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Designing railway transit network with first-mile access via ride-sharing autonomous vehicle service

TRAVEL BEHAVIOUR AND SOCIETY 2024年 35卷

作者： Shan, Ali An, Kun Hoang, Nam Hong Vu, Hai L. Monash Univ Inst Transport Studies Dept Civil Engn Melbourne Australia Tongji Univ Coll Transportat Engn 4800 Caoan Rd Shanghai Peoples R China

The rail transit system is an essential component of a sustainable transport system, but its accessibility remains a barrier owing to inadequate transit network coverage. Ride-sharing autonomous vehicle (RAV) service is anticipated to be a potentially viable option to improve the economics of rail-based transportation systems (RTS) by substantially lowering the upfront and ongoing costs. In this study, we model and evaluate the impact of RAVs on integrated RTS + first-mile autonomous vehicle design where the commuters' waiting time at railway stations is capped to ensure the level of quality service for rail. The proposed framework optimizes the RAV fleet size while allowing their relocations to tackle the vehicle imbalance problem in the network to assist operators in optimally and effectively running their services. To this end, this study intends to help the relevant Government agency in their planning, where the RTS extension design and RAV operations are planned concurrently to maximize the coverage area with improved accessibility. The modeling framework is solved using mixed-integer linear programming. We applied our proposed framework to the Doncaster railway extension in Melbourne, Australia, to demonstrate the applicability of our modeling framework. Our findings show that increasing rideshare capacity enhanced the operational performance of the integrated services, increased the percentage of AVs performing ride-sharing, and resulted in a shorter RTS alignment and that the RAV-only service is always a costlier option. Finally, the RTS alignment is found to be robust against the variations in capped commuters' waiting time, the disutility of commuter delays by the RAV service, and changes in headway between train trips.

关键词： Railway transit network design Ride-sharing Shared mobility Dynamic travel demand Vehicle relocations mixed-integer linear programming

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Finding Optimal Diverse Feature Sets with Alternative Feature Selection

arXiv

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arXiv 2023年

作者： Bach, Jakob Karlsruhe Germany

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods typically yield one feature set only, which might not suffice in some scenarios. For example, users might be interested in finding alternative feature sets with similar prediction quality, offering different explanations of the data. In this article, we introduce alternative feature selection and formalize it as an optimization problem. In particular, we define alternatives via constraints and enable users to control the number and dissimilarity of alternatives. We consider sequential as well as simultaneous search for alternatives. Next, we discuss how to integrate conventional feature-selection methods as objectives. In particular, we describe solver-based search methods to tackle the optimization problem. Further, we analyze the complexity of this optimization problem and prove NP-hardness. Additionally, we show that a constant-factor approximation exists under certain conditions and propose corresponding heuristic search methods. Finally, we evaluate alternative feature selection in comprehensive experiments with 30 binary-classification datasets. We observe that alternative feature sets may indeed have high prediction quality, and we analyze factors influencing this outcome. © 2023, CC BY.

关键词： mixed-integer linear programming

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Shelter Location-Allocation Problem with Vulnerabilities of Network and Disruption of Shelter During the Response Phase of Disaster

Shelter Location-Allocation Problem with Vulnerabilities of ...

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International-Federation-of-Information-Processing-Working-Group-5.7 (IFIP WG 5.7) International Conference on Advances in Production Management Systems (APMS)

作者： Hansuwa, Sweety Mohan, Usha Ganesan, Viswanath Kumar Indian Inst Technol Madras Dept Management Studies Chennai Tamil Nadu India Tata Consultancy Serv IITM Res Pk Chennai Tamil Nadu India

ISBN: (纸本)9783030859107;9783030859091

In this paper, we define and formulate the shelter location-allocation problem considering both the network vulnerability of the affected area and the shelter's disruption during the disaster management's response phase. We capture the vulnerability metric using the traveling cost and location vulnerability for shelter disruption using the shelter's operating cost. We formulate the problem as a mixed-integer linear programming (MILP) model and present an evacuee-allocation plan considering vulnerable network connectivities between the populated areas and the shelter locations. We finally apply and solve the problem using real-life case data obtained during the Nepal earthquake in 2015 and compare our models with Rahman's same data [10]. We demonstrate using the case example the usefulness of our modelling approach and show that we can achieve better results compared to a simulation study.

关键词： Shelter location-allocation mixed-integer linear programming Earthquake

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Unit commitment for multi-terminal VSC-connected AC systems including BESS facilities with energy time-shifting strategy

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 134卷 107367-107367页

作者： Guzman-Feria, Juan S. Castro, Luis M. Tovar-Hernandez, J. H. Gonzalez-Cabrera, N. Gutierrez-Alcaraz, G. Natl Autonomous Univ Mexico UNAM Posgrad Ingn Elect Mexico City DF Mexico Natl Autonomous Univ Mexico UNAM Dept Elect Energy Mexico City DF Mexico Inst Tecnol Morelia ITM Res & Postgrad Program Elect Engn Mexico City DF Mexico

This paper proposes a novel modelling framework for Unit Commitment (UC) studies in multi-terminal VSCconnected AC grids. Battery Energy Storage Systems (BESS) are also considered with an energy time-shifting strategy whose charge and discharge modes are defined within the 24-hour planning horizon to promote a high competition level of energy trading in the power grid. The entire transmission network is formulated by shift factors and power losses of AC/DC branches and VSC units are included by piecewise linear functions. Thus, this novel UC approach retains a mixed-integer linear programming model with a high modelling versatility for arbitrary hybrid power grids accommodating any number of VSC stations and BESS units. The method described in this paper enables the optimal generation scheduling combined with the charge and discharge BESS modes. Its applicability is confirmed using two case studies, one featuring a four-terminal VSCHVDC system with two BESS, and another characterised by seven VSC-connected AC systems with five BESS. To validate the proposed method, results are compared against those furnished by the classic UC representing the network through nodal equations. It is concluded that both methods favourably agree with each other as the errors are inferior to 2%. The usefulness of the proposed method to the real-time operation of AC/DC grids with BESS facilities is valuable.

关键词： AC/DC networks Battery energy storage systems mixed-integer linear programming Shift factors Unit commitment VSC stations

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Optimal scheduling of a multiproduct batch chemical plant with preemptive changeover tasks

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COMPUTERS & CHEMICAL ENGINEERING 2022年 162卷

作者： Castro, Pedro M. Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentais & Invest Oper P-1749016 Lisbon Portugal

Most short-term scheduling formulations in Process Systems Engineering assume a time horizon with-out interruptions. However, local working patterns may prevent execution of activities at nights or dur -ing weekends. One such problem is reported in Egli and Rippin (1986) and deals with a multiproduct batch chemical plant featuring recipes with stable and unstable intermediates, shared resources with limited availability of electricity and steam, non-instantaneous transfer times, and the possibility of inter-rupting changeover tasks when encountering a pre-defined break period. The objective function balances sequence-dependent changeovers with product holding costs. In this paper, we model these features with a Resource-Task Network (RTN) formulation based on a discrete-time representation. After solving the original problem to optimality for savings of 5%, we evaluate the alternative of forcing complete exe-cution of changeover tasks before or after a break, for a couple more scenarios. The results show that preemption is always beneficial, with improvements ranging from 0.57% to 36.4%.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Preemptive scheduling mixed-integer linear programming Multipurpose equipment Utility demand constraints Buffer stock

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Optimal schedule generation for single-channel crude transfer using a multi-model approach

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COMPUTERS & CHEMICAL ENGINEERING 2022年 160卷 107732-107732页

作者： Paranjape, Aditya A. Baranwal, Mayank Wagle, Satyavrat Lotti, Rushi Majumder, Sushanta Bulliere, Anne-Laure Imperial Coll London Dept Aeronaut London England Tata Consultancy Serv Ltd Tata Consultancy Serv Res Mumbai Maharashtra India Tata Consultancy Serv Ltd Engn & Ind Serv Mumbai Maharashtra India TotalEnergies Courbevoie France

This paper presents three techniques for scheduling for crude transfer between a port and a refinery on a single pipeline in the presence of stringent flow constraints. The three techniques are based on metaheuristics (business rules), mixed integer linear programming and reinforcement learning. In addition to comparing the three techniques, we also demonstrate how knowledge gleaned from one technique (in our case, the metaheuristics) can be used to design an algorithm based on another technique (in our case, reinforcement learning). A novel feature of our approach to reinforcement learning, in particular, is the use of low-fidelity, reduced-order simulators for training the scheduler and supporting it with a post-processor based either on business rules or on integer programming for ensuring compatibility with the constraints. The set of constraints considered here includes temporal restrictions on the use of the pipeline, flow constraints in the tanks that feed the column distillation units in the refinery, and the need to ensure a certain minimum residence time for crude in a given tank for dewatering. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Crude transfer scheduling Reinforcement learning Optimal control mixed-integer linear programming

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Continuous Piecewise linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

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ENERGIES 2022年第5期15卷 1699页

作者： Abreu, David Lucas dos Santos Finardi, Erlon Cristian Univ Fed Santa Catarina Elect Energy Syst Planning Lab LabPlan BR-88040900 Florianopolis SC Brazil Inst Syst & Comp Engn Res & Dev Brazil INESC P&D BR-11055300 Santos SP Brazil

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF dependent only on the turbined outflow. Basically, in both PWL models, offline, mixed-integer linear (or quadratic) programming techniques are used with an optimized pre-selection of the original HPF dataset obtained through the Ramer-Douglas-Peucker algorithm. As a highlight, the framework allows the control of the number of hyperplanes and, consequently, the number of variables and constraints of the PWL model. To this end, we offer two possibilities: (i) minimizing the error for a fixed number of hyperplanes, or (ii) minimizing the number of hyperplanes for a given error. We assessed the performance of the proposed framework using data from two large hydropower plants of the Brazilian system. The first has 3568 MW distributed in 50 Bulb-type GUs and operates as a run-of-river hydro plant. In turn, the second, which can vary the reservoir volume by up to 1000 hm(3), possesses 1140 MW distributed in three Francis-type units. The results showed a variation from 0.040% to 1.583% in term

关键词： hydro production function piecewise linear model mixed-integer linear programming

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The multi-stage framework for optimal sizing and operation of hybrid electrical-thermal energy storage system

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ENERGY 2022年第0期245卷 123248-123248页

作者： He, Yi Guo, Su Zhou, Jianxu Song, Guotao Kurban, Aynur Wang, Haowei Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211000 Peoples R China

In order to mitigate the intermittency of renewable energy, the paper proposes a hybrid electrical thermal energy storage system, which complementarily utilizes the cost-effective two-tank direct molten salt thermal energy storage system and the flexible lead-acid battery. A multi-stage framework is further proposed for optimal sizing and operation of the hybrid energy storage system. First, the renewable energy capacity is optimized considering the minimization of total net load. Then, the typical net load profiles are selected by K-means clustering algorithm. Finally, a bi-level optimization model considers the minimization of levelized cost of storage (LCOS) and power deviation to optimize the sizing and operation strategy of the hybrid energy storage system, and the optimization problem is solved by meta-heuristic algorithm and mixed-integer linear programming. The case studies show that: (1) the hybrid energy storage system is more reliable than single thermal energy storage and more cost-effective than single battery;(2) the multi-stage framework outperforms the commonly-used rule-based operation strategy;(3) demand response strategy can effectively reduce the investment cost of the proposed system. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Hybrid energy storage system Sizing and operation co-optimization Levelized cost of storage Particle swarm optimization mixed-integer linear programming Demand response

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Data-driven energy management of isolated power systems under rapidly varying operating conditions

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APPLIED ENERGY 2022年 314卷

作者： Chapaloglou, Spyridon Varagnolo, Damiano Marra, Francesco Tedeschi, Elisabetta Norwegian Univ Sci & Technol Dept Elect Power Engn Os Bragstads Plass 2 E N-7034 Trondheim Norway Norwegian Univ Sci & Technol Dept Engn Cybernet Os Bragstads Plass 2 E N-7034 Trondheim Norway Equinor R&T Elect Technol Dept Arkitekt Ebbels 10 N-7005 Trondheim Norway Univ Trento Dept Ind Engn Via Sommar 9 I-38123 Povo Italy Univ Padua Dept Informat Engn Via 8 Febbraio2 I-35122 Padua Italy

We propose an energy management algorithm for isolated industrial power systems that integrate uncertain renewable generation and energy storage. The proposed strategy is designed to ensure sustainable and costeffective operations by managing the energy flows in the grid, and is structured so to cope with: (1) high levels of renewable power penetration, and (2) load profiles characterized by non-smooth patterns and irregular events (i.e., events such as those occurring from connections/disconnections of large scale equipment, or from large wind speed ramps). The proposed algorithm leverages a stochastic economic model predictive control (MPC) scheme capable of dealing simultaneously with the dispatch and scheduling of the local generation units. More precisely, the scheme embeds a mixed-integer linear programming (MILP) optimal control policy formulation together with a stochastic programming approach. Moreover, the optimization problem accounts for multiple techno-economical objectives, such as minimization of operational costs, battery degradation, and non-utilized energy. We test the algorithm on a case study of an isolated offshore Oil & Gas platform producing energy onsite with conventional gas turbines and a local wind farm, while integrating a battery energy storage system. The results show that the proposed approach can issue ensemble predictions that successfully capture the potential irregular variations just by using recent past information of the associated random variable, even when no particular sudden events are anticipated in the near-future (i.e., step changes/trend reversals). In this way, the approach provides useful future information for the optimal management of the grid. This effect is numerically quantified via simulations that compare the performance of the proposed stochastic optimization approach against its deterministic MPC version in several realistic operating conditions. The empirical results suggest that the stochastic version leads

关键词： Stochastic model predictive control Quantile regression Random forests Isolated power systems Offshore wind power Energy storage mixed-integer linear programming Economic MPC

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System requirements and optimization of multi-chillers district cooling plants

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ENERGY 2022年第0期246卷 123349-123349页

作者： Ismaen, Rabah El Mekkawy, Tarek Y. Pokharel, Shaligram Al-Salem, Mohammed Qatar Univ Dept Mech & Ind Engn Doha Qatar

District cooling systems (DCS) are particularly important in the Middle East due to higher overall temperatures on most days of the year. The design, operation, and control of a new DCS should consider the abundant stakeholders and system requirements transformation using the system engineering processes. Integrating these requirements at an early stage in the system model is essential, specifically, the regulation on energy efficiency or system reliability;without such integration, the system may miss some important aspects and lead to higher energy consumption, lower reliability, higher system cost, greater CO2 emissions and may not satisfy cooling demand of the stakeholders. In this paper, a frame-work for DCS analysis is proposed by considering inputs, process (core), and outputs. The framework considers the structure and behavior of the DCS system to enhance system design and operation. The core of the framework uses a mathematical model in mixed-integer linear programming (MILP) to optimize the overall DCS cost by integrating the system and stakeholders' requirements. The results obtained from the application of the framework show that addressing the requirements reduces cost and increases energy efficiency, and when the cooling demand is variable, it might be better to have multiple capacities for chillers and chiller storage. The paper is helpful for the decision-makers to understand the impact of requirements and their management in the design, operation, and control of multi-chiller DCS in relief with the cost and energy efficiency. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.

关键词： Multi-chiller plant District cooling system Stakeholders' requirements mixed-integer linear programming Chiller design and control Model -based system engineering

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