scholarly journals A transition from manual to Intelligent Automated power system operation -A Indicative Review

2019 ◽  
Vol 9 (4) ◽  
pp. 2274
Author(s):  
Yamanappa N. Doddamani ◽  
U. C. Kapale
Keyword(s):  
Power System ◽  
Energy Management ◽  
Power Delivery ◽  
Islanding Detection ◽  
Energy Audit ◽  
System Operation ◽  
Power System Operation ◽  
Power System Operations ◽  
Detection Energy ◽  
Manual Mode

<p>This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.</p>

scholarly journals Integrating Electric Vehicles into Power System Operation Production Cost Models

2021 ◽  
Vol 12 (4) ◽  
pp. 263
Author(s):  
Jose David Alvarez Guerrero ◽  
Bikash Bhattarai ◽  
Rajendra Shrestha ◽  
Thomas L. Acker ◽  
Rafael Castro
Keyword(s):  
Power System ◽  
Electric Vehicles ◽  
Production Cost ◽  
Peak Load ◽  
System Operation ◽  
Power System Operation ◽  
Power System Operations ◽  
Battery Capacity ◽  
Coordinated Charging ◽  
Ev Charging

The electrification of the transportation sector will increase the demand for electric power, potentially impacting the peak load and power system operations. A change such as this will be multifaceted. A power system production cost model (PCM) is a useful tool with which to analyze one of these facets, the operation of the power system. A PCM is a computer simulation that mimics power system operation, i.e., unit commitment, economic dispatch, reserves, etc. To understand how electric vehicles (EVs) will affect power system operation, it is necessary to create models that describe how EVs interact with power system operations that are suitable for use in a PCM. In this work, EV charging data from the EV Project, reported by the Idaho National Laboratory, were used to create scalable, statistical models of EV charging load profiles suitable for incorporation into a PCM. Models of EV loads were created for uncoordinated and coordinated charging. Uncoordinated charging load represents the load resulting from EV owners that charge at times of their choosing. To create an uncoordinated charging load profile, the parameters of importance are the number of vehicles, charger type, battery capacity, availability for charging, and battery beginning and ending states of charge. Coordinated charging is where EVs are charged via an “aggregator” that interacts with a power system operator to schedule EV charging at times that either minimize system operating costs, decrease EV charging costs, or both, while meeting the daily EV charging requirements subject to the EV owners’ charging constraints. Beta distributions were found to be the most appropriate distribution for statistically modeling the initial and final state of charge (SoC) of vehicles in an EV fleet. A Monte Carlo technique was implemented by sampling the charging parameters of importance to create an uncoordinated charging load time series. Coordinated charging was modeled as a controllable load within the PCM to represent the influence of the EV fleet on the system’s electricity price. The charging models were integrated as EV loads in a simple 5-bus system to demonstrate their usefulness. Polaris Systems Optimization’s PCM power system optimizer (PSO) was employed to show the effect of the EVs on one day of operation in the 5-bus power system, yielding interesting and valid results and showing the effectiveness of the charging models.

Issues for Power System Operation for Future Renewable Energy Penetration

2011 ◽  
Vol 131 (8) ◽  
pp. 670-676 ◽  
Author(s):  
Naoto Yorino ◽  
Yutaka Sasaki ◽  
Shoki Fujita ◽  
Yoshifumi Zoka ◽  
Yoshiharu Okumoto
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
System Operation ◽  
Power System Operation

Advanced teaching method for learning power system operation based on load flow simulations

2021 ◽  
Author(s):  
Andrés Honrubia‐Escribano ◽  
Raquel Villena‐Ruiz ◽  
Estefanía Artigao ◽  
Emilio Gómez‐Lázaro ◽  
Ana Morales
Keyword(s):  
Power System ◽  
Teaching Method ◽  
Load Flow ◽  
System Operation ◽  
Power System Operation ◽  
Flow Simulations ◽  
Advanced Teaching

scholarly journals Architectural and functional classification of smart grid solutions

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Friederike Wenderoth ◽  
Elisabeth Drayer ◽  
Robert Schmoll ◽  
Michael Niedermeier ◽  
Martin Braun
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Power System ◽  
Power Distribution ◽  
Hierarchical Architecture ◽  
Distribution Grid ◽  
Active System ◽  
System Operation ◽  
Power System Operation

Abstract Historically, the power distribution grid was a passive system with limited control capabilities. Due to its increasing digitalization, this paradigm has shifted: the passive architecture of the power system itself, which includes cables, lines, and transformers, is extended by a communication infrastructure to become an active distribution grid. This transformation to an active system results from control capabilities that combine the communication and the physical components of the grid. It aims at optimizing, securing, enhancing, or facilitating the power system operation. The combination of power system, communication, and control capabilities is also referred to as a “smart grid”. A multitude of different architectures exist to realize such integrated systems. They are often labeled with descriptive terms such as “distributed,” “decentralized,” “local,” or “central." However, the actual meaning of these terms varies considerably within the research community.This paper illustrates the conflicting uses of prominent classification terms for the description of smart grid architectures. One source of this inconsistency is that the development of such interconnected systems is not only in the hands of classic power engineering but requires input from neighboring research disciplines such as control theory and automation, information and telecommunication technology, and electronics. This impedes a clear classification of smart grid solutions. Furthermore, this paper proposes a set of well-defined operation architectures specialized for use in power systems. Based on these architectures, this paper defines clear classifiers for the assessment of smart grid solutions. This allows the structural classification and comparison between different smart grid solutions and promotes a mutual understanding between the research disciplines. This paper presents revised parts of Chapters 4.2 and 5.2 of the dissertation of Drayer (Resilient Operation of Distribution Grids with Distributed-Hierarchical Architecture. Energy Management and Power System Operation, vol. 6, 2018).

scholarly journals Erratum to: “Methods for optimization of power system operation modes”

2014 ◽  
Vol 85 (8) ◽  
pp. 541-541 ◽  
Author(s):  
N. A. Belyaev ◽  
N. V. Korovkin ◽  
O. V. Frolov ◽  
V. S. Chudnyi
Keyword(s):  
Power System ◽  
System Operation ◽  
Power System Operation ◽  
Operation Modes
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