Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit *** from most existing studies that assume deterministic parameters as model inputs,this paper proposes a ...
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Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit *** from most existing studies that assume deterministic parameters as model inputs,this paper proposes a robust energy-efficient train speed profile optimization approach by considering the uncertainty of train modeling ***,we first construct a scenario-based position-time-speed(PTS)network by considering resistance parameters as discrete scenariobased random ***,a percentile reliability model is proposed to generate a robust train speed profile,by which the scenario-based energy consumption is less than the model objective value at a confidence *** solve the model efficiently,we present several algorithms to eliminate the infeasible nodes and arcs in the PTS network and propose a model reformulation strategy to transform the original model into an equivalent linear programming ***,on the basis of our field test data collected in Beijing metro Yizhuang line,a series of experiments are conducted to verify the effectiveness of the model and analyze the influences of parameter uncertainties on the generated train speed profile.
We consider the problem of controlling the charging of electric vehicles (EVs) connected to a single charging station that follows an aggregated power setpoint from a main controller of the local distribution grid. To...
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We consider the problem of controlling the charging of electric vehicles (EVs) connected to a single charging station that follows an aggregated power setpoint from a main controller of the local distribution grid. To cope with volatile resources such as load or distributed generation, this controller manages in real time the flexibility of the energy resources in the distribution grid and uses the charging station to adapt its power consumption. The aggregated power setpoint might exhibit rapid variations due to other volatile resources of the local distribution grid. However, large power jumps and minicycles could increase the EV battery wear. Hence, our first challenge is to properly allocate the powers to EVs so that such fluctuations are not directly absorbed by EV batteries. We assume that EVs are used as flexible loads and that they do not supply the grid. As the EVs have a minimum charging power that cannot be arbitrarily small, and as the rapid fluctuations of the aggregated power setpoint could lead to frequent disconnections and reconnections, the second challenge is to avoid these disconnections and reconnections. The third challenge is to fairly allocate the power in the absence of the information about future EVs arrivals and departures, as this information might be unavailable in practice. To address these challenges, we formulate an online optimization problem and repeatedly solve it by using a mixed-integer-quadratic program. To do so in real time, we develop a heuristic that reduces the number of integer variables. We validate our method by simulations with real-world data.
In this paper, a control scheme based on hybrid model predictive control (HMPC) is presented with two different cost functions for the energy management of a grid-connected microgrid (MG), including a battery storage ...
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ISBN:
(纸本)9781665433655
In this paper, a control scheme based on hybrid model predictive control (HMPC) is presented with two different cost functions for the energy management of a grid-connected microgrid (MG), including a battery storage device, a diesel generator, a photovoltaic system, and critical load. The real-time controller relies on the mixed-logical dynamical (MLD) model of the system and is in charge of computing the operating conditions for each MG component over a 24-hour horizon, with a sampling period of 60 min. An economic cost function is proposed to minimize the operation cost. Furthermore, considering the terminal equality constraint an alternative cost function based on minimization of the battery tracking error is presented. The mentioned constraint is in charge of stability and recursive feasibility of the operation. Moreover, simulation results are provided to show the advantages and disadvantages of both approaches.
Variable renewable energy (VRE) generation changes the shape of residual demand curves, contributing to the high operating costs of conventional generators. Moreover, the variable characteristics of VRE cause a mismat...
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Variable renewable energy (VRE) generation changes the shape of residual demand curves, contributing to the high operating costs of conventional generators. Moreover, the variable characteristics of VRE cause a mismatch between electricity demand and power generation, resulting in a greater expected energy not supplied (EENS) value. EENS involves an expected outage cost, which is one of the important components of power-generation costs. A utility-scale battery energy storage system (BESS) is popularly used to provide ancillary services to mitigate the VRE impact. The general BESS ancillary-service applications are as a spinning reserve, for regulation, and for ramping. A method to determine optimal sizing and the optimal daily-operation schedule of a grid-scale BESS (to compensate for the negative impacts of VRE in terms of operating costs, power-generation-reliability constraints, avoided expected-outage costs, and the installation cost of the BESS) is proposed in this paper. Moreover, the optimal BESS application at a specific time during the day can be selected. The method is based on a multiple-BESS-applications unit-commitment problem (MB-UC), which is solved by mixed-integer programming (MIP). The results show a different period for a BESS to operate at its best value in each application, and more benefits are found when operating the BESS in multiple applications.
Carriers and postal companies are under increasing pressure to reduce their operating costs and increase efficiency. One way to reduce costs is to improve the utilisation of drivers' working hours by employing mor...
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Carriers and postal companies are under increasing pressure to reduce their operating costs and increase efficiency. One way to reduce costs is to improve the utilisation of drivers' working hours by employing more efficient rest break policies. A rest break policy is a restrictive set of rules consistent with national regulations for hours of service. We develop and validate a novel framework to model and analyse a class of these policies that concern the location of the rest breaks. In particular, we compare two representative rest break policies using data from a major Australian postal carrier. The first policy imposes no restriction on the location of a rest break. The second policy requires the driver to return to a depot for rest taking allowing time for socialising and making use of full amenities. Using postal transport data from Sydney metropolitan area, we find that the difference between the two policies in terms of tour length is only over 1%. We further apply the proposed framework to assess the impact of increasing the minimum break time on the two representative policies.
This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled l...
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This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled large companies to have distributed production centers to become widespread, and effective production scheduling between these centers plays a vital role in the competitiveness of companies. To provide effective scheduling for the DPFSP-SDST, we propose a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model, which is presented for the first time in literature to the best of our knowledge. As the CP has become a solid competitor to the MILP in the literature, this study aims to exploit the effectiveness of CP to solve such a complex DPFSP-SDST. Since the problem is NP-hard, we also offer an evolution strategy (ES_en) algorithm that employs a self-adaptive scheme to obtain high-quality solutions in a short time. A ruin-and-recreate procedure is also embedded into the developed ES_en. We calibrate the parameters of the proposed ES_en using a design of experiment approach. We also compare the proposed ES_en algorithm's performance with three state-of-the-art metaheuristic algorithms from the literature, i.e., the IG2S (a variant of an iterated greedy algorithm with NEH2_en initialization), IGR (another variant of an iterated greedy algorithm with a restart scheme), and discrete artificial bee colony (DABC) algorithm. A detailed computational experiment is carried out to evaluate the performance of the mathematical models (MILP and CP) and the heuristic algorithms (ES_en, IG2S, IGR, and DABC). A comprehensive benchmark set is generated for the DPFSP-SDST from the well-known PFSP instances from the literature, considering various combinations of jobs, machines, factories, and SDST settings, resulting in 2880 benchmark instances. For 216 out of 240 small-size instances, optimal results are reported by solving the propose
In this work, we present a new iterative exact solution algorithm for a recently introduced NP-hard sequencing problem. In the problem we are given an upper bound on the allowed solution sequence length and a list of ...
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In this work, we present a new iterative exact solution algorithm for a recently introduced NP-hard sequencing problem. In the problem we are given an upper bound on the allowed solution sequence length and a list of symbols. For each symbol, there is a positive weight and a number, which gives the minimum amount of times the symbol has to occur in a feasible solution sequence. The goal is to find a feasible sequence, which minimizes the maximum weight-distance product, which is calculated for each consecutive appearance of each symbol in the sequence, including the last and first appearance in the sequence, i.e., the sequence is considered to be circular for the calculation of the objective function. Our proposed solution algorithm is based on a new mixed-integer programming model for the problem with a fixed sequence length. We also present various enhancements for our algorithm. We conduct a computational study on the instances from literature to assess the efficiency of our newly proposed solution *** approach solves 404 of 440 instances to optimality within the given time limit, most of them within five minutes. The previous best existing solution approach for the problem only solves 229 of these instances and its exactness depends on an unproven conjecture. Moreover, our approach is up to two orders of magnitude faster compared to this best existing solution approach.
The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where users' preferences and revenue ma...
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The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where users' preferences and revenue management are often overlooked. In this paper, we present a mechanism for accepting/rejecting user requests in a Demand Responsive Transportation (DRT) context based on the representative utilities of alternative transportation modes. We consider utility-maximising users and propose a mixed-integer programming formulation for a Chance Constrained DARP (CC-DARP), that captures users' preferences via a Logit model. We further introduce class-based user groups and consider various pricing structures for DRT services. A customised local search based heuristic and a matheuristic are developed to solve the proposed CC-DARP. We report numerical results for both DARP benchmarking instances and a realistic case study based on New York City yellow taxi trip data. Computational experiments performed on 105 benchmarking instances with up to 96 nodes yield average profit gaps of 2.59% and 0.17% using the proposed local search heuristic and matheuristic, respectively. The results obtained on the realistic case study reveal that a zonal fare structure is the best strategy in terms of optimising revenue and ridership. The proposed CC-DARP formulation provides a new decision-support tool to inform on revenue and fleet management for DRT systems on a strategic planning level.
This paper studies the distributionally robust fair transit resource allocation model (DrFRAM) under the Wasserstein ambiguity set to optimize the public transit resource allocation during a pandemic. We show that the...
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This paper studies the distributionally robust fair transit resource allocation model (DrFRAM) under the Wasserstein ambiguity set to optimize the public transit resource allocation during a pandemic. We show that the proposed DrFRAM is highly nonconvex and nonlinear, and it is NP-hard in general. Fortunately, we show that DrFRAM can be reformulated as a mixedinteger linear programming (MILP) by leveraging the equivalent representation of distributionally robust optimization and monotonicity properties, binarizing integer variables, and linearizing nonconvex terms. To improve the proposed MILP formulation, we derive stronger ones and develop valid inequalities by exploiting the model structures. Additionally, we develop scenario decomposition methods using different MILP formulations to solve the scenario subproblems and introduce a simple yet effective no one left-based approximation algorithm with a provable approximation guarantee to solve the model to near optimality. Finally, we numerically demonstrate the effectiveness of the proposed approaches and apply them to real-world data provided by the Blacksburg Transit.
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