scholarly journals Automatic Tariff Controller

2021 ◽  
Vol 9 (VII) ◽  
pp. 3572-3574
Author(s):  
H. R. Kulkarni
Keyword(s):  
Energy Demand ◽  
Electrical Power ◽  
Energy Resource ◽  
Cost Effective ◽  
Management Program ◽  
Load Management ◽  
Demand Growth ◽  
Power Load ◽  
Development Plans ◽  
Dramatic Growth

The growth in the demand for electricity in Libya during the last decade witnessed a dramatic growth in the national's annual residential development has played a major role in boosting the demand for electric power. The domestic sector in Libya already accounts for approximately 39 percent of electricity demand. To meet the projected demand for electrical power to cope with, the development plans, increases in the population and the rising in the living standards, government will have to accomplish new power generating units. Comparing with the high budget of constructing new generating power units, load management system it would be attractive resource that should be seriously considered as an important part of national energy program, where demand growth rate exceeds the supply since it is playing an increasing role around the world as a valuable and cost effective energy resource. Hence, was light projecting on power load management program, for its benefit in reducing the energy demand at peak timeown

A Multi-Agent Machine Learning Framework for Intelligent Energy Demand Management

2011 ◽  
pp. 198-212
Author(s):  
Ying Guo ◽  
Rongxin Li
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Demand Management ◽  
System Stability ◽  
Load Management ◽  
Time Interval ◽  
Learning Framework ◽  
Power Load ◽  
Negative Effect ◽  
Multi Agent

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this chapter the authors explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. The authors apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable. They test the robustness of their algorithm by applying it to two separate systems, one with indirect feedback and one with direct feedback from local load agents. Results show that their method is not only adaptive to multiple systems, but is also able to find the optimal balance between both system stability and energy cost.

A Multi-Agent Machine Learning Framework for Intelligent Energy Demand Management

2012 ◽  
pp. 318-332
Author(s):  
Ying Guo ◽  
Rongxin Li
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Demand Management ◽  
System Stability ◽  
Load Management ◽  
Time Interval ◽  
Learning Framework ◽  
Power Load ◽  
Negative Effect ◽  
Multi Agent

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this chapter the authors explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. The authors apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable. They test the robustness of their algorithm by applying it to two separate systems, one with indirect feedback and one with direct feedback from local load agents. Results show that their method is not only adaptive to multiple systems, but is also able to find the optimal balance between both system stability and energy cost.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

scholarly journals Turbomachinery Engineering and Optimization for 25 MW and 50 MW Compressed Air Energy Storage Systems

1984 ◽  
Author(s):  
Robert Schainker ◽  
Michael Nakhamkin ◽  
John R. Stange ◽  
Louis F. Giannuzzi
Keyword(s):  
Energy Storage ◽  
Power Plant ◽  
Storage Systems ◽  
Cost Effective ◽  
Performance Data ◽  
Compressed Air ◽  
Load Management ◽  
Compressed Air Energy Storage ◽  
Energy Storage Systems ◽  
Equipment Performance

Results of engineering and optimization of 25 MW and 50 MW turbomachinery trains for compressed air energy storage (CAES) power plant application are presented. Submitted by equipment suppliers, proposals are based on the commercially available equipment. Performance data and budget prices indicate that the CAES power plant is one of the most cost effective sources of providing peaking power and load management.

scholarly journals The Physical and Morphological Properties of Kenaf/ Epoxy Fibres in Coating Treatment Process

2019 ◽  
Vol 16 (2) ◽  
pp. 43 ◽  
Author(s):  
Muhammad Mustakim Mohd Ghaztar ◽  
Nik Noor Idayu Nik Ibrahim ◽  
Sarani Zakaria ◽  
Ahmad Zafir Romli
Keyword(s):  
Energy Demand ◽  
Cost Effective ◽  
Treatment Method ◽  
High Energy ◽  
Natural Fibre ◽  
Morphological Properties ◽  
Fibre Coating ◽  
Acceptable Method ◽  
Section Analysis ◽  
Kenaf Fibres

Natural fibre is an economical material that often used in various applications due to its low in density, non-abrasiveness in processing and biodegradable. But, its usage in various applications is still limited due to the low in overall properties. The acceptable method to improve the properties of the fibres is by chemical treatment method that is costly, meticulous process and high energy demand. Thus, a new, simple and cost-effective fibre coating treatment method was developed which was able to improve the physical and morphological properties that open a new path for natural based materials to be used in a more robust application. In this study, the physical and morphological properties of various coated Kenaf fibres were analysed to comprehend the cutting behaviour of coated fibres after subjected to the pulverisation process. The Kenaf fibres were individually immersed in 1:4, 1:5 and 1:6 epoxy to acetone coating solutions prior cured, and pulverised consecutively using 5 mm, 1 mm, 0.5 mm and 0.25 mm mesh sizes aperture. The morphological characteristic was analysed using polarised optical and scanning electron microscope. The result showed that 1:6 coating ratio solution able to effectively coat the fibres’ aspect ratio that forming individual coated fibre which in long length pulverised fibres. Moreover, the low viscous 1:6 solution able to penetrate inside fibre structure that supported by density and fibre cross-section analysis compare to the other solutions. In future, this analysis is crucial to give insight on the coated fibres behaviour after subjected to the mechanical means of cutting process that later relates to the reinforcing mechanism in the composite samples.

Coverage of Non-Metals in the ASME B31.3 Chemical Plant and Petroleum Refinery Piping Code

1992 ◽  
Vol 206 (1) ◽  
pp. 67-72
Author(s):  
W E Short II
Keyword(s):  
United States ◽  
Environmental Protection Agency ◽  
Petroleum Refinery ◽  
Cost Effective ◽  
The United States ◽  
Chemical Plant ◽  
Area Of Interest ◽  
Plastic Materials ◽  
Cost Effective Alternative ◽  
Dramatic Growth

The chemical and petrochemical industries have decades of experience in specifying metallic piping lined with non-metals as a cost effective alternative to high-priced alloy materials of construction for piping in corrosive service. Early on, application of plastic piping was essentially limited to atmospheric chemical sewage service and to above-ground vents and drains. However, applications and usage of plastic piping continue to increase as engineers become more confident in specifying plastic materials and mechanical contractors gain experience with their installation. Non-metallic materials are being developed that are not only corrosion resistant but also have increasingly higher pressure and temperature capabilities. Plastic double-containment piping has experienced tremendous growth for handling hazards and toxic fluids. In the United States, recent dramatic growth of plastic double-containment piping applications has been, to a large extent, for compliance with the Environmental Protection Agency (EPA) regulations of the 1976 Resource Conservation and Recovery Act (RCRA). Related EPA regulatory efforts were accelerated in 1988 by more stringent amendments to this legislation. Industry in the United States must comply with these EPA regulations by December 1998. Plastic piping and metallic piping lined with non-metals have been covered to some extent by the ASME B31.3 Chemical Plant and Petroleum Refinery Piping Code for several years. The distinctive requirements of non-metallic piping and piping lined with non-metals were incorporated into the 1980 edition as a separate Chapter VII, which is dedicated to this growing area of interest in piping. This paper provides an overview of the present coverage of non-metallic piping lined with non-metals in the ASME B31.3 Chemical Plant and Petroleum Refinery Piping Code (1). Some topics that warrant further investigation are presented as well.

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