the emerging paradigm shift for analysis of elastic systems is caused by demands of reorganization behavior of systems at run-time. For solution this problem a mathematical framework for graph-based dynamic analysis i...
详细信息
ISBN:
(纸本)9781728159539
the emerging paradigm shift for analysis of elastic systems is caused by demands of reorganization behavior of systems at run-time. For solution this problem a mathematical framework for graph-based dynamic analysis is introduced. the essential of the framework is a definition of the generalized graph-based model of the elastic system. Bond graphs, Fractal Petri nets, fractal graphs, and their matrix presentation are included in the framework and inherit technique of generalized model.
Integrated energy systems can effectively improve the efficiency of energy utilization and promote sustainable development of energy, while meeting the diversified energy demands. In order to deal withthe operation r...
详细信息
Integrated energy systems can effectively improve the efficiency of energy utilization and promote sustainable development of energy, while meeting the diversified energy demands. In order to deal withthe operation risk caused by system uncertainties, the CVaR method was introduced into the system optimization model for risk management. the CVaR model was adopted to solve a multi-objective optimization problem using a two-stage stochastic programming method, where the uncertainty about solar energy and energy demand are considered at the same time. By comparing the optimization results obtained from the CVaR method and from the conventional scenario-based stochastic programming method, the effectiveness of the CVaR method in risk management of integrated energy systems is verified. (C) 2020 the Author(s). Published by Elsevier Ltd.
Cloud Network Slicing (CNS), emerging alongside the 5G mobile network, comprises a paradigm shift in the way networks are provisioned, managed, and operated. Fundamentally, CNS fosters the deployment of a multitude of...
详细信息
ISBN:
(纸本)9781665405225
Cloud Network Slicing (CNS), emerging alongside the 5G mobile network, comprises a paradigm shift in the way networks are provisioned, managed, and operated. Fundamentally, CNS fosters the deployment of a multitude of modern applications, e.g., virtual and augmented reality, 4K video streaming, and autonomous vehicles, which require ultra-low latency, high bandwidth consumption, or both. Slicing promotes the realization of such services through the allocation of computing and network resource bundles, which, as CNS mandates, are isolated from the rest of the network. Typically, such resources are arranged into wide geographical areas (e.g., into multiple countries or even continents), which implies that it is possible to allocate from multiple infrastructure providers. this exacerbates the already challenging problem of maximizing resource allocation efficiency, a feature commonly addressed by CNS architectures. In this respect, we study the optimized embedding of slices across multiple domains. therefore, we account for slices as a collection of computing and network parts. Given specific resource requirements from slice tenants and potentially multiple offers per slice part, we model the problem as a Mixed Integer Linear Program (MILP). We further design two heuristic algorithms in order to mitigate the complex intricacies that would be perceptible in large problem instances. Our evaluation results, based on a simulation environment aligned withthe NECOS slicing architecture, indicate that the MILP approach yields better efficiency c ompared t o b oth h euristics, w ith r espect to client expenditure with a fair amount of performance parameters in an adequate execution time. Our main contribution lies in the optimization methods based on the split and combine approach, integrated into the NECOS CNS architecture.
In the low-carbon optimization scheduling of the grid-connected integrated energy system (IES), elucidating the carbon reduction responsibility of electricity consumers involves considering the electricity indirect ca...
详细信息
ISBN:
(数字)9798350359558
ISBN:
(纸本)9798350359565
In the low-carbon optimization scheduling of the grid-connected integrated energy system (IES), elucidating the carbon reduction responsibility of electricity consumers involves considering the electricity indirect carbon emissions resulting from grid electricity purchases. Nevertheless, based on the carbon emission flow theory, determining the electricity indirect carbon emission intensity at different power system nodes relies on real-time active power flow in the grid, posing challenges for accurate advance predictions. In order to achieve day-ahead robust low-carbon optimization, modeling the electricity indirect carbon emission intensity with budget uncertainty set requires obtaining the predicted values, predicted intervals, and uncertainty budgets. As an artificially defined virtual physical quantity, the modeling process of electricity indirect carbon emission intensity is different from that of the source-load parameters of real physical quantities, so a comparison of the methods for obtaining uncertain parameters for both is necessary. Firstly, the paper analyzes how to obtain the predicted values of electricity indirect carbon emission intensity. Subsequently, an analysis of the variation trend in electricity indirect carbon emission intensity under source-load changes is conducted to determine the prediction methods for the predicted interval. Following that, an analysis of the selection of uncertain budgets is conducted. Finally, conclusions are drawn, clarifying the similarities and differences in the methods of obtaining carbon uncertainty parameters and source-load uncertainty parameters.
In this paper, the problem of clamping force control for the Electromechanical Brake (EMB) system in autonomous vehicles is addressed. A nonlinear model predictive control framework is proposed to solve for the optima...
详细信息
ISBN:
(数字)9798350385724
ISBN:
(纸本)9798350385731
In this paper, the problem of clamping force control for the Electromechanical Brake (EMB) system in autonomous vehicles is addressed. A nonlinear model predictive control framework is proposed to solve for the optimal maneuver at each optimization step to realize accurate clamping force tracking. First, the EMB system is modeled to a state-space representation through the first-principle modeling; Second, a control framework that includes three blocks is proposed to achieve clamping force tracking. the first block designs a clamping force separation strategy and an unknown input observer based on Kalman filter is applied to estimate the clamping force to replace the force sensors; the second block develops an adaptive estimation of polynomial coefficients to deal withthe time-varying coefficients to improve model adaptiveness; the last block conceives the nonlinear model predictive control to track the reference force, and the resulted nonlinear programming is solved by the multiple shooting method. Finally, the proposed framework is validated to achieve faster tracking speed and more accurate tracking in multiple simulation scenarios.
We consider the following time-optimal control problem with state constraints: compute minimal travelling time of a controllable object moving in a prescribed flow field in a bounded domain between two given points. T...
详细信息
ISBN:
(纸本)9781728159539
We consider the following time-optimal control problem with state constraints: compute minimal travelling time of a controllable object moving in a prescribed flow field in a bounded domain between two given points. the optimal control problem is solved numerically using two direct methods interior-point line search filter method and sequential quadratic programming. Five sample flows are considered, and computational properties of the corresponding simulations are measured and discussed.
the problem of designing controllers for achieving composite goals is well studied in the literature and is considered an open problem. In this paper we propose a controller architecture that builds on approaches for ...
详细信息
ISBN:
(纸本)9781728159539
the problem of designing controllers for achieving composite goals is well studied in the literature and is considered an open problem. In this paper we propose a controller architecture that builds on approaches for designing reactive decision making systems developed in the field of computer science. Specifically, we propose an extension of the Behavioural programming (BP) approach that is tailored to allow controller design. As the name of the paper suggests, we identify the issue of "patience" as a key feature of the new approach - in many composite control problems it is not possible to advance all goals at all time, i.e., some goals need to "patiently" wait while others are being advanced. We demonstrate how the proposed approach is capable of handling elaborated multi-objective situations that include patience in an intuitive and effective way.
Response of energy storage units and power plants to a set-point change is not necessarily linear with a constant ramp rate, especially for frequency containment services Which are actuated on a $\leq$ 30s time scale....
详细信息
ISBN:
(数字)9781665479820
ISBN:
(纸本)9781665479837
Response of energy storage units and power plants to a set-point change is not necessarily linear with a constant ramp rate, especially for frequency containment services Which are actuated on a $\leq$ 30s time scale. this is because real systems have time lag in their dynamic behavior Which will cause the output to behave nonlinearly before settling down. this paper shows how to implement this behavior into the linear programming optimization framework using settling time constants of a common energy storage system and power plants. Simulation results show that if linear programming is used to model the units participating in the frequency containment services, neglecting the settling time constraints can lead to decisions Which may not be grid-compliant in practice.
this chapter describes a single-objective, multi-facility, location model for a logistics network, whose aim is to support the economical aspect. In this work, a new variant of the facility location model is presented...
详细信息
ISBN:
(纸本)9783030897437;9783030897420
this chapter describes a single-objective, multi-facility, location model for a logistics network, whose aim is to support the economical aspect. In this work, a new variant of the facility location model is presented to ask the optimum positions of the new facilities withthe target that the aggregate logistics cost from the endure facilities to the new facilities along withthe fixed-charge cost will be reduced. A new approximation approach is incorporated for solving the proposed model for extracting results. An experimental design is consolidated to demonstrate the proficiency and viability of the proposed consideration in connection with reality. the novel contributions of this study have introduced a way to connect the facility location problem and fixed-charge transportation problem using a new approximation approach with minimizing the conveyance cost. the chapter ends with conclusions and perspectives on future studies.
暂无评论