system cost through improved flexible resource quantification.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Jiayong Li, Mohammad E. Khodayar, Jianhui Wang, Bin Zhou
Summary: This paper presents a data-driven distributionally robust co-optimization model for P2P energy trading and network operation of MGs. The model considers various operational constraints and uncertainties from load consumption and RG, utilizing emerging technologies to address them effectively.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Green & Sustainable Science & Technology
Shengfei Yin, Jianhui Wang, Harsha Gangammanavar
Summary: This paper presents a three-stage unit commitment model for transmission and distribution coordination in the presence of renewable generation and demand uncertainties. The model utilizes a multi-stage stochastic programming approach to handle uncertainties and adopts a convexified AC branch flow formulation in the distribution system. A generalized nested L-shaped algorithm is devised for efficient solving of the proposed framework, with numerical experiments confirming its efficacy on multi-scale test systems.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Engineering, Electrical & Electronic
Lei Fan, Chaoyue Zhao, Guangyuan Zhang, Qiuhua Huang
Summary: This paper proposes a robust optimization based framework to measure system flexibility by considering the interaction between Economic Dispatch and Automatic Generation Control. By utilizing a cutting plane algorithm with reformulation technique, seven different indices of the system are obtained and the impacts of several system factors on system flexibility are studied numerically.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Mahdi Khodayar, Jianhui Wang
Summary: This paper proposes a deep GDL algorithm for learning the topological patterns of power grids by capturing the probability density functions of nodes and edges. Simulation results demonstrate the significant accuracy of the created synthetic power grids in terms of topological metrics and power flow measurements.
2021 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST)
(2021)
Proceedings Paper
Energy & Fuels
Shengfei Yin, Jianhui Wang, Yanling Lin, Xin Fang, Jin Tan, Haoyu Yuan
Summary: With renewable resources increasingly entering power systems, energy storage systems (ESSs) have become essential for providing energy arbitrage and ancillary services. This paper proposes a general framework in the current electricity market environment to model the participation of multi-type ESSs and evaluate their performance, demonstrating the excellent potential of ESSs in providing ancillary services for the bulk power system.
2020 52ND NORTH AMERICAN POWER SYMPOSIUM (NAPS)
(2021)
Proceedings Paper
Energy & Fuels
Xin Fang, Jin Tan, Haoyu Yuan, Shengfei Yin, Jianhui Wang
Summary: With the increasing penetration of photovoltaic generation, electric power systems require more flexible resources and renewable generation, including PV, to provide more flexible ancillary services to improve system reliability and increase PV profitability.
2020 52ND NORTH AMERICAN POWER SYMPOSIUM (NAPS)
(2021)
Article
Engineering, Electrical & Electronic
Mingjian Cui, Jianhui Wang
Summary: This paper proposed a new defense mechanism called DH-MTD to hide the reactance of each phase in unbalanced AC distribution system and ensure system voltage stability. By using data-driven methods to combat cyberattacks, the effectiveness of DH-MTD was demonstrated in an unbalanced IEEE 123-bus distribution system.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Review
Energy & Fuels
Mandi Khodayar, Guangyi Liu, Jianhui Wang, Mohammad E. Khodayar
Summary: With the rapid growth of power systems measurements, utilizing deep learning algorithms for power systems data processing has become a research trend. The study reveals the theoretical advantages of deep learning in power systems research and discusses solutions under various problem settings.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Zhe Chen, Zhengshuo Li, Chuangxin Guo, Jianhui Wang, Yi Ding
Summary: In this paper, a coordinated robust reserve scheduling model for the coupled transmission and distribution networks is proposed, using a fully distributed ADMM framework to solve the problem. A two-layer iterative process is presented to enhance the convergence, improving cost-effectiveness and reliability effectively.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
