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distributive-mixing
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Phase identification of singlephase customers and PV panels via smart Meter data
, Article IEEE Transactions on Smart Grid ; Volume 12, Issue 5 , 2021 , Pages 4543-4552 ; 19493053 (ISSN) ; Safdarian, A ; Aminifar, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
With proliferation of single-phase rooftop photovoltaic (PV) panels, phase balancing in low voltage (LV) distribution feeders becomes the point of concern. In this way, identification of the hosting phase of connected single-phase customers and PV panels is a prerequisite. This paper proposes an optimization model for the phase identification problem. The objective is to minimize the summation of the absolute error between estimated and measured variables. Smart meters (SMs) data including active and reactive power absorptions/injections, nodal voltage magnitudes, and network configuration data form the input of the model. Potential errors in the input data are captured in the model while...
Coordinated power system expansion planning considering the DSO's market operations
, Article IET Generation, Transmission and Distribution ; Volume 13, Issue 21 , 2019 , Pages 4987-4997 ; 17518687 (ISSN) ; Ehsan, M ; Shahidehpour, M ; Fotuhi Firuzabad, M ; Mohammadi Ivatloo, B ; Wang, X ; Sharif University of Technology
Institution of Engineering and Technology
2019
Abstract
The main motivation of this study is to address the challenges due to high penetration of renewable distributed energy resources (DERs) and efficiently benefit from DERs in distribution system planning (DSP). This paper considers a decentralized enhanced platform for DSP which is coordinated with bulk power system planning (PSP) to keep the optimality and security of the whole power system. In the proposed coordinated approach, distribution system operators (DSOs) plan and operate DERs to upgrade their local distribution areas (LDAs), supply forecasting local load growth, and avoid or defer costly generation and transmission expansion planning at the bulk power system. The proposed DSP model...
Comparative analysis of different static mixers performance by CFD technique: An innovative mixer
, Article Chinese Journal of Chemical Engineering ; Volume 28, Issue 3 , 2020 , Pages 672-684 ; Hosseini, S. H ; Rashtchian, D ; Olazar, M ; Sharif University of Technology
Chemical Industry Press
2020
Abstract
The flow and mixing behavior of two miscible liquids has been studied in an innovative static mixer by using CFD, with Reynolds numbers ranging from 20 to 160. The performance of the new mixer is compared with those of Kenics, SMX, and Komax static mixers. The pressure drop ratio (Z-factor), coefficient of variation (CoV), and extensional efficiency (α) features have been used to evaluate power consumption, distributive mixing, and dispersive mixing performances, respectively, in all mixers. The model is firstly validated based on experimental data measured for the pressure drop ratio and the coefficient of variation. CFD results are consistent with measured data and those obtained by...
Predictive-reactive rescheduling for new order arrivals with optimal dynamic pegging
, Article 16th IEEE International Conference on Automation Science and Engineering, CASE 2020, 20 August 2020 through 21 August 2020 ; Volume 2020-August , 8 October , 2020 , Pages 710-715 ; Saitou, K ; Sharif University of Technology
IEEE Computer Society
2020
Abstract
This paper presents a new predictive-reactive rescheduling method for adjusting production schedules in response to the unplanned arrival of new orders in multi-level production. It is based on the concept of dynamic pegging, which enables the reassignment of the Work-In-Progress (WIP) to the existing or newly arrived orders at the time of rescheduling. Extending our previous work on reactive rescheduling with dynamic pegging, the new approach incorporates a probabilistic predictive model of new order arrival in the initial scheduling at the begging of the scheduling horizon. A Mixed Integer Programming (MIP) model is developed for two-phase, predictive-reactive scheduling before and after...