scholarly journals SMART GRID ENERGY CONSERVATION MANAGEMENT BASED ON THE UNIVERSITY'S ENERGY INNOVATION HUB OF KNOWLEDGE

Management ◽  
2022 ◽  
Vol 34 (2) ◽  
pp. 90-102
Author(s):  
Oleksii Volianyk
Keyword(s):  
Smart Grid ◽  
Energy Conservation ◽  
Conservation Management ◽  
Supply And Demand ◽  
Economic Effect ◽  
Self Healing ◽  
Time Data ◽  
Grid Data ◽  
Energy Innovation ◽  
The University

BACKGROUND AND OBJECTIVES. Due to increasing energy costs, as well as strict environmental regulations, there is a growing need for greater resource efficiency, which makes energy-efficient solutions necessary. Thus, the importance of innovations based on technologies designed to save energy, such as the Smart Grid, is increasing. Smart Grid is not just a compilation of smart meters or other electrical devices, it is a series of technologies, a concept of a fully integrated, self-regulating and self-healing power grid, which has a network topology and includes all sources of generation, transmission and distribution, managed by a single network of information and control devices and systems.METHODS. As the main method used was the calculation of the synthetic balance of savings from the use of different types of energy resources by the university after the implementation of the application Smart Grid-energy conservation management on the basis of the university energy-innovation Hub of knowledge.FINDINGS. A mechanism for the implementation of the Smart Grid energy-saving management application on the basis of the university energy-innovation Knowledge Hub is proposed. Smart Grid is designed to provide real-time data on the almost instantaneous balance of energy supply and demand. To ensure grid reliability by reducing peak demands and improving energy efficiency, Smart Grid data management is an affordable and effective tool for data analysis and decision making.CONCLUSION. The results of calculation of the predicted effect of the Smart Grid application implementation for the 4th building of Kyiv National University of Technologies and Design proved that the reduction of installed capacity as a result of the project was 80.5%, i.e. a 1% reduction in capacity creates an economic effect of 0.58% of the costs associated with modernization. Given the current level of electricity consumption, we can predict a potential reduction of 951 thousand UAH per year or almost 50% of the cost of electricity consumed in 2020.

scholarly journals A Technical Review on Self-Healing Control Strategy for Smart Grid Power Systems

2021 ◽  
Vol 1055 (1) ◽  
pp. 012153
Author(s):  
D Sarathkumar ◽  
M Srinivasan ◽  
Albert Alexander Stonier ◽  
Ravi Samikannu ◽  
Narasimha Rao Dasari ◽  
...  
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Control Strategy ◽  
Self Healing ◽  
Technical Review

scholarly journals The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3731 ◽  
Author(s):  
Mohamad El Hariri ◽  
Eric Harmon ◽  
Tarek Youssef ◽  
Mahmoud Saleh ◽  
Hany Habib ◽  
...  
Keyword(s):  
Smart Grid ◽  
Protocol Analysis ◽  
Data Detection ◽  
Detection Accuracy ◽  
Wide Spread ◽  
Grid Data ◽  
Iec 61850 ◽  
Forecasting Error ◽  
Heterogeneous Nature ◽  
Statistical Indicators

The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

A multi-agent based self-healing smart grid

2013 ◽  
Author(s):  
Ruchi Gupta ◽  
Deependra Kumar Jha ◽  
Vinod Kumar Yadav ◽  
Sanjeev Kumar
Keyword(s):  
Smart Grid ◽  
Self Healing ◽  
Agent Based ◽  
Multi Agent

scholarly journals A Comprehensive Survey on Cyber-Physical Smart Grid Testbed Architectures: Requirements and Challenges

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1043
Author(s):  
Abdallah A. Smadi ◽  
Babatunde Tobi Ajao ◽  
Brian K. Johnson ◽  
Hangtian Lei ◽  
Yacine Chakhchoukh ◽  
...  
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Smart Grids ◽  
Data Exchange ◽  
Information Technologies ◽  
New Technologies ◽  
Rapid Development ◽  
Environmental Benefits ◽  
Future Research ◽  
Time Data

The integration of improved control techniques with advanced information technologies enables the rapid development of smart grids. The necessity of having an efficient, reliable, and flexible communication infrastructure is achieved by enabling real-time data exchange between numerous intelligent and traditional electrical grid elements. The performance and efficiency of the power grid are enhanced with the incorporation of communication networks, intelligent automation, advanced sensors, and information technologies. Although smart grid technologies bring about valuable economic, social, and environmental benefits, testing the combination of heterogeneous and co-existing Cyber-Physical-Smart Grids (CP-SGs) with conventional technologies presents many challenges. The examination for both hardware and software components of the Smart Grid (SG) system is essential prior to the deployment in real-time systems. This can take place by developing a prototype to mimic the real operational circumstances with adequate configurations and precision. Therefore, it is essential to summarize state-of-the-art technologies of industrial control system testbeds and evaluate new technologies and vulnerabilities with the motivation of stimulating discoveries and designs. In this paper, a comprehensive review of the advancement of CP-SGs with their corresponding testbeds including diverse testing paradigms has been performed. In particular, we broadly discuss CP-SG testbed architectures along with the associated functions and main vulnerabilities. The testbed requirements, constraints, and applications are also discussed. Finally, the trends and future research directions are highlighted and specified.

Power Flow Adjustment for Smart Microgrid Based on Edge Computing and Deep Reinforcement Learning

2021 ◽  
Author(s):  
Tianjiao Pu ◽  
Fei Jiao ◽  
Yifan Cao ◽  
Zhicheng Liu ◽  
Chao Qiu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Power Control ◽  
Power Flow ◽  
Supply And Demand ◽  
Edge Computing ◽  
Stable Operation ◽  
Local Autonomy ◽  
Real Time Control ◽  
Flow Adjustment

Abstract As one of the core components that improve transportation, generation, delivery, and electricity consumption in terms of protection and reliability, smart grid can provide full visibility and universal control of power assets and services, provide resilience to system anomalies and enable new ways to supply and trade resources in a coordinated manner. In current power grids, a large number of power supply and demand components, sensing and control devices generate lots of requirements, e.g., data perception, information transmission, business processing and real-time control, while existing centralized cloud computing paradigm is hard to address issues and challenges such as rapid response and local autonomy. Specifically, the trend of micro grid computing is one of the key challenges in smart grid, because a lot of in the power grid, diverse, adjustable supply components and more complex, optimization of difficulty is also relatively large, whereas traditional, manual, centralized methods are often dependent on expert experience, and requires a lot of manpower. Furthermore, the application of edge intelligence to power flow adjustment in smart grid is still in its infancy. In order to meet this challenge, we propose a power control framework combining edge computing and machine learning, which makes full use of edge nodes to sense network state and power control, so as to achieve the goal of fast response and local autonomy. Furthermore, we design and implement parameters such as state, action and reward by using deep reinforcement learning to make intelligent control decisions, aiming at the problem that flow calculation often does not converge. The simulation results demonstrate the effectiveness of our method with successful dynamic power flow calculating and stable operation under various power conditions.

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