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Economic design framework of microalga-based biodiesel supply chains under uncertainties in CO2 emission and diesel demand

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COMPUTERS & CHEMICAL ENGINEERING 2021年 155卷 107538-107538页

作者： Ahn, Yuchan Kim, Junghwan Korea Inst Ind Technol Green Mat & Proc R&D Grp Ulsan 44413 South Korea Keimyung Univ Dept Chem Engn Daegu 42601 South Korea

To determine the optimal configuration of supply chain network for microalga-based biodiesel (MADSCN) considering uncertain values which influence the optimal strategies of microalga-based biodiesel production, we develop a two-stage stochastic model that considers the CO2 supply and biodiesel demand as uncertain parameters, which simultaneously influence the potential locations for the construction of carbon-capture-and-storage systems to supply CO2 as feedstock, and of biorefineries to produce and distribute the biodiesel as to regions in which it is in demand. The proposed framework is formulated using mixed-integer linear programming to identify the optimal MADSCN configuration that minimizes the overall cost of microalga-based biodiesel production. Variation of both uncertain factors in the stochastic model affect the configurations and operating strategies of MADSCN compared to the deterministic model that fixes the factors. The optimal superstructure of MADSCN was affected more significantly by uncertain biodiesel demand than by uncertain CO2 emission. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Microalgae mixed-integer linear programming Optimization Uncertainty Carbon dioxide Biodiesel

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Integration of Building Inertia Thermal Energy Storage into Smart Grid Control 3

Integration of Building Inertia Thermal Energy Storage into ...

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3rd International Conference on Smart Energy Systems and Technologies (SEST)

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

ISBN: (纸本)9781728147017

A building's structural mass does provide inherent thermal storage capabilities. Through the application of sector coupling energy resources, e.g., combined heat and power plants or power-to-heat, the building mass can provide flexibility to the electric power system. Within this work, a mathematical model of a building inertia thermal energy storage is proposed to allow integration into optimized smart grid control for real-world applications. It is shown how necessary model parameters can be obtained using multiple linear regression based on measurable building data. Thereby, the nonlinear characteristics of external conditions influencing the storage operation, such as outside temperature and solar irradiation, are transformed into weighted linear factors. The possibility of the ceiling surface temperature serving as a proxy for measuring the current state of energy is demonstrated. This allows real-world implementation using minimal additional hardware. Within a case study, the potential of using building storage in optimized smart grid control with sector coupling assets of a Virtual Power Plant is analyzed. The utilization of building storage compared to conventionally used hot water tanks is presented, and cost benefits for both Virtual Power Plant and building operator are shown.

关键词： Building Inertia Thermal Energy Storage Energy Management mixed-integer linear programming Sector Coupling Smart Grid Virtual Power Plant

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Optimal Energy Bidding for the Italian Balancing Service by EVs: a Sensitivity Analysis

Optimal Energy Bidding for the Italian Balancing Service by ...

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20th IEEE International Conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power Systems Europe Conference (I and CPS Europe)

作者： Canevese, Silvia Gatti, Antonio Ric Sul Sistema Energet RSE SpA Energy Syst Dev Dept Milan Italy

ISBN: (纸本)9781728174556

Thanks to their flexible power response, Battery Energy Storage Systems (BESSs) have gained attention as possible new suppliers of Ancillary Services (ASs) to the power system. BESSs in Electric Vehicles (EVs), in particular, could supply ASs during EV recharge. Here, some evaluations of how profitable the Balancing Service (BS) could be for the EV owner are carried out: an optimization problem is set up, returning the energy to be bid for the service on the Italian Balancing Market (BM) to minimize the cost of recharging the desired amount of energy;the bid prices and the bid acceptance/rejection status are assumed as known input values;hourly and quarter-of-an-hourly bids are considered, based, respectively, on the current product resolution on the market and on its foreseen evolution. The simulated case studies are for overnight recharge and involve two kinds of end user, with different values of the maximum recharging power, of the energy to be recharged and of taxes on energy exchanges;both one-directional (V1G) and bi-directional (V2G) recharge are analyzed. Overall annual results show that, if the energy to be recharged is small, moderate savings in the EV electricity bill can be achieved with respect to not supplying the BS;high savings can be achieved if such energy is larger (as in case of a non-domestic user) and V2G recharge is adopted, so that energy selling for the BS is enhanced.

关键词： Balancing Market balancing service battery cycling aging Electric Vehicle cost optimization mixed-integer linear programming V1G V2G

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A Multi-objective Emergency Scheduling Model for Forest Fires with Priority Areas

A Multi-objective Emergency Scheduling Model for Forest Fire...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Wang, Lubing Wu, Peng Chu, Feng Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China

ISBN: (纸本)9781538672204

With global warming, the probability of forest fires is increasing greatly, and more research attention has been paid to forest fires emergency scheduling. This paper addresses a resource-constrained emergency scheduling problem for dealing with forest fires with priority disaster areas. It aims to determine an optimal fire-fighting scheduling plan for multiple forest fire points to minimize the total transport distance and the fire extinguishing rescue time, simultaneously. To effectively solve this problem, we formulate a multi-objective mixed-integer linear programming model, and an iterative and fuzzy logic decision-making based on epsilon-constraint method is designed to obtain a preferred emergency scheduling scheme. Finally, the computational results on benchmark and randomly generate test instances verify the effectiveness and feasibility of the proposed model and method.

关键词： Forest fires emergency scheduling mixed-integer linear programming multi-objective optimization

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A dynamic optimization model for bus schedule design to mitigate the passenger waiting time by dispatching the bus platoon

A dynamic optimization model for bus schedule design to miti...

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American Control Conference (ACC)

作者： Zhang, Yi Su, Rong Zhang, Yicheng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682661

We present a platoon-based bus dispatching strategy and passenger boarding strategy which utilizes a platoon of vehicles to improve capacity flexibility in response to dynamically changing demands, and controls passenger boarding flows to minimize the networkwise passengers' perceived delay time. The released buses in the same platoon are allowed to separate when approaching the stop station, which makes our strategy more flexible and data-driven. A mixed integer linear programming (MILP) model is firstly developed to formulate the problem with the linear cost, in which both the passengers' actual delay time and the operating bus vacancy are minimized subject to the volume dynamic constraints on both buses and stops. With the computational complexity as a concern, the Genetic Algorithm (GA) is adopted to solve the problem in real time. Comparison between MILP and GA on the computational time and result quality is conducted to show the efficiency of our method. Also, the optimization model with the nonlinear cost considering the passengers' perceived delay time and the operating bus vacancy is directly solved by the GA. Finally, the performance of our method and the traditional bus schedule strategies under two different objectives is discussed in the case study, which indicates the potential of the platoon dispatching in mitigating the passenger's perceived delay.

关键词： public transport systems bus scheduling mixed-logic constraints mixed-integer linear programming genetic algorithm

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Optimal Scheduling of a Virtual Power Plant with Demand Response in Short-Term Electricity Market 20

Optimal Scheduling of a Virtual Power Plant with Demand Resp...

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20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON)

作者： Rashidizadeh-Kermani, Homa Shafie-Khah, Miadreza Osorio, Gerardo J. Catalao, Joao P. S. Vahedipour-Dahraie, Mostafa Univ Birjand Birjand Iran Univ Vaasa Vaasa Finland Univ Beira Interior C MAST Covilha Portugal Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152004

This paper presents an optimal bidding and offering strategy for a virtual power plant (VPP), which participates in day-ahead (DA) and balancing markets. The VPP comprises distributed energy resources, plug-in electric vehicles (PEVs) and flexible demands. The objective of the problem is maximizing the VPP's profit while demand response (DR) providers who aggregated the loads try to supply the required demand under their jurisdiction with minimum costs. The proposed optimization problem is formulated as a bi-level stochastic scheduling programming to address uncertainties in DA and balancing electricity prices, renewable energy source's (RES) and DR relationship. Simulation results demonstrate the applicability and effectiveness of the proposed model to any real markets. Also, numerical results show that the flexibility of responsive loads and PEVs can improve the VPP operator's energy management and increase its expected profit.

关键词： Demand response Electricity market mixed-integer linear programming Virtual power plants Stochastic scheduling

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MODELING REAL-TIME APPLICATION PROCESSOR SCHEDULING FOR FOG COMPUTING

MODELING REAL-TIME APPLICATION PROCESSOR SCHEDULING FOR FOG ...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Sharifi, Mani Abhari, Abdolreza Taghipour, Sharareh Ryerson Univ Dept Comp Sci Distributed Syst & Multimedia Proc Lab DSMP Lab Toronto ON Canada Ryerson Univ Mech & Ind Engn Dept Reliabil Risk & Maintenance Res Lab RRMR Lab Toronto ON Canada

ISBN: (纸本)9781565553750

This paper presents a model for fog computing by considering the processing nodes of both edge and cloud devices for real-time applications. We use mixed-integer linear programming (MILP) mathematical model to find the optimal task scheduling and compare it with the performance of the FIFO online scheduling strategies for a fog computing sample that consists of n edge processors (EP) and one cloud processor. The MILP mathematical dispatching strategy optimizes the jobs' scheduling on the EPs and cloud processors. Finally, solving the model and simulation of more scenarios is presented to compare the performance of the optimized job scheduling model with two FIFO scenarios for a real-time application on fog computing. The results show that the FIFO process scheduling strategy's performance is between 62.71% to 95.10% of the optimal jobs' scheduling proposed in this work for the real-time fog computing-based applications.

关键词： Fog computing Real-time processors' scheduling Dispatching strategies mixed-integer linear programming Performance measurement

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Lossless Compression of Deep Neural Networks 17th

Lossless Compression of Deep Neural Networks

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17th International Conference on the Integration of Constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Serra, Thiago Kumar, Abhinav Ramalingam, Srikumar Bucknell Univ Lewisburg PA 17837 USA Univ Utah Salt Lake City UT USA

ISBN: (纸本)9783030589424;9783030589417

Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition, where large neural networks are often used to obtain good accuracy. Consequently, it is challenging to deploy these networks under limited computational resources, such as in mobile devices. In this work, we introduce an algorithm that removes units and layers of a neural network while not changing the output that is produced, which thus implies a lossless compression. This algorithm, which we denote as LEO (Lossless Expressiveness Optimization), relies on mixed-integer linear programming (MILP) to identify Rectified linear Units (ReLUs) with linear behavior over the input domain. By using l(1) regularization to induce such behavior, we can benefit from training over a larger architecture than we would later use in the environment where the trained neural network is deployed.

关键词： Deep learning mixed-integer linear programming Neural network pruning Neuron stability Rectified linear Unit

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Emerging Heuristic Optimization Algorithms for Expansion Planning and Flexibility Optimization in Sustainable Electrical Power Systems

Emerging Heuristic Optimization Algorithms for Expansion Pla...

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International Conference on Power, Control and Communication Infrastructure (ICPCCI)

作者： Sarda, Jigar Pandya, Kartik Shah, Margi CHARUSAT Univ Anand 388421 Gujarat India

ISBN: (纸本)9789811502262;9789811502255

The expansion planning and flexibility optimization of sustainable electrical power systems are facing higher complexity introduced by massive integration of variable renewable generation, the increasing need of facts and HVDC devices for flexibility in highly interactive energy markets, responsive demand and multi-energy sector coupling. Therefore, the expansion and flexibility management problems involved in investments decision-making and operational planning need consideration of more accurate models such as non-linear models, probabilistic models and a large number of decision variables. As the problem is difficult to tackle using classical optimisation tools, metaheuristic methods are depicted to solve it. The paper is based on three benchmark systems to evaluate the feasibility and effectiveness of optimization algorithms in systems of different characteristics and size. Also, the paper presents the results and statistical comparative evaluation of the performance of different emerging heuristic optimization algorithms.

关键词： mixed-integer linear programming Power flow control Transmission expansion planning

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Designing an optimized fueling infrastructure for a hydrogen railway system

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Journal of Rail Transport Planning & Management 2025年 35卷

作者： Alessio Trivella Amina Balha Daniela Guericke Industrial Engineering and Business Information Systems University of Twente Hallenweg 17 7522 NH Enschede Netherlands

Hydrogen use is increasing in transportation, including within the railway sector. In collaboration with a governmental institution in the Netherlands, we study how to design an efficient hydrogen fueling infrastructure for a railway system. The problem involves selecting yards in a network for hydrogen fueling, assigning trains to these yards, locating hydrogen storage and fueling stations, and connecting them via pipelines. This key planning phase must avoid oversizing costly fueling infrastructure while accounting for track availability at yards and costs due to fueling operations. We formulate this novel problem, which has the structure of a nested facility location problem, as a mixed-integer linear program to minimize total annualized investment and operational costs. Due to the complexity of real-sized instances, we propose a matheuristic that estimates the infrastructural costs for each yard and train assignment by combining a constructive algorithm with a set covering model. It then solves a single-stage facility location problem to select yards and assign trains, followed by a yard-level improvement phase. Numerical experiments on a real Dutch case show that our approach delivers high-quality solutions quickly and offer insights into the optimal infrastructure design depending on the discretization of yard areas, number of trains, and other parameters.

关键词： Railway systems Hydrogen trains Fueling infrastructure Facility location problem mixed-integer linear programming Matheuristic

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