Robust Multi-Layer Energy Management and Control Methodologies for Reefer Container Park in Port Terminal

The full electrification of ports is a promising prospect for saving energy and reducing greenhouse gas emissions. The control scheme of the reefer container is particularly important for the energy management of the port, as the operation of the reefer container is one of the main energy consumers of ports. This paper proposes a reefer container hierarchical control scheme that contains a day-ahead module and intra-day module which is used to generate a rough scheduling strategy based on forecast data and fine-tuning the strategy, respectively. The final strategy should realize the economical operation while ensuring that each reefer container does not exceed the temperature limit during operation. Numerical analysis on the reefer container park with 200 and 850 containers using the Time of Use (TOU) tariff and super-peak tariff is fully analyzed. In the case of 200 containers, the proposed method helps reduce operating costs by about 14.7%, and 18% in the scenario of 850 containers. The proposed method can effectively save container operating costs and ensure that the internal temperature of the container does not exceed the limit while changing the distribution of energy which could help alleviate the peak load problem of the port electric system.

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An Innovation-Based Methodology for HVAC System Fault Detection

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.3140737 ◽

1985 ◽

Vol 107 (4) ◽

pp. 284-289 ◽

Cited By ~ 23

Author(s):

P. B. Usoro ◽

I. C. Schick ◽

S. Negahdaripour

Keyword(s):

Mathematical Model ◽

Control Systems ◽

Energy Management ◽

Air Conditioning ◽

Energy Use ◽

Hvac System ◽

Test Results ◽

Nonlinear Mathematical Model ◽

Control Scheme ◽

And Control

Although Energy Management and Control Systems (EMCS) have since the early 1970’s contributed significantly to the reduction (20-40 percent) of energy use in buildings without sacrificing occupants’ comfort, their full capabilities have not been completely realized. This is in part due to their inability to quickly detect and compensate for failures in the Heating, Ventilation and Air Conditioning (HVAC) system. In fact, no matter how good the control scheme for the HVAC system might be, the presence of undetected faults can completely offset any expected savings. This paper presents a methodology for detecting faults in an HVAC system using a nonlinear mathematical model and an extended Kalman filter. The technique was implemented in a computer program and successfully used to detect “planted” faults in simulations of the air handler unit of an HVAC system. Test results are presented to demonstrate the effectiveness of the methodology.

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ON NONLINEARITY CONTROL OF CNC FEED DRIVES

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2006-0005 ◽

2006 ◽

Vol 30 (1) ◽

pp. 63-80

Author(s):

Rami M. Shahin ◽

Waguih H. ElMaraghy ◽

EISayed M. ElBeheiry

Keyword(s):

Machine Tools ◽

Manufacturing Systems ◽

Hierarchical Control ◽

Control Process ◽

Main Function ◽

Reconfigurable Manufacturing ◽

Feed Drives ◽

Control Scheme ◽

Contouring Accuracy ◽

And Control

A Unified Reconfigurable Open Control Architecture (UROCA) aims at unifying the reconfiguration aspects and managing the interaction amongst the different operating levels of individual machining control systems that are likely to perform in reconfigurable manufacturing systems. The hierarchical control structure of UROCA demands the usage of a supervisory control scheme in order to manage operations of supervisory and servo controllers altogether into a reconfigurable control process. The main function of the supervisory unit is to serve as a switching/reconfiguring logic amongst different available controllers, according to need, in order to maintain motion output within the permitted limits. Due to backlash, efficiency of machine tools will be undesirably turned down causing higher vibrations, lower contouring accuracy, and may draw the whole system into instability region. A Switching control scheme designated to manage the control process where two different controllers with two different control functionalities, acting differently in two vital zones - one of them where the backlash lies, and the other when moving past the backlash - is the main topic of this paper. The proposed switching schemes emphasize a reconfiguration aspect on the control process level for machine tools as perceived, investigated and resolved by the physical and control layers located at the deliberative part of the UROCA architecture.

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A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †

Energies ◽

10.3390/en12061006 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1006 ◽

Cited By ~ 14

Author(s):

Sergio Bruno ◽

Gabriella Dellino ◽

Massimo La Scala ◽

Carlo Meloni

Keyword(s):

Energy Management ◽

Predictive Control ◽

Moving Average ◽

Hierarchical Control ◽

Control Device ◽

Small Scale ◽

Monitoring And Control ◽

Control Functions ◽

The Right ◽

And Control

The paper describes the methodology used for developing an electric load microforecasting module to be integrated in the Energy Management System (EMS) architecture designed and tested within the “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers and prosumers with a monitoring and control device that would enable demand response through optimization of their own distributed energy resources (DERs). The optimal control of resources is organized with a hierarchical control structure and performed in two stages. A cloud-based computation platform provides global control functions based on model predictive control whereas a closed-loop local device manages actual monitoring and control of field components. In this architecture, load forecasts on a small scale (a single residential or tertiary building) are needed as inputs of the predictive control problem. The microforecasting module aimed at providing such inputs was designed to be flexible, adaptive, and able to treat data with low time resolution. The module includes alternative forecasting techniques, such as autoregressive integrated moving average (ARIMA), neural networks, and exponential smoothing, allowing the application of the right forecasting strategy each time. The presented test results are based on a dataset acquired during a monitoring campaign in two pilot systems, installed during the ER Project in public buildings.

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