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Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and CAES Aggregators

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第1期11卷 457-466页

作者： Ghavidel, Sahand Ghadi, Mojtaba Jabbari Azizivahed, Ali Aghaei, Jamshid Li, Li Zhang, Jiangfeng Univ Technol Sydney Sch Elect & Data Engn Broadway NSW 2007 Australia Shiraz Univ Technol Dept Elect & Elect Engn Shiraz *** Iran

This paper proposes a coordinated strategy of a hybrid power plant (HPP), which includes a wind power aggregator and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday, and balancing markets). The CAES aggregator has an extra ability which is called a simple-cycle mode operation that makes it works like a gas turbine when needed, which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem that can be easily solved by commercially available software such as GAMS. The results that were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit and decrease the financial risks.

关键词： Wind power generation Electricity supply industry Indexes Energy storage Uncertainty Turbines Stochastic processes Hybrid power plant (HPP) wind power aggregator (WPA) compressed air energy storage (CAES) mixed integer linear programming financial risks stochastic optimization problem

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Global Optimization of Offshore Wind Farm Collection Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第3期35卷 2256-2267页

作者： Perez-Rua, Juan-Andres Stolpe, Mathias Das, Kaushik Cutululis, Nicolaos A. Tech Univ Denmark DTU Wind Energy Roskilde 4000 Denmark

A mathematical program for global optimization of the cable layout of Offshore Wind Farms (OWFs) is presented. The model consists on a mixed integer linear Program (MILP). Modern branch-and-cut solvers are able to solve large-scale instances, defined by more than hundred Wind Turbines (WTs), and a reasonable number of Offshore Substations (OSSs). In addition to the MILP model to optimize total cable length or initial investment, a pre-processing strategy is proposed in order to incorporate total electrical power losses into the objective function. High fidelity models are adapted to calculate cables current capacities, spatial currents. The MILP model is embedded in an iterative algorithmic framework, solving a sequence of problems with increasing search space size. The search space is defined as a set of underlying candidate arcs. The applicability of the method is illustrated through 10 case studies of real-world large-scale wind farms. Results show that: (i) feasible points are obtained in seconds, (ii) points with an imposed maximum tolerance near the global optimum are calculated in a reasonable computational time in the order of hours, and (iii) the proposed method compares favorably against state-of-the-art method available in literature.

关键词： Power cables Wind turbines Optimization Mathematical model Computational modeling linear programming Measurement Offshore wind energy collection system layout design global optimization mixed integer linear programming medium voltage submarine cables heuristics

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A bottom-up packing approach for modeling the constrained two-dimensional guillotine placement problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 115卷

作者： Martin, Mateus Morabito, Reinaldo Munari, Pedro Univ Fed Sao Carlos Dept Prod Engn Via Washington Luiz Km 235 BR-13565905 Sao Carlos SP Brazil

In this paper, we address the Constrained Two-dimensional Guillotine Placement Problem (C2GPP). This problem considers a rectangular large object and a set of rectangular small item types of given sizes and values. The objective is to select and cut the most valuable subset of small items from the large object using orthogonal guillotine cuts and constrained patterns. To completely model the problem, we present pseudo-polynomial and compact integer non-linear formulations. Then, we obtain an equivalent mixed integer linear programming (MILP) formulation from each non-linear model. These novel formulations are related to a bottom-up packing approach of successive horizontal and vertical builds of the small items. Additionally, we develop a set of constraints for each model which allows us to strictly consider d-staged guillotine cutting patterns, for a given positive integer d. To evaluate the MILP models and compare their performance to the state-of-the-art formulation of the C2GPP, we run computational experiments using three sets of benchmark instances, two of them from the literature. The results show that the proposed models, based on a bottom-up packing approach, lead to optimal or near-optimal solutions in reasonable processing times, even for scenarios that are intractable for the benchmark model. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cutting & packing problems Constrained two-dimensional cutting Non-staged and staged guillotine cuts mixed integer linear programming Bottom-up packing

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Optimal stock control and procurement by reusing of obsolescences in manufacturing

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 148卷

作者： Frontoni, Emanuele Marinelli, Fabrizio Rosetti, Roberto Zingaretti, Primo Univ Politecn Marche Dipartimento Ingn Informaz Via Brecce Manche I-60131 Ancona Italy

Production, procurement planning and logistic organization are complex tasks in companies with multiple production/stocking sites. For companies operating in fashion markets, these tasks are harder due to the high demand variability and frequent turnover of trends that make products outmoded rapidly. In the field of industry informatics under an Industry 4.0 scenario, this paper presents a data and processing framework that mix a Decision Support System (DSS) and a mixed integer linear programming (MILP) model to maximize the potential revenue of the outmoded products. The solution proposed considers both the investment for missing components and the transportation costs between hubs and takes into account constraints on the overall number of items produced, the budget for new component orders and the minimum lot scheduling thresholds. Moreover, to avoid the increment of obsolescences due to raw material orders, the MILP model bounds purchased components to a percentage of used ones. A real worldwide dataset provided by a fashion company, market leader in the design, manufacture and distribution of fashion, luxury, sports and performance eyewear, was used to test performances and repeatability of the mathematical model under different configurations. The results obtained show a huge positive impact on the financial results.

关键词： Production planning Distributed production system Obsolete component reusing Decision support system mixed integer linear programming Fashion industry

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Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 184卷

作者： Ge, Xiaolin Chen, Quan Fu, Yang Chung, C. Y. Mi, Yang Shanghai Univ Elect Power Elect Power Coll Shanghai Peoples R China Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

An optimization model for offshore wind farm maintenance scheduling is presented, considering minimum maintenance costs and maximum power generation. For power generation, the wind speed at each tower site plays an important role that is impacted not only by the dynamically changing wake overlap area and the consequence of variation of wind direction but also by the relative position of the wake affected by the maintenance status. This paper combines the wake model with the maintenance status to accurately express the input wind speed of the wind turbine (WT) in each period. Because the optimization model includes complex dynamical coupling relationships and a number of nonlinear constraints, mixed integer second-order cone programming (MISOCP) are employed to address these issues. The MISOCP model is relaxed as a mixed integer linear programming (MILP) model to improve computational efficiency and the e-constraint method is utilized to handle the multi-objective function. The proposed model and method are tested in a short-term maintenance case of an offshore wind farm. The numerical results demonstrate that the proposed approach can achieve sound economic benefits and provide comprehensive decision support.

关键词： Offshore wind farm Maintenance Wake effect mixed integer linear programming Multi-objective

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Multi-objective optimization of sustainable biomass supply chain network design

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APPLIED ENERGY 2020年 272卷

作者： Durmaz, Yesim Gital Bilgen, Bilge DokuzEylul Univ Grad Sch Nat & Appl Sci Dept Ind Engn Tinaztepe Campus TR-35397 Izmir Turkey DokuzEylul Univ Dept Ind Engn Tinaztepe Campus TR-35397 Izmir Turkey

This paper addresses the optimal design and planning of the biomass supply chain network that encompasses flow from poultry farms to biogas facilities. A novel multi-stage solution methodology is developed to solve the sustainable biomass supply chain network design problem. Geographical Information Systems, and Analytic Hierarchy Process Techniques are used to determine the candidate location of biogas facilities. The proposed multi-objective mixed integer linear programming model is capable of making strategic decisions (optimal biogas facility locations with capacities) along with the tactical decisions (transportation network flows). The model incorporates the two objective function of maximization of the profit, and minimization of total distance between poultry farms and biogas facilities. The aim is to determine the optimal number, location, and size of the biogas facilities, as well as the network flow, and electricity generated. The applicability of the model and solution methodology is demonstrated through a case study for a poultry supply chain network in Turkey. Additionally, sensitivity analysis is conducted to account for the impact of different parameters on the model. Sensitivity analysis show that both maximum distance parameter, and purchasing prices have major impact on decisions, and financial yield.

关键词： Biomass supply chain network design Multi-objective optimization Geographic information systems Analytical hierarchy process mixed integer linear programming Poultry waste

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Closed loop supply chain network for local and distributed plastic recycling for 3D printing: a MILP-based optimization approach

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RESOURCES CONSERVATION AND RECYCLING 2020年 154卷

作者： Santander, Pavlo Sanchez, Fabio A. Cruz Boudaoud, Hakim Camargo, Mauricio Univ Lorraine ERPI F-54000 Nancy France

Recent research and initiatives increasingly propose a new approach, based on distributed plastic recycling for open-source (OS) 3D printing technologies, as a way to deal with the issue of plastic waste and to support the development of the circular economy (CE). Distributed recycling can be thought of as a sort of "smart grid", composed of small and coordinated recycling units. However, the operational complexity of this distributed approach limits its application. Furthermore, the environmental and economic advantages have yet to be demonstrated. This article therefore explores the economic and environmental feasibility of this distributed plastic recycling approach from a logistics perspective, as a step towards its validation. To achieve this, an optimization mixed integer linear programming (MILP) model was used as an evaluation tool, representing a local closed loop supply chain (CLSC) network. The proposed model is illustrated using a case study of a university seeking to implement a distributed recycling demonstrator in order to recover 3D printing wastes from secondary schools in the northeast of France. Following this step, a sensitivity analysis was carried out considering the market variations (price of virgin plastic filament) and the amount of available plastic waste derived from the schools. The results obtained show positive economic and environmental benefits of carrying out this new method of plastic recycling. This work serves as a basis for continuing to explore the feasibility and replication of the distributed plastic recycling network in other contexts.

关键词： Closed loop supply chain Plastic recycling 3D printing Circular economy mixed integer linear programming Distributed recycling

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Designing an efficient clustering strategy for combined Fog-to-Cloud scenarios

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 109卷 392-406页

作者： Asensio, A. Masip-Bruin, X. Duran, R. J. de Miguel, I Ren, G. Daijavad, S. Jukan, A. Univ Politecn Cataluna Adv Network Architectures Lab CRAAX UPC Barcelona Spain Univ Valladolid Valladolid Spain IBM Corp Almaden Res Ctr San Jose CA USA IBM Corp Almaden Res Ctr Next Generat Comp Syst Dept San Jose CA USA Tech Univ Braunschweig Commun Networks Braunschweig Germany

The evolution of the Internet of Things (IoT) is imposing many distinct challenges, particularly to guarantee both wide and global systems communication, and to ensure an optimal execution of services. To that end, IoT services must make the most out of both cloud and fog computing (turning into combined fog-cloud scenarios), which indeed requires novel and efficient resource management procedures to handle such diversity of resources in a coordinated way. Most of the related works that can be found in the literature focus on resource mapping for service-specific execution in fog-cloud;however, those works assume a control and management architecture already deployed. Interestingly, few works propose algorithms to set that control architecture, necessary to execute the services and effectively implement services and resource mapping. This paper addresses that challenge by solving the problem of optimal clustering of devices located at the edge of the network offering their resources to support fog computing while defining the control and management role of each of them in the architecture, in order to ensure access to management functions in combined fog-cloud scenarios. In particular, we set out the Fog-Cloud Clustering (FCC) problem as an optimization problem, which is based on multi-objective optimization, including realistic, novel and stringent constraints;e.g., to improve the architecture's robustness by means of a device acting as a backup in the cluster. We model the FCC problem as a mixed integer linear programming (MILP) formulation, for which a lower and an upper bound on the number of required clusters is derived. We also propose a machine learning-based heuristic that provides scalable and near-optimal solutions in realistic scenarios in which, due to the high number of connected devices, solving the MILP formulation is not viable. By means of a simulation study, we demonstrate the effectiveness of the algorithms comparing its results with those of MILP

关键词： Fog-to-Cloud systems Fog-to-Cloud management Clustering mixed integer linear programming Machine learning

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Thermal energy storage using phase change materials: Techno-economic evaluation of a cold storage installation in an office building

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APPLIED ENERGY 2020年 276卷

作者： Tan, Pepe Lindberg, Patrik Eichler, Kaia Loveryd, Per Johansson, Par Kalagasidis, Angela Sasic Chalmers Univ Technol Dept Architecture & Civil Engn Div Bldg Technol Gothenburg Sweden AF Poyry AB Gothenburg Sweden Akad Hus AB Gothenburg Sweden

Utilizing the latent heat of solidification and melting of so-called phase change materials (PCMs) allows higher storage densities and increased process flexibility within energy systems. However, there is an existing gap in the current literature studying simultaneously the technical and economic performance of these thermal energy storages within an actual application. Thus, in this work a comprehensive techno-economic analysis of a fullscale storage with 7000 L salt-hydrate surrounding a polypropylene capillary tube heat exchanger is presented. The storage is located in a multi-story office building in Gothenburg, Sweden and is used for daily peak shaving of the building's cooling energy demands. The daily utilizable storage capacity for the installation was determined to be 99kWh, which is 36% of the installed capacity given by the storage manufacturer. The major limiting factor were found to be 60-75% smaller charging rates than what was designed by the manufacturer. Using a mixed integer linear programming model (MILP) to yield optimum scheduling, the storage investment cost limit for a 5 year payback time can be estimated as 9804 SEK (approximate to 921 EUR). These developed key performance indicators can be readily compared against alternative storage technologies and designs in order to select the optimal storage design for equivalent applications. Future work is needed to investigate reasons behind the lower than expected storage capacity.

关键词： Thermal energy storage Phase change materials mixed integer linear programming Key performance indicators

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Scheduling of a single-source multiproduct pipeline system by a matheuristic approach: Combining simulated annealing and MILP

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COMPUTERS & CHEMICAL ENGINEERING 2020年 136卷

作者： Tsunoda Meira, William Hitoshi Magatao, Leandro Neves-Jr, Flavio Arruda, Lucia V. R. Vaqueiro, Jonas P. Relvas, Susana Barbosa-Povoa, Ana Paula Univ Tecnol Fed Parana UTFPR Grad Program Elect & Comp Engn CPGEI BR-80230901 Curitiba Parana Brazil Petroleo Brasileiro SA PETROBRAS Leopoldo Amer Miguez de Mello Res & Dev Ctr CENPE BR-21941915 Rio De Janeiro Brazil Univ Lisbon Ctr Management Studies Inst Super Tecn P-1049001 Lisbon Portugal

Pipeline systems are used for distribution of oil derivatives, where the optimization of transport activities impact considerably in the oil company's economy. We focused on the scheduling of pumping and delivery operations on straight multiproduct pipeline systems connecting a single-source to multiple-destinations. This paper develops new preprocessing heuristics and a discrete-time mixed integer linear programming (MILP) formulation for the allocation and sequencing activity (ASM model). The new MILP model includes constraints concerning the scheduling of pumping interruptions considering stoppages in order to avoid inventory problems. A preprocessing block is also proposed to better estimate the delivery operations for the initialization batches using a simulated annealing metaheuristic to provide a more robust input data for the ASM model. The results for two case studies of a real-world pipeline system were studied. Better inventory management is observed when facing low demand instances due to its capacity to effectively propose pipeline interruptions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Oil pipeline Scheduling Matheuristic mixed integer linear programming Simulated annealing

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