scholarly journals Definition of Discrete Choice Models of EV Owners Based on Different Socio-Demographic Aspects

2021 ◽  
Vol 11 (8) ◽  
pp. 3679
Author(s):  
Martina Kajanova ◽  
Peter Bracinik
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Prediction Models ◽  
Stated Preference ◽  
Distribution Networks ◽  
Discrete Choice Models ◽  
Stable Operation ◽  
Vehicle To Grid ◽  
Charging Station ◽  
Definition Of

With an increasing number of electric vehicles (EVs), their owners’ involvement in the control of electric power systems and their market seems to be the only option for stable operation of future power networks. However, these people usually have little knowledge about power systems’ operation and follow just their interests. Therefore, this paper deals with the decision-making process of EV drivers at the charging station. The paper presents the stated preference survey used to collect the responses to hypothetical scenarios, where respondents chose between three alternatives, namely slow charging, fast charging, and vehicle-to-grid services. The survey also contained questions about respondents’ socio-demographic characteristics, as gender, age, etc. The decision-making prediction models for each socio-demographic characteristic were created using the acquired data. The paper presents the estimated parameters of the attributes affecting the respondents’ choices for the models that allow models’ simple implementation. Knowing these models and the customers’ composition, the operators of the charging stations or the distribution networks could better estimate EV owners’ behavior and so their expected power demand. Moreover, operators could more effectively implement incentives for their customers and affect the customers’ behavior in a way that is suitable for better operation of their power systems.

scholarly journals A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6120
Author(s):  
Nikolaos Milas ◽  
Dimitris Mourtzis ◽  
Emmanuel Tatakis
Keyword(s):  
Decision Making ◽  
Electric Vehicles ◽  
Scheduling Algorithm ◽  
Analytic Hierarchy ◽  
Open Platform ◽  
Charging Station ◽  
Smart Charging ◽  
Definition Of ◽  
Hierarchy Process ◽  
Selection Of

During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

scholarly journals A Chronological Literature Review of Electric Vehicle Interactions with Power Distribution Systems

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 3016
Author(s):  
Andrés Arias-Londoño ◽  
Oscar Danilo Montoya ◽  
Luis Fernando Grisales-Noreña
Keyword(s):  
Power Systems ◽  
Electricity Markets ◽  
Power Distribution ◽  
Distribution Systems ◽  
Demand Management ◽  
Distribution Networks ◽  
Power Grids ◽  
System Stability ◽  
Systematic Classification ◽  
Vehicle To Grid

In the last decade, the deployment of electric vehicles (EVs) has been largely promoted. This development has increased challenges in the power systems in the context of planning and operation due to the massive amount of recharge needed for EVs. Furthermore, EVs may also offer new opportunities and can be used to support the grid to provide auxiliary services. In this regard, and considering the research around EVs and power grids, this paper presents a chronological background review of EVs and their interactions with power systems, particularly electric distribution networks, considering publications from the IEEE Xplore database. The review is extended from 1973 to 2019 and is developed via systematic classification using key categories that describe the types of interactions between EVs and power grids. These interactions are in the framework of the power quality, study of scenarios, electricity markets, demand response, demand management, power system stability, Vehicle-to-Grid (V2G) concept, and optimal location of battery swap and charging stations.

scholarly journals Electrical DebtRank Algorithm–Based Identification of Vulnerable Transmission Lines in Power Systems

2021 ◽  
Vol 9 ◽  
Author(s):  
Lijuan Li ◽  
Yiwei Zeng ◽  
Jie Chen ◽  
Yue Li ◽  
Hai Liu ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transmission Lines ◽  
Large Scale ◽  
Operation Mode ◽  
System Structure ◽  
Stable Operation ◽  
Dual Model ◽  
Line Load ◽  
Definition Of

With the increase of complexity of the power system structure and operation mode, the risk of large-scale power outage accidents rises, which urgently need an accuracy algorithm for identifying vulnerabilities and mitigating risks. Aiming at this, the improved DebtRank (DR) algorithm is modified to adapt to the property of the power systems. The overloading state of the transmission lines plays a notable role of stable operation of the power systems. An electrical DR algorithm is proposed to incorporate the overloading state to the identification of vulnerable lines in the power systems in this article. First, a dual model of power system topology is established, the nodes of which represent the lines in the power systems. Then, besides the normal state and failure state having been considered, the definition of the overloading state is also added, and the line load and network topology are considered in the electrical DR algorithm to identify vulnerable lines. Finally, the correctness and reasonability of the vulnerable lines of the power systems identified by the electrical DR algorithm are proved by the comparative analysis of cascade failure simulation, showing its better advantages in vulnerability assessment of power systems.

A Unifying Principle for a Researcher Learning Safety: Reduce Uncertainty!

2018 ◽  
Author(s):  
Camilla Kao ◽  
Russell Furr
Keyword(s):  
Decision Making ◽  
Best Practices ◽  
Mission Statement ◽  
Safety Information ◽  
Health And Safety ◽  
Mission Statements ◽  
Definition Of ◽  
Physical And Chemical

Conveying safety information to researchers is challenging. A list of rules and best practices often is not remembered thoroughly even by individuals who want to remember everything. Researchers in science thinking according to principles: mathematical, physical, and chemical laws; biological paradigms. They use frameworks and logic, rather than memorization, to achieve the bulk of their work. Can safety be taught to researchers in a manner that matches with how they are trained to think? Is there a principle more defined than "Think safety!" that can help researchers make good decisions in situations that are complex, new, and demanding?<div><br></div><div>Effective trainings in other professions can arise from the use of a mission statement that participants internalize as a mental framework or model for future decision-making. We propose that mission statements incorporating the concept of <b>reducing uncertainty</b> could provide such a framework for learning safety. This essay briefly explains the definition of <b>uncertainty</b> in the context of health and safety, discusses the need for an individual to <b>personalize</b> a mission statement in order to internalize it, and connects the idea of <b>greater control</b> over a situation with less uncertainty with respect to safety. The principle of reducing uncertainty might also help <b>non-researchers</b> think about safety. People from all walks of life should be able to understand that more control over their situations provides more protection for them, their colleagues, and the environment.</div>

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

An Analysis of the Demand for Reporting on Internal Control

2000 ◽  
Vol 14 (3) ◽  
pp. 325-341 ◽  
Author(s):  
Heather M. Hermanson
Keyword(s):  
Decision Making ◽  
Financial Reporting ◽  
Internal Control ◽  
Operational Definition ◽  
Financial Statement ◽  
Individual Investors ◽  
Internal Auditors ◽  
Additional Information ◽  
User Groups ◽  
Definition Of

The purpose of this study is to analyze the demand for reporting on internal control. Nine financial statement user groups were identified and surveyed to determine whether they agree that: (1) management reports on internal control (MRIC) are useful, (2) MRICs influence decisions, and (3) financial reporting is improved by adding MRICs. In addition, the paper examined whether responses varied based on: (1) the definition of internal control used (manipulated as broad, operational definition vs. narrow, financial-reporting definition) and (2) user group. The results indicate that financial statement users agree that internal controls are important. Respondents agreed that voluntary MRICs improved controls and provided additional information for decision making. Respondents also agreed that mandatory MRICs improved controls, but did not agree about their value for decision making. Using a broad definition of controls, respondents strongly agreed that MRICs improved controls and provided a better indicator of a company's long-term viability. Executive respondents were less likely to agree about the value of MRICs than individual investors and internal auditors.

scholarly journals A Methodology for Redesigning Networks by Using Markov Random Fields

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Random Fields ◽  
Markov Random Fields ◽  
Application Performance ◽  
Cross Border ◽  
Markov Random ◽  
User Groups ◽  
Universal Definition ◽  
Definition Of ◽  
Decision Making Model

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

scholarly journals Optimal Sizing and Placement of Capacitor Banks in Distribution Networks Using a Genetic Algorithm

Electricity ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 187-204
Author(s):  
Gian Giuseppe Soma
Keyword(s):  
Genetic Algorithm ◽  
Electricity Market ◽  
Distribution Systems ◽  
Reactive Power ◽  
Electricity Consumption ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Capacitor Banks ◽  
Control Pattern ◽  
Definition Of

Nowadays, response to electricity consumption growth is mainly supported by efficiency; therefore, this is the new main goal in the development of electric distribution networks, which must fully comply with the system’s constraints. In recent decades, the issue of independent reactive power services, including the optimal placement of capacitors in the grid due to the restructuring of the electricity industry and the creation of a competitive electricity market, has received attention from related companies. In this context, a genetic algorithm is proposed for optimal planning of capacitor banks. A case study derived from a real network, considering the application of suitable daily profiles for loads and generators, to obtain a better representation of the electrical conditions, is discussed in the present paper. The results confirmed that some placement solutions can be obtained with a good compromise between costs and benefits; the adopted benefits are energy losses and power factor infringements, taking into account the network technical limits. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of capacitor banks in distribution systems, with the definition of a suitable control pattern, have been proved.

scholarly journals Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Tracking Problem ◽  
Smart Meters ◽  
Set Point ◽  
Point Tracking ◽  
Power Set ◽  
Decision Making Framework

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

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