scholarly journals Quantifying the Demand Response Potential of Inherent Energy Storages in Production Systems

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4161
Author(s):  
Nina Strobel ◽  
Daniel Fuhrländer-Völker ◽  
Matthias Weigold ◽  
Eberhard Abele
Keyword(s):  
Demand Response ◽  
Production Systems ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Industrial Sector ◽  
Mathematical Framework ◽  
Heterogeneous Structure ◽  
Management Measures ◽  
Metalworking Industry ◽  
Different Types

The increasing share of volatile, renewable energy sources rises the demand for consumers who can shift their electrical power demand in time. In theory, the industrial sector offers great potential here, as it accounts for a large proportion of electricity demand. However, the heterogeneous structure of facilities in factories and the concerns of operators regarding data security and process control often prevent the implementation of demand side management measures in this sector. In order to counteract these obstacles, this paper presents a general mathematical framework for modelling and evaluating different types of inherent energy storages (IES) which typically can be found in industrial production systems. The method can be used to calculate the flexibility potential of the IES in a factory with focus on hysteresis-controlled devices and make the potential visible and usable for power grid stabilization. The method is applied in a typical production line from metalworking industry to provide live monitoring of the current flexibility potential of selected devices.

scholarly journals The smart grid concept applied to an industrial electrical system

2021 ◽  
Vol 12 (4) ◽  
pp. 2140
Author(s):  
Isidro Fraga Hurtado ◽  
Julio Rafael Gómez Sarduy ◽  
Percy Rafael Viego Felipe ◽  
Vladimir Sousa Santos ◽  
Enrique Ciro Quispe Oqueña
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Information And Communication Technologies ◽  
Smart Grids ◽  
Renewable Energy Sources ◽  
Demand Management ◽  
Electrical Power ◽  
Distribution Networks ◽  
Industrial Sector ◽  
Reactive Compensation

Smart grids can be considered as a concept that integrates electrical, automatic control, information, and communication technologies. This concept constitutes a fundamental complement in the integration of renewable energy sources in electrical power systems. Although its application is fundamentally framed in transmission and distribution networks, it could also be implemented in industrial electrical systems. This article aims to analyze the advantages of implementing solutions based on smart grids in the industrial sector. Likewise, the results of its implementation in the large industry in the province of Cienfuegos, Cuba are presented. Specifically, reactive compensation, voltage, and demand management controls were integrated into a Supervision, Control, and Data Acquisition system forming a smart grid. It is shown that, in industries where infrastructure and equipment conditions exist, it is possible to successfully implement solutions with the functionalities and benefits inherent to smart grids.

scholarly journals On the Effects of Infrastructure Investments on Industrial CO2 Emissions in Portugal

2019 ◽  
Vol 5 (1) ◽  
pp. 47
Author(s):  
Alfredo Marvão Pereira ◽  
Rui Marvão Pereira
Keyword(s):  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Economic Effects ◽  
Policy Implications ◽  
Different Types ◽  
Emissions Factor ◽  
Airport Infrastructure ◽  
Emissions Intensity ◽  
Infrastructure Investments

<p><em>We estimate the effects of infrastructure investments on industrial CO<sub>2</sub> emissions in Portugal based on the economic effects of twelve types of infrastructure investments on twenty-two different industries and the industry-specific CO<sub>2</sub> emission factors. Our conclusions are as follows. First, most infrastructure investments help the emissions intensity of the economy. The exceptions are investments in airports and healthcare. Second, the economic effects of the different types of infrastructure investments on the electrical power industry are central in determining the overall effects on emissions. Indeed, electric power accounts for 35% of CO<sub>2</sub> emissions and has extremely high emissions factor. Third, if the emissions from electricity generation were eliminated, most infrastructure investments would still lead to a decline in emissions intensity. Investments in national roads would leave the emissions intensity unchanged while investments in healthcare have adverse effects. There are several important policy implications of these results. Given the present electric generating mix, investment in national roads are appropriate from an environmental perspective, while investments in airport infrastructure are not. Under a scenario of aggressive use of renewable energy sources in electricity generation, however, the best investments would be in railroads and airports, two industries highly dependent on the use of electricity. </em></p>

ELECTRICAL POWER GENERATED FROM AN OPERATING DESKTOP COMPUTER THROUGH WASTE HEAT RECOVERY

10.6036/10150 ◽  
2021 ◽  
Vol 96 (4) ◽  
pp. 345-345
Author(s):  
CARLOS ALBERTO GARCIA RODRIGUEZ ◽  
PEDRO QUINTO DIEZ ◽  
JOSE ALFREDO JIMENEZ BERNAL ◽  
IGNACIO CARVAJAL MARISCAL
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Heat Recovery ◽  
Waste Heat ◽  
Electrical Power ◽  
Industrial Sector ◽  
Total Energy Consumption ◽  
Desktop Computer ◽  
Increase Energy Efficiency ◽  
Different Types

Use of heat recovery systems applied to different industries as a technology to increase energy efficiency [1] is becoming more and more common, one third of the total energy consumption is related to the industrial sector, and of this, about fifty percent is wasted as heat [2]. Increasing use of different types of computers forces us to rethink the possibility of improving their energy efficiency and consequently reducing their energy consumption.

A Review of Uncertainty Handling Techniques in Smart Grid

2018 ◽  
Vol 26 (03) ◽  
pp. 345-378 ◽  
Author(s):  
Pranjal Pragya Verma ◽  
Dipti Srinivasan ◽  
K. S. Swarup ◽  
Rahul Mehta
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Uncertainty Modeling ◽  
Electrical Power System ◽  
Uncertainty Handling ◽  
Modeling Techniques ◽  
Processing Techniques ◽  
Risk Processing

This paper is a review of uncertainty modeling techniques used in smart grid studies. The literature dealing with uncertainty from various sources in smart grid is analyzed and presented. In a modern power grid, the risk may arise due to different reasons; in-termittent renewable energy sources, uncertain consumer reactions on demand response, driving patterns of electric vehicles, etc. The paper has two objectives. First is to bring out the trends in uncertainty handling techniques used in electrical power system problems, and second to introduce the scope of new risk processing techniques with the perspective of recent smart grid issues.

scholarly journals Voltage Stability of Power Systems with Renewable-Energy Inverter-Based Generators: A Review

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 115
Author(s):  
Nasser Hosseinzadeh ◽  
Asma Aziz ◽  
Apel Mahmud ◽  
Ameen Gargoom ◽  
Mahbub Rabbani
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Voltage Stability ◽  
Reactive Power ◽  
Power Grid ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
System Stability ◽  
Special Issue ◽  
Synchronous Generators

The main purpose of developing microgrids (MGs) is to facilitate the integration of renewable energy sources (RESs) into the power grid. RESs are normally connected to the grid via power electronic inverters. As various types of RESs are increasingly being connected to the electrical power grid, power systems of the near future will have more inverter-based generators (IBGs) instead of synchronous machines. Since IBGs have significant differences in their characteristics compared to synchronous generators (SGs), particularly concerning their inertia and capability to provide reactive power, their impacts on the system dynamics are different compared to SGs. In particular, system stability analysis will require new approaches. As such, research is currently being conducted on the stability of power systems with the inclusion of IBGs. This review article is intended to be a preface to the Special Issue on Voltage Stability of Microgrids in Power Systems. It presents a comprehensive review of the literature on voltage stability of power systems with a relatively high percentage of IBGs in the generation mix of the system. As the research is developing rapidly in this field, it is understood that by the time that this article is published, and further in the future, there will be many more new developments in this area. Certainly, other articles in this special issue will highlight some other important aspects of the voltage stability of microgrids.

scholarly journals A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2839
Author(s):  
Artvin-Darien Gonzalez-Abreu ◽  
Miguel Delgado-Prieto ◽  
Roque-Alfredo Osornio-Rios ◽  
Juan-Jose Saucedo-Dorantes ◽  
Rene-de-Jesus Romero-Troncoso
Keyword(s):  
Deep Learning ◽  
Power Quality ◽  
Electrical Power ◽  
Synthetic Data ◽  
Industrial Sector ◽  
Industrial Facilities ◽  
Diagnosis Method ◽  
Neural Network Structure ◽  
Power Quality Disturbances ◽  
Disturbance Detection

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

Responsive Production Systems and Roles that Robots can Play

2009 ◽  
Vol 62-64 ◽  
pp. 275-292
Author(s):  
R.H. Weston
Keyword(s):  
Building Block ◽  
Production System ◽  
World Wide ◽  
Production Systems ◽  
Industrial Robots ◽  
Robotic Systems ◽  
Different Types ◽  
Useful Lifetime

With increased product dynamics world-wide, the average economic lifetime of production systems is falling. Industrial robots are widely assumed to be inherently flexible and therefore that they can function as a programmable building block of response production systems. This paper reviews common capabilities of contemporary industrial robotic systems and investigates their capability to extend the useful lifetime of production system by coping with different types of product dynamic. Also considered are relative capabilities of conventional programmable robots and an emerging generation of programmable and configurable component-based machines.

A MEASUREMENT TO ESTIMATE THE LEVEL OF DIGITAL TRANSFORMATION

10.6036/10173 ◽  
2021 ◽  
Vol 96 (4) ◽  
pp. 335-337
Author(s):  
JUAN AURELIO TAMAYO ◽  
JAVIER GAMERO ROJAS ◽  
JUAN ANTONIO MARTINEZ ROMAN ◽  
MARIA DE LORETO DELGADO GONZALEZ
Keyword(s):  
Social System ◽  
Social Systems ◽  
Industrial Sector ◽  
Digital Transformation ◽  
The Internet ◽  
Transition Stage ◽  
Multiple Parameters ◽  
Units Of Analysis ◽  
Different Types

A measure is proposed to estimate the degree of digital transformation of a social system that could be applied to different units of analysis: the organization, the industrial sector or society. The measure contemplates the existence of classic (non-digital) social systems, a transition stage and the transformation towards the digital social system. An estimate of the percentage of people who have made purchases over the Internet has been used to estimate the degree of digital transformation of different territories, regions and countries included in the INE and Eurostat statistics. The initially proposed function can serve as a basis to include panels of indicators or multiple parameters that diagnose other relevant aspects of society and the economy. In general, the measure could be useful to assess the transition and evolution of different types of systems.

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