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Optimal energy-saving operation strategy of 5G base station with communication caching

Energy Reports 2025年 14卷 343-351页

作者： Chunguang Lu Jiaying Wang Junzhe Huang Tao Xiao Lei Yan State Grid Zhejiang Electric Power Co. Ltd. Marketing Service Center Hangzhou China Zhejiang University Hangzhou China

To further explore the energy-saving potential of 5 G base stations, this paper proposes an energy-saving operation model for 5 G base stations that incorporates communication caching and linearization techniques. Firstly, in terms of energy equipment, the electrical component characteristics of the 5 G base station’s constituent units are modeled, including air conditioning loads, power supply systems, and energy storage systems. Secondly, with respect to communication equipment, the relationship between equipment power consumption and communication traffic is quantified, and a communication flow model incorporating caching technology is constructed. Finally, an energy-saving operation model is developed, which couples the energy equipment and communication equipment, and is formulated as a mixed-integer linear programming (MILP) problem. Case studies demonstrate that the proposed model effectively integrates the characteristics of electrical components and data flow, enhancing energy efficiency while satisfying user communication demands, thus contributing to the energy-saving goals of 5 G base stations.

关键词： 5 G base station Communication data flow Communication caching mixed-integer linear programming Energy-saving operation strategy

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Power-to-Gas for energy system flexibility under uncertainty in demand, production and price

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ENERGY 2023年 284卷

作者： Marzi, Emanuela Morini, Mirko Saletti, Costanza Vouros, Stavros Zaccaria, Valentina Kyprianidis, Konstantinos Gambarotta, Agostino Univ Parma Dept Engn & Architecture Parco Area Sci 181-A I-43124 Parma Italy Univ Parma Ctr Energy & Environm CIDEA Parco Area Sci 42 I-43124 Parma Italy Malardalen Univ Sch Business Soc & Engn POB 883 S-72123 Vasteras Sweden

The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as mixed-integer linear programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances.

关键词： Multi-energy systems Two-stage stochastic programming Power-to-Gas mixed-integer linear programming Uncertainties Hydrogen storage

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A matheuristic-based solution approach for integrated stock selection and sizing problem with a case study in a corrugated box-production company

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 195卷

作者： Baykasoglu, Adil Subulan, Kemal Karaman, Ilkim Dokuz Eylul Univ Fac Engn Dept Ind Engn Buca Izmir Turkiye Dokuz Eylul Univ Grad Sch Nat & Appl Sci Buca Izmir Turkiye

The majority of former research has concentrated on different variants of the cutting stock problems with fixedsized stocks only, and there is a scarcity of studies in the literature on variable-sized cutting stock problems, where dimensions of stocks (width and length) also need to be determined. In this research topic, a few studies have been published in recent years. Based on this motivation, this paper addressed a case-oriented twodimensional cutting stock problem with variable-sized stocks of a carton box manufacturing company to determine optimal dimensions, production requirements, and the purchasing amounts of the corrugated boards. A novel mathematical programming model of the problem is first developed that formally defines the problem. Due to the complex nature of the proposed model and difficulties in its optimal solution, a matheuristic-based solution approach is also proposed. The proposed approach incorporates a mixed-integer linear program (MILP) into a Simulated Annealing (SA) algorithm. By using the proposed matheuristic approach, the dimension values of the purchased corrugated boards are selected from the combinations of zero-waste dimensions of the demanded carton boxes and supplied at each iteration of the SA algorithm as inputs to MILP. Then, MILP is easily solved within reasonable computation times by making use of the Gurobi MIP solver in each SA iteration. The performance of the matheuristic approach is first tested on a real-life application study for the towel radiator box products of a manufacturing company in Turkey. Thereafter, a comprehensive computational study is also performed. Computational results have demonstrated that the proposed approach can generate promising results, which necessitate minimal stock diversity and impose minimum waste costs when compared to the existing cutting and purchasing plans of the company as well as the results of some greedy search heuristics embedded in the proposed MILP model.

关键词： Stock selection and sizing problem Matheuristic mixed-integer linear programming Simulated annealing Corrugated carton box production industry

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A Case Study of Smart Industry in Uruguay: Grain Production Facility Optimization 4th

A Case Study of Smart Industry in Uruguay: Grain Production ...

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4th Ibero-American Congress on Smart Cities (ICSC-CITIES)

作者： Baya, Gabriel Sartor, Pablo Robledo, Franco Canale, Eduardo Nesmachnow, Sergio Univ Republica Montevideo Uruguay Univ Montevideo Montevideo Uruguay

ISBN: (纸本)9783030967536;9783030967529

This article presents a mixed-integer linear programming model for cost optimization in multi-product multi-line production scheduling. The proposed model applies discrete time windows and includes realistic constraints. The model is validated on a specific case study from a real Uruguayan grain production facility. Results of the evaluation indicate that the proposed model improves over the current ad-hoc heuristic planning, reducing up to 10.4% the overall production costs.

关键词： Grain facility optimization Smart industry mixed-integer linear programming Real case study

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Collaborative Optimization of Wind-Solar-Storage Configuration in County Distribution Network Considering Near-Zero Carbon Emission

Collaborative Optimization of Wind-Solar-Storage Configurati...

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58th IEEE/IAS Industrial and Commercial Power Systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Yang, Lu Peng, Dong Liu, Hongyang Cheng, Yu Guo, Ning North China Elect Power Univ Elect & Elect Engn Beijing Peoples R China State Grid Elect Power Co Ltd State Grid Econ & Technol Res Inst Co Ltd Beijing Peoples R China State Grid Elect Power Co Ltd State Grid Ningxia Elect Power Ecotech Res Inst Yinchuan Ningxia Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

In order to achieve the goals of "emission peak" and "carbon neutrality", this paper proposes a collaborative optimization method of renewable energy and energy storage capacity for the construction of carbon-free county distribution networks, considering the complementary characteristics of wind and solar energy. Considering the uncertainty of renewable energy output, a multi-scenario collaborative stochastic optimization model of renewable energy and energy storage planning is established with the zero-carbon vision of county distribution network. Aiming at maximizing the net benefit of the wind-solar-storage configuration in a zero-carbon energy supply county system, the model optimizes the proportion structure of wind and solar energy sources as well as the corresponding energy storage capacity, taking the zero-carbon emission constraints into account. For solving the problem, the collaborative optimization model is converted into a mixed-integer linear programming model. Focusing on a county distribution grid in northern China, the impact of energy storage configuration and cascade utilization on the capacity configuration of various resources and the economic performance of the investment scheme in the zero-carbon wind-solar-storage energy supply system are analyzed respectively in case studies.

关键词： near-zero carbon emission renewable energy and energy storage configuration multi-scenario optimization mixed-integer linear programming

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A Centralized Home Energy Management System to Minimize Consumer's Electricity Bill

A Centralized Home Energy Management System to Minimize Cons...

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IEEE International Conference on Environment and Electrical Engineering (EEEIC) / IEEE Industrial and Commercial Power Systems Europe (I and CPS Europe) Conference

作者： Nezhad, Ali Esmaeel Nardelli, Pedro H. J. Sahoo, Subham Ghanavati, Farideh Osorio, Gerardo J. LUT Univ Dept Elect Engn Sch Energy Syst Lappeenranta 53850 Finland Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Univ Aveiro Dept Ind Engn & Management Aveiro Portugal Univ Portucalense Infante Henrique UPT P-4200072 Porto Portugal

ISBN: (纸本)9781665485371

This paper investigates a centralized, high-resolution and fast model for home energy management. The model is provided within mixed-integer linear programming (MILP) framework while it benefits from an open-access optimization model in Python for running the model free of charge. To minimize the electricity bill, the time-of-use (TOU) electricity tariff has been selected by the consumer to manage the daily electricity consumption. This consumer-centric home energy management system (HEMS) enhances the flexibility that can be provided by the dedicated consumers during peak periods while reducing the electricity bill of the end-users benefiting from the TOU tariff. The time resolution of home appliance scheduling is 15 minutes in this study and it is compatible with the smart metering data recording for energy consumed by the end-users. The simulation results show that the electricity bill would be considerably decreased by using the proposed self-scheduling model.

关键词： Time-of-Use Tariff Home Energy Management System Demand Response mixed-integer linear programming Python

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Generalized Additive Modeling of Building Inertia Thermal Energy Storage for Integration Into Smart Grid Control

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IEEE ACCESS 2021年 9卷 71699-71711页

作者： Voss, Marcus Heinekamp, Jan F. Krutzsch, Sabine Sick, Friedrich Albayrak, Sahin Strunz, Kai Tech Univ Berlin Distributed Artificial Intelligence Lab DAI Lab D-10587 Berlin Germany Tech Univ Berlin Chair Sustainable Elect Networks & Sources Energy D-10587 Berlin Germany Hsch Tech & Wirtschaft HTW Berlin Sch Engn Energy & Informat D-12459 Berlin Germany

The structural mass of a building provides inherent thermal storage capability. Through sector coupling, the building mass can provide additional flexibility to the electric power system, using, for instance, combined heat and power plants or power-to-heat. In this work, a mathematical model of building inertia thermal energy storage (BITES) for integration into optimized smart grid control is introduced. It is shown how necessary model parameters can be obtained using generalized additive modeling (GAM) based on measurable building data. For this purpose, it is demonstrated that the ceiling surface temperature can serve as a proxy for the current state of energy. This allows for real-world implementation using only temperature sensors as additionally required hardware. Compared with linear modeling, GAM enable improved modeling of the nonlinear characteristics and interactions of external factors influencing the storage operation. Two case studies demonstrate the potential of using building storage as part of a virtual power plant (VPP) for optimized smart grid control. In the first case study, BITES is compared with conventionally used hot water tanks, revealing economic benefits for both the VPP and building operator. The second case study investigates the potential for savings in CO2 emission and grid connection capacity. It shows similar benefits when using BITES compared to using battery storage, without the need for hardware investment. Given the ubiquity of buildings and the recent advances in building control systems, BITES offers great potential as an additional source of flexibility to the low-carbon energy systems of the future.

关键词： Buildings Thermal energy Cogeneration Mathematical model Power generation Additives Smart grids Building inertia thermal energy storage energy management generalized additive model mixed-integer linear programming sector coupling smart grid virtual power plant

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Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

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INFORMS JOURNAL ON COMPUTING 2021年第2期33卷 739-756页

作者： Xavier, Alinson S. Qiu, Feng Ahmed, Shabbir Argonne Natl Lab Energy Syst Div Argonne IL 60439 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

关键词： security-constrained unit commitment mixed-integer linear programming machine learning

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A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第36期28卷 50636-50648页

作者： Tavana, Madjid Tohidi, Hamid Alimohammadi, Milad Lesansalmasi, Reza La Salle Univ Business Syst & Analyt Dept Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderbom Germany Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran

The location-inventory-routing modeling is an integrated and comprehensive approach to the interconnected location planning, inventory management, and vehicle routing problems in supply chain management. Supplier selection and order allocation are critical operational and strategic decisions in green supply chains. Green supply chain management is an environmental approach to sourcing and production that considers sustainability in every supply chain stage. In this study, a novel bi-objective mixed-integer linear programming model is formulated to solve the location-inventory-routing problems in green supply chains with low-carbon emissions under uncertainty. The proposed model is used for supplier selection and order allocation by considering the location priorities, heterogeneous vehicle routing, storage needs, uncertain demand, and backorder shortage. The formulated bi-objective model is solved with a weighted fuzzy multi-objective solution approach coupled with a novel intelligent simulation algorithm to ensure the feasibility of the solution space. We generate and solve different-sized problems to demonstrate the applicability and efficacy of the proposed model.

关键词： Location-inventory-routing Green supply chain Low-carbon emission mixed-integer linear programming Fuzzy set theory

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Distributionally robust facility location problem under decision-dependent stochastic demand

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期292卷 548-561页

作者： Basciftci, Beste Ahmed, Shabbir Shen, Siqian Sabanci Univ Ind Engn Program Istanbul Turkey Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

While the traditional facility location problem considers exogenous demand, in some applications, locations of facilities could affect the willingness of customers to use certain types of services, e.g., carsharing, and therefore they also affect realizations of random demand. Moreover, a decision maker may not know the exact distribution of such endogenous demand and how it is affected by location choices. In this paper, we consider a distributionally robust facility location problem, in which we interpret the moments of stochastic demand as functions of facility-location decisions. We reformulate a two-stage decision-dependent distributionally robust optimization model as a monolithic formulation, and then derive exact mixed-integer linear programming reformulation as well as valid inequalities when the means and variances of demand are piecewise linear functions of location solutions. We conduct extensive computational studies, in which we compare our model with a decision-dependent deterministic model, as well as stochastic programming and distributionally robust models without the decision-dependent assumption. The results show superior performance of our approach with remarkable improvement in profit and quality of service under various settings, in addition to computational speed-ups given by formulation enhancements. These results draw attention to the need of considering the impact of location decisions on customer demand within this strategic-level planning problem. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Uncertainty modeling Facility location Distributionally robust optimization Decision-dependent uncertainty mixed-integer linear programming

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