Energy Demand Response of Process Systems through Production Scheduling and Control

For a production system, 'scheduling' aims to find out which machine/worker processes which job at what time to produce the best result for user-set objectives, such as minimising the total cost. Finding the optimal solution to a large scheduling problem, however, is extremely time consuming due to the high complexity. To reduce this time to one instance, Dr Wei Weng, from the Institute of Liberal Arts and Science, Kanazawa University in Japan, is leading research projects on developing online scheduling and control systems that provide near-optimal solutions in real time, even for large production systems. In her system, a large scheduling problem will be solved as distributed small problems and information of jobs and machines is collected online to provide results instantly. This will bring two big changes: 1. Large scheduling problems, for which it tends to take days to reach the optimal solution, will be solved instantly by reaching near-optimal solutions; 2. Rescheduling, which is still difficult to be made in real time by optimization algorithms, will be completed instantly in case some urgent jobs arrive or some scheduled jobs need to be changed or cancelled during production. The projects have great potential in raising efficiency of scheduling and production control in future smart industry and enabling achieving lower costs, higher productivity and better customer service.

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Auswirkungen restriktiver Ernährung auf die Wachstumsleistung von Ziegenlämmern

Archives Animal Breeding ◽

10.5194/aab-42-281-1999 ◽

1999 ◽

Vol 42 (3) ◽

pp. 281-294

Author(s):

Y. Toukourou ◽

K.-J. Peters

Keyword(s):

Growth Performance ◽

Energy Demand ◽

Daily Weight Gain ◽

Feed Restriction ◽

Control Group ◽

Control Groups ◽

Experimental Animals ◽

Clear Cut ◽

And Control ◽

Different Levels

Abstract. Title of the paper: Impaet of feed restriction on the growth performance of goat kids The influence of differential feeding levels on growth performance in 72 goat kids "Bunte Deutsche Edelziege" during the pre-weaning period was examined. The 72 animals were assigned to a control group and two experimental groups that received respectively 20% and 40% less milk/less concentrate compared to the control (fed at 2.4 times energy demand for maintenance). The experimental gained animals significantly less relative to the control group. However, during the subsequent realimentation period when all animals were fed at a energy level of 2.4 times maintenance same treatment, the daily weight gain among the kids was in inverse proportion to the level ofmilk deprivation in the pre-weaning phase. The rapid growth among the experimental animals was such that the initial differences in body weight between the experimental and control groups were fully compensated. Growth performance of kids with respect to different levels of concentrated feed was less clear cut and d.ffered significantly only behveen the group that received the lowest feed level relative to all the other groups.

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An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

Energy & Environment ◽

10.1177/0958305x18787259 ◽

2018 ◽

Vol 30 (1) ◽

pp. 63-80 ◽

Cited By ~ 10

Author(s):

Paraskevas Panagiotidis ◽

Andrew Effraimis ◽

George A Xydis

Keyword(s):

Demand Response ◽

Energy Demand ◽

Moving Average ◽

Electricity Consumption ◽

End Users ◽

Market Model ◽

Residential Sector ◽

Response Strategies ◽

System Operator ◽

Modelling Methodology

The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

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