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Advancing Multi-Energy Hub Design: an Integrated Approach for Optimizing Residential Clusters in High RES Penetration Scenarios

Advancing Multi-Energy Hub Design: an Integrated Approach fo...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Di Somma, Marialaura Jin, Lingkang Papadimitriou, Christina Bianco, Nicola Univ Naples Federico II Dept Ind Engn Naples Italy Eindhoven Univ Technol Elect Engn Dept Eindhoven Netherlands

ISBN: (纸本)9798350386509;9798350386493

With the energy transition underway, the wider penetration of multi-energy hubs (MEHs) is inevitable as they allow for the integration of multiple energy carriers at the local level and especially at the users' side by enhancing flexibility of energy supply and renewables penetration. Thus, addressing their design properly is imperative. While numerous energy modeling software and tools are extensively discussed in the literature, they primarily focus on a single energy carrier, mainly dedicated to the electrical network. In this study, an optimization planning tool capable of analyzing the synergies of various energy carriers is presented. The tool aims to determine the optimal configuration of MEHs with sizes of the chosen technologies based on the modeler's preferences. The tool offers flexibility in modelling a wide range of technologies as potential options for the optimal configuration, and in considering different types of objectives as economic and environmental ones that can be assessed through a multi-objective approach. It is formulated through mixed-integer linear programming in a modular manner, facilitating the easy implementation of new emerging technologies, by enhancing scalability and applicability in real context. To prove the effectiveness of the optimization tool, it is applied for the design of a MEH for a residential building cluster located in Torino (Italy). Different scenarios are analyzed to determine the impact of high levels of renewables penetration on the design of the MEH while guaranteeing the economic sustainability of the solution.

关键词： Optimal design Multi-energy hubs Energy planning Residential clusters mixed-integer linear programming

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Demand Response for a Novel Decentralized Power-To-Methanol Process

Demand Response for a Novel Decentralized Power-To-Methanol ...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Kollmer, Max Vogelbacher, Markus Terzi, Mucahit Singh, Mohit Vidal-Vazquez, Francisco Matthes, Jorg Karlsruhe Inst Technol Inst Automat & Appl Informat D-76344 Eggenstein Leopoldshafen Germany Karlsruhe Inst Technol Inst Micro Proc Engn D-76344 Eggenstein Leopoldshafen Germany ICODOS GmbH D-68163 Mannheim Germany

ISBN: (纸本)9798350386509;9798350386493

Due to the increasing share of renewable energies, the role of demand response and production planning is becoming more important. Power-to-X (PtX) technologies, especially power-to-methanol (PtM), are particularly suitable for storing excess energy, as these processes are highly flexible as they can be ramped up and down quickly. In this work, an optimization framework for the flexibilization of a novel power to methanol process that uses decentralized carbon dioxide point sources is developed. Based on simulation data of the stationary operation of a power to methanol process, a mixed-integer Nonlinear Program (MINLP) considering buffer storages, an electrolyser and a power to methanol plant is constructed. The optimal operation is determined taking into account variable electricity costs, varying renewable electricity supply and various production constraints. It has been shown that a suitable scheduling of the process can achieve cost savings up to twenty percent, depending on the boundary conditions and configuration.

关键词： mixed-integer linear programming

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Job shop scheduling under time-of-use electricity prices 18

Job shop scheduling under time-of-use electricity prices

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18th International Conference on Control Automation Robotics and Vision

作者： Dai, Jingwei Yu, Yaowen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9798331518509;9798331518493

Job shop scheduling is an important problem in the operations of manufacturing systems and has great potential for improving electricity efficiency. This paper studies an energy-aware job shop scheduling problem that aims to minimize the total energy cost and total weighted tardiness by selecting an optimal job schedule, considering operation and other constraints. To efficiently model operation constraints and to consider different machine types, a new set of linear integer constraints is developed. The energy-aware job shop scheduling problem considering time-of-use (TOU) electricity prices is then formulated as a mixed-integer linear programming (MILP) problem and solved using the branch-and-cut method. Numerical results tested against three examples demonstrate the computational efficiency and solution quality of our method.

关键词： mixed-integer linear programming

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Menu Optimization for Assisted Living Facilities 57

Menu Optimization for Assisted Living Facilities

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57th Hawaii International Conference on System Sciences (HICSS)

作者： Kulturel-Konak, Sadan Konak, Abdullah Gavirneni, Nagesh Penn State Berks Reading PA 19610 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9780998133171

The percentage of the US population who live in assisted living facilities is increasing steadily. In many cases, seniors solely rely on meals served in these facilities to meet their nutritional needs. Research shows that most assisted-living elderly residents have deficiencies of several key macro- and micronutrients and an excessive intake of sodium, fat, cholesterol, and saturated fat, considering their age group. This paper focuses on designing healthy menus for assisted living facilities under multiple objectives and complex nutritional constraints. The proposed system considers various factors in the construction of menus, including the USDA healthy eating guidelines, chefs' choices and experiences, diversity of menu items, cost, and residents' preferences with different dietary requirements or diet patterns. The paper's main contribution to earlier work is to include people's behaviors while selecting menu items so that resulting menus can better reflect what might happen when implemented and to increase the feeling of autonomy.

关键词： Menu optimization assisted living facilities mixed-integer linear programming goal programming healthy eating

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Planning of Safe Trajectories in Urban Airspace

Planning of Safe Trajectories in Urban Airspace

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AIAA Aviation Forum

作者： Layer-Reiss, Samuel Kunz, Johanna Bittner, Matthias Holzapfel, Florian Tech Univ Munich Inst Flight Syst Dynam Munich Germany Kempten Univ Appl Sci Allgtiu Res Ctr Kempten Germany Kempten Univ Appl Sci Fac Elect Engn Kempten Germany

ISBN: (数字)9781624107160

ISBN: (纸本)9781624107160

The envisioned concept of Urban Air Mobility (UAM) introduces new challenges for computing safe trajectories. A safe trajectory must ensure that a vertiport to conduct a safety landing is always within reach in the event of a contingency. Moreover, the trajectory must avoid any no-fly zones. Computing optimal trajectories that fulfill both requirements is particularly complicated for areas with numerous no-fly zones and vertiports because the resulting optimization problem involves continuous and discrete variables. As a consequence, the problem cannot be optimized efficiently using collocation- or shooting-based methods. Instead, we propose a linear mixed-integer-based formulation of the problem, which can be solved using mixed-integer linear programming (MILP) solvers. Our approach considers electric Vertical Take-Off and Landing aircraft (eVTOL), which can transition between vertical and wingborne flight. The capabilities of the approach are demonstrated with a two-dimensional example.

关键词： mixed-integer linear programming

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End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty 40

End-to-End Learning for Fair Multiobjective Optimization Und...

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40th Conference on Uncertainty in Artificial Intelligence (UAI)

作者： Dinh, My H. Kotary, James Fioretto, Ferdinando Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize (PtO) paradigm in machine learning aims to maximize downstream decision quality by training the parametric inference model end-to-end with the subsequent constrained optimization. This requires backpropagation through the optimization problem using approximation techniques specific to the problem's form, especially for non-differentiable linear and mixed-integer programs. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives, known for their ability to ensure properties of fairness and robustness in decision models. Through a collection of training techniques and proposed application settings, it shows how the optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.

关键词： mixed-integer linear programming

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The Study of Unit Commitment Based on Adaptive mixed-Variable Particle Swarm Optimization Algorithm 6

The Study of Unit Commitment Based on Adaptive Mixed-Variabl...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： He, Mengru Wang, Yanping Dalian Polytechnic University Da Lian China

ISBN: (纸本)9798350377798

The unit commitment (UC) problem is a crucial type of economic dispatch problem in power systems, and its mathematical model is a complex, large-scale mixed-integer nonlinear problem, making it difficult to obtain a theoretical optimal solution. To address this, an adaptive mixed-variable particle swarm optimization (PSO) algorithm is proposed in this paper, considering the remaining cycle count of the energy storage battery. This approach not only enhances the traditional PSO, which tends to fall into local optima, but also handles continuous, binary, and integer variables effectively. To evaluate the impact of transfer functions on the algorithm, eight different transfer functions are compared. Results from two case studies demonstrate that the proposed algorithm can efficiently solve the UC problem with nonlinear mixed variables, while also being robust. This makes it a feasible method to mitigate the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. © 2024 IEEE.

关键词： mixed-integer linear programming

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Design of sustainable perishable food supply chain network under uncertainty

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OPSEARCH 2025年第1期62卷 406-422页

作者： Souri, Fateme Ghomi, S. M. T. Fatemi Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran 159634311 Iran

Managing a perishable food supply chain is a challenging task because of the short lifetime of the product and the uncertainty of demand. Perishable products require special plans with environmental, social and economic effects. Therefore, the sustainability of food supply chains plays a key role in supply chain efficiency. This paper designs a sustainable perishable food supply chain network under uncertainty. A multi-objective mixed-integer linear programming model is proposed that optimizes total costs, emissions, and shipping time to meet environmental demand. Since the demand parameter is assumed to be uncertain, the two-stage planning optimization approach under the scenario has been used. Also, the method of sum weights has been applied to make the model single-objective, and some tests are performed to select the best coefficients. In the end, deterministic and stochastic objective values compare with the Expected value, Wait and see, and Here and now methods.

关键词： Supply chain Two-stage Perishable food Uncertainly mixed-integer linear programming

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Online Cooperative Optimal Power Scheduling of Multiple Microgrids via Hierarchical Imitation Learning 18

Online Cooperative Optimal Power Scheduling of Multiple Micr...

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IEEE 18th International Conference on Control and Automation (ICCA)

作者： Zhao, Yunyi Gao, Shuhua Wang, Jing Li, Congcong Yu, Ming Xiang, Cheng Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China State Grid Shandong Elect Power Co Metering Ctr Mkt Serv Ctr Jinan 250003 Peoples R China Power Automat Pte Ltd Singapore 119968 Singapore

ISBN: (纸本)9798350354416;9798350354409

This paper looks into the issue of optimal power scheduling of multiple microgrids using hierarchical imitation learning. The system is designed to be a hierarchical learning model towards a two-level microgrid community (MGC) structure. The upper-level MGC agent uses an imitation learning algorithm to schedule exchange power between different microgrids, while lower-level microgrid agents are controlled by individual energy management systems using mixed-integer linear programming (MILP). In this paper, we focus on achieving economic dispatch in a large microgrid community while maintaining the privacy of the local microgrids. A simulation study of hierarchical imitation learning is provided based on an MGC system. Our results show the outstanding performance of the designed algorithm with a cost close to the centralized optimal results, about 10% improvement compared to the offline method, and very fast execution, which would be suitable for online power scheduling.

关键词： Energy management imitation learning mixed-integer linear programming microgrid community

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Air-rail timetable synchronisation: Improving passenger connections in Europe within and across transportation modes

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2024年 115卷

作者： Buire, Clara Marzuoli, Aude Delahaye, Daniel Mongeau, Marcel Univ Toulouse ENAC Toulouse France Georgia Inst Technol North Ave NW Atlanta GA USA

This study addresses the integration of the railway and airline scheduling problems, in order to offer passengers smooth transfers between rail and air. This paper focuses on optimising the air and rail timetables at 18 major European airports including three hubs and their associated train stations. A multimodal passenger demand simulation, using constraint programming and based on real data, is proposed. A typical week, from Monday to Saturday, of December 2019 is analysed. Ten passenger demand simulations are run for each day, resulting in 60 test instances that are publicly released. The air-rail timetable synchronisation is applied to these 60 instances. Three scenarios are tested in which each operator agrees to change its schedule or not. Results show that changing the schedule of only 13% of European flights by 11 min, and half of trains scheduled to stop at the three hubs of 17 min, on average, could increase the number of suitable connections for passengers by 60%. In addition, if both airlines and railway operators adapt their schedules, passenger comfort is improved and operator costs are reduced, even more so than with unilateral changes.

关键词： Air-rail integrated timetable Optimisation Passengers Multimodal demand mixed-integer linear programming Constraint programming

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