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A new mixed-integer linear programming model for rescue path planning in uncertain adversarial environment

COMPUTERS & OPERATIONS RESEARCH 2012年第12期39卷 3420-3430页

作者： Berger, Jean Boukhtouta, Abdeslem Benmoussa, Abdelhamid Kettani, Ossama Def R&D Canada Valcartier Quebec City PQ Canada Def R&D Canada CORA Ottawa ON Canada Modellium Inc Quebec City PQ Canada

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far rely on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model is proposed to solve the discrete rescue path planning problem for a single agent navigating in uncertain adversarial environment. It relies on a novel and simplified mathematical mixed-integer linear programming formulation aimed at minimizing traveled distance and threat exposure. Exploiting a user-defined survivability function approximation and survivability threshold, the approximate model allows constructing a solution providing an adjustable optimality gap interval on the optimal solution. Experimental results show the value of the proposed approach in computing near optimal solutions reasonably fast for various problem instances. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Path planning Constrained shortest path mixed-integer linear programming

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Enhancing power grid resilience to winter storms via generator winterization with equity considerations

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SUSTAINABLE CITIES AND SOCIETY 2024年 114卷

作者： Bilir, Baris Kutanoglu, Erhan Hasenbein, John J. Austgen, Brent Garcia, Manuel Skolfield, J. Kyle Univ Texas Austin Operat Res & Ind Engn Grad Program Austin TX USA Los Alamos Natl Lab Los Alamos NM USA Sandia Natl Labs Livermore CA USA

We develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.

关键词： Equitable community resilience Power grid resilience mixed-integer linear programming Winter storm

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Overview and advancement of power system topology addressing pre- and post-event strategies under abnormal operating conditions

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 40卷

作者： Yaldiz, Abdurahman Gokcek, Tayfur Ates, Yavuz Erdinc, Ozan Yildiz Tech Univ Dept Elect Engn Istanbul Turkiye

The transition towards increased utilization of renewable energy and electric vehicles (EVs), along with the growing use of various other electrical devices, poses challenges to the stable and resilient operation of electric power systems (EPS), especially in the face of natural phenomena associated with climate change. This means that accurate topology and balanced EPS plays a key role to increase the capacity to respond quickly and in a coordinated manner to disaster situations such as cyber-attacks, earthquakes and floods. In this study, a new approach is presented to quickly and accurately detect topology attacks in EPS, thus contributing to making safer and more resilient. The proposed methods provide insights into maintaining uninterrupted electricity service by enabling EPS management through both post- and pre-event operational strategies. This approach is created by identifying faulty points with the obtained topology information and creating microgrid (MG) groups. Machine learning techniques have been integrated into the data intrusion attack detection (DIAD) system, enabling the detection of manipulated or faulty smart meters (SM). Concurrently, a topology identification (TI)-based graph learning algorithm is propounded to determine the exact fault locations before and after the event. For MV region restoration after determining the TI region, a mixed-integer linear programming (MILP) approach is employed to optimize the load restoration process in the MG regions. This approach aims to minimize losses and restore critical loads to their previous state as quickly as possible using flexible and emergency power balancing systems, including grid-support storage systems (GSSs), photovoltaic systems (PVs), electric vehicle charging stations (EVCS), and mobile generators. Moreover, a detailed compilation is presented under the topics of EPS topology, phase identification (PI) and its effect on power system resiliency (PSR), shedding light on the future developme

关键词： Smart grid Phase identification Power system resiliency Grid support storage system Topology identification Optimization mixed-integer linear programming Machine learning

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Energy-efficient train control incorporating inherent reduced-power and hybrid braking characteristics of railway vehicles

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 163卷

作者： Peng, Yang Lu, Shaofeng Chen, Feng Liu, Xiao Tian, Zhongbei South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou Peoples R China Univ Liverpool Sch Elect Engn & Elect Liverpool England Univ Birmingham Sch Elect Elect & Syst Engn Birmingham England

With the increasing awareness of carbon neutrality, the application of energy -efficient train control (EETC) to rail transportation systems continues to attract attention from industry and academia. In many classic EETC studies, train models are commonly simplified with pure regenerative braking and constant power characteristics during high speeds, to simplify the complexity of the model. In this paper, a realistic model incorporating hybrid braking characteristics combining regenerative and mechanical braking, and reduced -power characteristics at high speeds into the EETC problem is proposed to improve control precision of the train and the modeling precision of the energy consumption and time. This study addresses the minimum -time train control (MTTC) problem and EETC problem considering nonlinear traction characteristics based on the mixed -integer linear programming (MILP) method, and nonlinear traction and braking characteristics are approximated via a piecewise linear (PWL) modeling technique. Results indicate that the proposed alternative models achieves some deviations from the realistic model in terms of time, energy consumption, and control strategies. The deviations between models in energy consumption and time accumulate as the number of operating stations increases. Therefore, the choice of an appropriate model depends on the precision requirements of various scenarios. In scenarios demanding higher precision, selecting the proposed realistic model is crucial for more accurate computation of energy consumption and time and for obtaining more precise control strategies.

关键词： Energy-efficient train control Minimum-time train control Reduced-power characteristics Hybrid braking characteristics mixed-integer linear programming Railway vehicles

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Energy management of space-heating systems and grid-connected batteries in smart homes

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ENERGY ECOLOGY AND ENVIRONMENT 2022年第1期7卷 37-50页

作者： Al Essa, Mohammed Jasim M. Univ Kufa Fac Engn POB 21 Najaf Governorate Iraq

Several approaches of energy management systems reduce power consumption of heating demand and electricity storage based on static or dynamic tariffs. However, such methodologies impose uncertainties due to forecasting errors of energy consumption and generation, while evaluating electricity prices. Alternatively, this paper proposes a novel methodology of residential energy management to decrease electricity consumption of space-heating units and grid-connected batteries without incorporating price signals, while maintaining their characteristic operation. The proposed algorithm of energy management develops seasonal calculations of heating load and storage power to achieve energy savings in smart homes based on mixed-integer linear programming, considering photovoltaic electric generation. Power consumption of heating systems is estimated considering heat losses of conduction and ventilation through buildings in addition to other important parameters such as outdoor and indoor temperatures. Charging and discharging patterns of grid-connected batteries are modelled consistent with residential loads. Simulation results show that the proposed algorithm of energy management is able to reduce energy consumption of space-heating loads by 15%, mitigating their environmental impact while keeping their functioning usage. Moreover, the algorithm decreases charging demand of grid-connected batteries by 13%, maintaining their state-of-charge levels between 10 and 90%.

关键词： Energy storage systems Grid-level batteries mixed-integer linear programming Photovoltaics Smart energy management Space heating Thermostatically controlled loads

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An MILP Model for Optimal Placement of Sectionalizing Switches and Tie Lines in Distribution Networks With Complex Topologies

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IEEE TRANSACTIONS ON SMART GRID 2021年第6期12卷 4740-4751页

作者： Jooshaki, Mohammad Karimi-Arpanahi, Sahand Lehtonen, Matti Millar, R. John Fotuhi-Firuzabad, Mahmud Geologian Tutkimuskeskus Circular Econ Solut Unit Espoo 02151 Finland Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia Sharif Univ Technol Elect Engn Dept Tehran Iran

Sectionalizing switches (SSs) and tie lines play essential roles in reducing the duration of customer interruptions in electricity distribution networks. The effectiveness of such assets is strongly influenced by their placement in the grid. Operation of SSs and tie lines is also inherently interdependent. Due to the structural complexities regarding the mathematical modeling of such dependencies, optimization of the planning and operation of switches and tie lines has typically required either leveraging heuristic and metaheuristic approaches or oversimplifying the network topology. To tackle such issues, this paper presents a computationally-efficient model for reliability-oriented concurrent switch and tie line placement in distribution networks with complex topologies. The proposed model can be applied to grids with several tie lines and laterals per feeder, and yields the optimal location of tie lines, type of tie switches, namely manual or remote-controlled, and the location and type of SSs. Being cast as a mixed integer linear programming (MILP) problem, the model can be efficiently solved with guaranteed convergence to global optimality using off-the-shelf optimization software. The efficiency and scalability of the proposed model are demonstrated through implementation on five networks and the outcomes are thoroughly discussed.

关键词： Optimization Reliability Indexes Topology Resource management Network topology Manuals Electricity distribution mixed-integer linear programming optimization reliability switch tie line

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A multi-objective model for publicly funded festival planning

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第0期

作者： Alcada-Almeida, L. Sousa, N. Coutinho-Rodrigues, J. INESC Coimbra P-3030790 Coimbra Portugal Univ Coimbra Fac Econ P-3004512 Coimbra Portugal Univ Aberta Dept Sci & Technol P-1269001 Lisbon Portugal Univ Coimbra Fac Sci & Technol Dept Civil Engn P-3004512 Coimbra Portugal

This article proposes a multi-objective mixed-integer linear programming model to assist event managers in obtaining and evaluating non-dominated solutions to the problem of selecting a daily lineup of shows and activities for a festival - be it cultural, sports, ceremonial or any other kind. The model, which is especially adequate for designing festivals with public funding, has five objectives, relating to financial, logistical and how renowned the festival artists or acts are. It includes support for multiple days, multiple stages and different types of shows, all subject to constraints imposed by the intrinsic nature of the festival itself. The output of the model is a set of optimized daily lineups for the activities that constitute the festival, each corresponding to a particular compromise between the five objectives. The approach is demonstrated with a case study for a 5-day festival, for which non-dominated solutions are derived, presented and discussed. Results show that the model can provide a good variety of solutions while ensuring the persistence of the more desirable shows. The model is a novel decision support tool to assist in designing festival lineups that provide optimal audience experience, a key factor in attracting spectators, tourists and increasing comeback value.

关键词： event and festival planning multi-objective optimization mixed-integer linear programming cultural event management

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An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand

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ENERGY 2024年 290卷

作者： Nayak, Dhyaan Sandeep Misra, Shamik Indian Inst Technol Tirupati Dept Chem Engn Tirupati 517619 Andhra Pradesh India

This paper proposes a novel mathematical optimization model aimed at incorporating demand response strategies in the operational scheduling problem of Electric Vehicle (EV) Battery Swapping Stations (BSS). The primary goal of the model is to minimize the operational costs while accounting for intricacies arising from diverse charging modes, variations in battery capacities, charger availability, different states of charges (SOC) of incoming discharged batteries, time -of -use power prices, power constraints and fluctuating customer demand patterns. Furthermore, the model takes into consideration the effect of faster charging modes on battery health to ensure a sustainable operation. The proposed model is further extended to incorporate the effect of swapping demand uncertainty on the operational decision -making of the BSS, and a two -stage stochastic optimization model with a recourse approach is proposed. The proposed stochastic optimization model is tested with relevant case studies, and the results demonstrate the efficacy of the model in providing a risk -neutral solution by determining the optimal demand satisfaction pattern and calculating the number of additional batteries needed to minimize demand shortfalls across all scenarios. The proposed scheduling framework holds the promise of improving both the efficiency of the BSS operations and customer satisfaction, paving the way for accelerated EV adoption.

关键词： Battery swapping Electric Vehicle Demand response Stochastic optimization Scheduling mixed-integer linear programming

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Function-dependent neural-network-driven state feedback control and self-verification stability for discrete-time nonlinear system

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NEUROCOMPUTING 2024年 609卷

作者： Wang, Jingya Feng, Xiao Yu, Yongbin Wang, Xiangxiang Han, Xinyi Shi, Kaibo Zhong, Shouming Shen, Jiarun Cai, Jingye Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Sichuan Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China

Deep learning significantly impacts neural network controller synthesis. Despite the higher efficiency of deep learning algorithms compared to traditional model-based controller design methods, the performance of these neural network controllers lacks theoretical guarantees. Initially, this paper proposes an architecture to synthesize the state feedback controller and Lyapunov function, called function-dependent neural-network- driven (NN-driven) architecture. The discrete-time nonlinear system dynamics, state feedback controller and Lyapunov function in this architecture are all designed using neural networks, which can improve the efficiency of stability verification and control performance. To realize self-verification of system stability by neural networks, three optimization problems are designed based on Lyapunov stability conditions and training speed constraints, solved using mixed-integer linear programming (MILP) solvers. Additionally, a MILP-based algorithm called Stability Counter-examples Updating Training Set (SCUTS) is proposed to simultaneously train the state feedback controller and Lyapunov function neural networks. Finally, experiments conducted on a second-order discrete-time nonlinear system, an inverted pendulum with NN-driven dynamics and an inverted pendulum with Lagrangian dynamics demonstrate the effectiveness of this work. Experimental results indicate that this work outperforms previous research on synthesizing neural network controllers in both the neural network training speed and the control system stabilization speed.

关键词： Function-dependent Neural-network-driven Self-verification Lyapunov stability mixed-integer linear programming Nonlinear system

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Modeling of a Biomass-Based Energy Production Case Study Using Flexible Inputs with the P-Graph Framework

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ENERGIES 2024年第3期17卷 687页

作者： Eles, Andras Heckl, Istvan Cabezas, Heriberto Univ Pannonia Dept Comp Sci & Syst Technol H-8200 Veszprem Hungary Szecheny Istvan Univ Gyor Albert Kazmer Fac Agr & Food Sci Dept Appl Sustainabil H-9026 Gyor Hungary

In this work, a modeling technique utilizing the P-Graph framework was used for a case study involving biomass-based local energy production. In recent years, distributed energy systems gained attention. These systems aim to satisfy energy supply demands, support the local economy, decrease transportation needs and dependence on imports, and, in general, obtain a more sustainable energy production process. Designing such systems is a challenge, for which novel optimization approaches were developed to help decision making. Previous work used the P-Graph framework to optimize energy production in a small rural area, involving manure, intercrops, grass, and corn silage as inputs and fermenters. Biogas is produced in fermenters, and Combined Heat and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs was applied to model fermenters in the original case study. A new implementation of the original decision problem was made both as a mixed-integer linear programming (MILP) model and as a purely P-Graph model by using the flexible input technique. Both approaches provided the same optimal solution, with a 31% larger profit than the fixed input model.

关键词： P-Graph framework flexible inputs mixed-integer linear programming biomass sustainability

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