Abstract
This paper presents an algorithm that employs a Sequential Monte Carlo Simulation (SMCS), to estimate operational states of components connected to a grid. Then, by the use of an Accelerated Quantum Particle Swarm Optimization (AQPSO), the algorithm determines the optimum size and location of Static Var Compensators (SVCs). The approach maximizes the level of reliability of the smart grid, which is subject to voltage regulation. The specific contribution of the paper is that it presents the impact of the integration of SVC over the system reliability that leads to a comprehensive composite system adequacy evaluation for a smart grid environment.
Original language | English |
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Title of host publication | 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISBN (Print) | 9781538635964 |
DOIs | |
Publication status | Published - 17 Aug 2018 |
Event | 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Boise, United States Duration: 24 Jun 2018 → 28 Jun 2018 |
Publication series
Name | 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings |
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Conference
Conference | 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 |
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Country/Territory | United States |
City | Boise |
Period | 24/06/18 → 28/06/18 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This study was supported by the Walter Valdano Raffo II program in Escuela Superior Politécnica del Litoral (ESPOL) and the Secretariat of Higher Education, Science, Technology and Innovation of the Republic of Ecuador (Senescyt).
Publisher Copyright:
© 2018 IEEE.
Keywords
- Accelerated quantum particle swarm optimization
- Markov Chain
- Monte Carlo simulation
- Reliability assessment
- Smart-grids
- Static var compensator
ASJC Scopus subject areas
- Computer Networks and Communications
- Statistics, Probability and Uncertainty
- Energy Engineering and Power Technology
- Statistics and Probability