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Energy-aware flow shop scheduling with uncertain renewable energy

COMPUTERS & OPERATIONS RESEARCH 2024年 170卷

作者： Ghorbanzadeh, Masoumeh Davari, Morteza Ranjbar, Mohammad Ferdowsi Univ Mashhad Fac Engn Ind Engn Dept Mashhad Iran Univ Cote Azur SKEMA Business Sch Lille France

This paper investigates an energy-aware flow shop scheduling problem with on-site renewable and grid energy resources. To deal with the uncertainty of renewable energy resources, we first develop two two-stage stochastic programming formulations based on pulse and step models to minimize the total energy cost purchased from the grid. Next, we develop two robust models where in the first one we assume the cost of buying energy from the grid is limited to a given budget and we aim to maximize the number of scenarios that comply with this limitation. In the second robust model, we aim to minimize the grid energy cost by considering a predetermined confidence level. To solve the stochastic and robust models, we develop Benders decomposition algorithms and incorporate the warm-up technique for Benders algorithm. Computational experiments on randomly generated test instances demonstrate that the step formulation outperforms the pulse formulation for larger instances. Additionally, each developed Benders decomposition algorithm outperforms its corresponding model, and the warm-up technique improves the performance of the Benders decomposition algorithms.

关键词： Flow shop scheduling Renewable energy stochastic programming Robust optimization Benders decomposition

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stochastic Optimal Dispatching of Electrothermal Integrated Energy System Considering Uncertainty of Heat Load 4

Stochastic Optimal Dispatching of Electrothermal Integrated ...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Song, Lin Wu, Haotian Ke, Deping Xu, Jian Zheng, Jingwen Ling, Zaixun Cui, Yibo Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China State Grid Hubei Elect Power Co Ltd Energy Internet Technol Ctr Elect Power Res Inst Wuhan Peoples R China

ISBN: (纸本)9781665479141

This paper is based on Cornish-Fisher series to predict solar irradiance in multiple scenarios, and establishes a day-ahead stochastic dispatch model of regional electric heating integrated energy system considering the uncertainty of thermal load. It is verified by simulation that the proposed method has higher accuracy in predicting heat load, can promote the consumption of renewable energy, and effectively reduce the energy cost of users.

关键词： stochastic programming integrated energy system heat load Cornish-Fisher series

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Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources

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RENEWABLE ENERGY 2024年 229卷

作者： Alvarez-Arroyo, Cesar Vergine, Salvatore de la Nieta, Agustin Sanchez Alvarado-Barrios, Lazaro D'Amico, Guglielmo Univ Seville Escuela Politecn Super Elect Engn Dept c-Virgen Africa 9 Seville 41092 Spain Marche Polytech Univ Dept Management I-60121 Ancona Italy Univ Loyola Andalucia Seville Spain Univ G dAnnunzio Pescara Italy

The significance of microgrid systems has grown considerably. This research proposes an innovative approach to manage uncertainty in microgrids by employing energy storage systems as the exclusive flexible resource. To address this challenge, a mathematical problem is formulated as a two-stage stochastic programming model, considering two uncertainties in the microgrid: wind and photovoltaic production. The microgrid system encompasses multiple components, including a diesel generator, a microturbine, wind and photovoltaic power generation, an energy storage system, and the microgrid's demand. Notably, the microgrid exhibits two distinctive features: (i) the complete integration of wind and photovoltaic production, and (ii) the utilisation of an energy storage system as the sole flexible resource. The objective is to minimise the expected cost of the microgrid system while determining the optimal capacity of the energy storage system to meet the energy balance constraint. This constraint takes into account the varying scenarios of wind and photovoltaic production. The decisions are taking for a duration of 8760 h, a long-term evaluation. A case study is presented for actual data from Greece and the results show high volatility of the renewable energy sources implies higher energy storage system capacity as a sole flexible source for avoiding renewable curtailment.

关键词： Energy storage system Flexible energy source Microgrid Photovoltaic power stochastic programming Wind power

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A novel fuzzy scenario-based stochastic general best-worst method

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期252卷

作者： Tavana, Madjid Sorooshian, Shahryar Rezaei, Homa Mina, Hassan La Salle Univ Distinguished Chair Business Analyt Business Syst & Analyt Dept Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Univ Gothenburg Dept Business Adm Gothenburg Sweden Islamic Azad Univ IAU Coll Tech & Engn Dept Ind Engn Saveh Branch Saveh Iran Saito Univ Coll Prime Sch Logist Petaling Jaya 46200 Selangor Malaysia

The best-worst method (BWM) is a popular multi-criteria decision-making (MCDM) method known for the low number of pairwise comparisons and high consistency. General BWM (GBWM) is a new version of BWM that considers the interdependencies between interwind factors in MCDM problems. This study proposes a fuzzy stochastic GBWM for weighting intertwined factors using a scenario-based approach in complex intertwined or hierarchical networks under uncertainty. Fuzzy stochastic GBWvM provides decision-makers with a wide range of weights, from fuzzy-stochastic weights to stochastic weights (defuzzified weights), fuzzy weights, and deterministic weights to use with different assumptions in the decision-making process. We demonstrate the efficacy and applicability of the proposed method with a well-known car-buying problem in the literature and a real-world problem in the transportation industry.

关键词： General best-worst method stochastic programming Fuzzy theory Interdependency Multi-criteria decision-making

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Multiserver time window allowance schedules for virtual visits with uncertain time-dependent no-shows and service times

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ADVANCED ENGINEERING INFORMATICS 2024年 59卷

作者： Shen, Xiaoxiao Li, Na Xie, Xiaoqing Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management 800 Dongchuan Rd Shanghai 200240 Peoples R China China Europe Int Business Sch Dept Econ & Decis Sci 699 Hongfeng Rd Shanghai 201206 Peoples R China

Virtual care serves as a new mode that can divert non-urgent visits from traditional office visits. Whether virtual service can improve the access to medical treatment and reduce the burden of traditional office services, the key issue is to generate efficient appointment schedules with the lowest operation cost. In this paper, considering the uncertainty of time-dependent no-shows and service times, we investigate a multiserver time window allowance appointment scheduling problem, where time window constraints that restricts virtual visits to be served during the particular period are explicitly modeled. We formulate the problem as a stochastic mixed-integer program to optimize decisions of physician allocation and appointment time simultaneously. Based on the sample average approximation, a stabilized Benders decomposition algorithm is developed by incorporating acceleration techniques, such as cut aggregation and feasibility cuts. Numerical results based on real data indicate the effectiveness of the proposed multiserver time window allowance schedules (MTWAS) and algorithm. Comparing with the off-the-shelf solver Gurobi, the developed algorithm demonstrates high performance in terms of computation speed and solution quality. Under different time-dependent no-show patterns of virtual and office visits, the obtained MTWAS perform better than previous solutions in almost all test cases. In addition, we offer useful managerial insights to aid the virtual service provider in making better scheduling decisions.

关键词： Virtual visit Appointment scheduling Time window allowance schedule stochastic programming Benders decomposition

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An enhanced benders decomposition method and a matheuristic algorithm for solving the stochastic capacitated facility location problem with shortages

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Gomez-Rocha, Jose Emmanuel Hernandez-Gress, Eva Selene Camacho-Vallejo, Jose-Fernando Santos, Cipriano Tecnol Monterrey Escuela Ingn & Ciencias Monterrey Mexico

The Capacitated Facility Location Problem (CFLP) is a well-known combinatorial optimization problem extensively studied in the field of location sciences. It has numerous applications in industrial engineering, humanitarian logistics, telecommunication networks, and other domains. Incorporating uncertainties in demands, stochastic programming emerges as a suitable approach to address this problem. Therefore, due to the inherent stochasticity, not all customers demand may be satisfied. To address this issue, we incorporate the concept of shortages into the CFLP. As a result, the CFLP with shortages and normally distributed demands is proposed, where the cost of losing a customer is considered as a penalty cost in the objective function. To address the problem, we propose three exact methods and a matheuristic algorithm. The exact methods are grounded in Benders decomposition: a straightforward implementation, a refined version adding a set of valid inequalities, and an enhanced approach based on Branch-and-Cut. The matheuristic algorithm follows a Fixing- First scheme based on pricing strategies, efficiently solving the problem within a reasonable computational time. The effectiveness of the proposed algorithms is evaluated by comparing it against a deterministic equivalent solution given by the general-purpose solver Gurobi. Computational experiments are conducted on a set of challenging instances using a sample average approximation scheme. To validate the applicability of the problem under study, a real case study involving Mobile Health Clinics (MHCs) located in Mexico was analyzed. Interesting managerial insights were obtained, highlighting the importance of having at least 271 MHCs to achieve the objectives that the government has set for medical coverage of acute respiratory infections for socially vulnerable people through its healthcare programs.

关键词： Facility location stochastic programming Shortages Benders decomposition Matheuristics Mobile health clinics

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An adaptive robust optimization model for transmission expansion planning considering uncertain intervals

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 157卷

作者： Mahmoud, Haitham A. Sharaf, Mohamed El-Meligy, Mohammed A. King Saud Univ Coll Engn Ind Engn Dept POB 800 Riyadh 11421 Saudi Arabia King Saud Univ Adv Mfg Inst Riyadh 11421 Saudi Arabia

The degree of uncertainty level has been increasing in power grids due to the growing integration of renewable generation. stochastic programming (SP) and robust optimization (RO) or a hybrid SP/RO have been employed to cope with uncertainty in power system planning studies. In particular, RO has attracted much attention in this context due to its effectiveness and reliable implementation. Hence, several RO-based models, methodologies, and solution algorithms have been presented to improve the preciseness when modeling uncertainties. To employ RO, it is required to construct an uncertainty set that can be predicted using historical data. The current literature assumes that this uncertainty set is fully known to the planner, which does not reflect a real issue. In this paper, we propose a new model where the intervals defining polyhedral uncertainty sets are supposed to be the uncertain parameters. These uncertain intervals are modeled using either SP or RO. This means that the lower and upper bounds of the intervals are supposed to follow a specific probability density function (PDF), or they lie in specific intervals. The numerical studies show the methodology's effectiveness and imply that it is crucial to model uncertain intervals in the TEP problem.

关键词： Robust optimization stochastic programming Transmission expansion planning Uncertain intervals

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Multi-plant hydro power production function modeling: Application to the stochastic mid-term hydrothermal planning problem

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Lira, Pedro T. M. Diniz, Andre Luiz Borges, Carmen L. T. Elect Energy Res Ctr CEPEL Rio De Janeiro Brazil Fed Univ Rio De Janeiro UFRJ Rio De Janeiro Brazil

This work proposes the modeling and application of the so-called "multi-hydro plants"for the stochastic midterm hydrothermal planning problem. The approach consists of aggregating sub-cascades of hydroelectric plants based on the fact that the operation of the cascades is dictated only by decisions on the release of the reservoirs with regularization capacity, and the operation of run-of-the-river plants is a consequence of such decisions. As a result, we obtain a formulation of the problem with a smaller number of variables and constraints, without any loss of representation of the physical and operation constraints of the hydro plants. This yields a reduction in the computational time to solve typical stochastic hydrothermal coordination problems of more than 20%, and results are presented for a real case of the Brazilian system. Furthermore, there is even a gain in accuracy of about 6% in the approximation of the non-concave hydro production function, and an analytical investigation regarding this aspect is conducted.

关键词： Hydro production function Mid-term planning stochastic programming Piecewise-linear models

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A pricing strategy based on bi-level stochastic optimization for virtual power plant trading in multi-market: Energy, ancillary services and carbon trading market

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 231卷

作者： Chen, Yue Niu, Yuguang Qu, Chenzhi Du, Ming Liu, Pin North China Elect Power Univ Sch Control & Comp Engn State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

Virtual power plants (VPPs) has been considered as an effective approach to manage internal prosumers participating in market transactions with a jointly clearing of energy markets (EM), reserve ancillary services markets (ASM), and carbon trading markets (CTM). Thus, how to address the problem of energy management for VPP in coupling multiple market trading mechanisms has determined as a technical point. In this paper, a bi-level Stackelberg game pricing strategy for VPP trading on multi -market is devised to incentivize its internal units, especially focusing on electric vehicles (EVs), through distinct price signals. An accurate dispatchable power model for EVs is established based on travel patterns and responsiveness. With the goal of maximizing profits, VPP determines trading prices for heterogeneous prosumers, while prosumers respond consistently with price signals to minimize respective operating costs. The hierarchical model is converted to a unified mixed -integer linear programming problem via duality theory. To overcome the uncertainty of renewable energy during solving, a stochastic programming approach with Conditional -Value -at -Risk (CVaR) is employed to estimate the expected losses. Adopting the proposed pricing method in VPPs increases expected profit by 5.51%, purchase prices being 2.08% higher and selling prices 4.85% lower than unified pricing, benefiting both producers and consumers.

关键词： Virtual power plant Auxiliary service market Carbon trading market Pricing strategy Stackelberg game stochastic programming

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A loose Benders decomposition algorithm for approximating two-stage mixed-integer recourse models

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MATHEMATICAL programming 2021年第1-2期190卷 761-794页

作者： van der Laan, Niels Romeijnders, Ward Univ Groningen Dept Operat POB 800 NL-9700 AV Groningen Netherlands

We propose a new class of convex approximations for two-stage mixed-integer recourse models, the so-called generalized alpha-approximations. The advantage of these convex approximations over existing ones is that they are more suitable for efficient computations. Indeed, we construct a loose Benders decomposition algorithm that solves large problem instances in reasonable time. To guarantee the performance of the resulting solution, we derive corresponding error bounds that depend on the total variations of the probability density functions of the random variables in the model. The error bounds converge to zero if these total variations converge to zero. We empirically assess our solution method on several test instances, including the SIZES and SSLP instances from SIPLIB. We show that our method finds near-optimal solutions if the variability of the random parameters in the model is large. Moreover, our method outperforms existing methods in terms of computation time, especially for large problem instances.

关键词： stochastic programming Mixed-integer recourse Convex approximations Error bounds

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