Minimizing Peak Power Consumption during Scan Testing: Structural Technique for Don't Care Bits Assignment

2006 ◽  
Author(s):  
N. Badereddine ◽  
P. Girard ◽  
S. Pravossoudovitch ◽  
C. Landrault ◽  
A. Virazel ◽  
...  
Keyword(s):  
Power Consumption ◽  
Peak Power ◽  
Scan Testing ◽  
Peak Power Consumption

scholarly journals Planning of a distributed integrated cooling system in reducing the peak power consumption

2021 ◽  
Vol 7 ◽  
pp. 458-468
Author(s):  
Dongwen Chen ◽  
Xiao Hu ◽  
Yong Li ◽  
Jingcheng Chen ◽  
Ruzhu Wang
Keyword(s):  
Power Consumption ◽  
Peak Power ◽  
Cooling System ◽  
Peak Power Consumption

scholarly journals Application of Predictive Control in Scheduling of Domestic Appliances

2020 ◽  
Vol 10 (5) ◽  
pp. 1627 ◽  
Author(s):  
Himanshu Nagpal ◽  
Andrea Staino ◽  
Biswajit Basu
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Predictive Control ◽  
Peak Power ◽  
Mixed Integer ◽  
Consumption Pattern ◽  
Problem Model ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Peak Power Consumption

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption.

Flow shop scheduling with peak power consumption constraints

2013 ◽  
Vol 206 (1) ◽  
pp. 115-145 ◽  
Author(s):  
Kan Fang ◽  
Nelson A. Uhan ◽  
Fu Zhao ◽  
John W. Sutherland
Keyword(s):  
Power Consumption ◽  
Peak Power ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Shop Scheduling ◽  
Peak Power Consumption ◽  
Consumption Constraints

WEIGHTED AUTOMATA AND REGULAR EXPRESSIONS OVER VALUATION MONOIDS

2011 ◽  
Vol 22 (08) ◽  
pp. 1829-1844 ◽  
Author(s):  
MANFRED DROSTE ◽  
INGMAR MEINECKE
Keyword(s):  
Power Consumption ◽  
Average Cost ◽  
Peak Power ◽  
Regular Expressions ◽  
Infinite Words ◽  
New Class ◽  
Weighted Automata ◽  
Peak Power Consumption ◽  
Weight Structures

Quantitative aspects of systems like consumption of resources, output of goods, or reliability can be modeled by weighted automata. Recently, objectives like the average cost or the longtime peak power consumption of a system have been modeled by weighted automata which are not semiring weighted anymore. Instead, operations like limit superior, limit average, or discounting are used to determine the behavior of these automata. Here, we introduce a new class of weight structures subsuming a range of these models as well as semirings. Our main result shows that such weighted automata and Kleene-type regular expressions are expressively equivalent both for finite and infinite words.

scholarly journals Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4649 ◽  
Author(s):  
Mohammad Reza Zavvar Sabegh ◽  
Chris Bingham
Keyword(s):  
Model Predictive Control ◽  
Power Consumption ◽  
Quadratic Programming ◽  
Predictive Control ◽  
Peak Power ◽  
Peak Load ◽  
Temperature Hysteresis ◽  
Binary Quadratic Programming ◽  
Peak Power Consumption ◽  
Experimental Trials

The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

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