Next Article in Journal
The Use of the Surface Roughness Value to Quantify the Extent of Supercritical CO2 Involved Geochemical Reaction at a CO2 Sequestration Site
Next Article in Special Issue
Virtual Inertia: Current Trends and Future Directions
Previous Article in Journal
Dual Functionalized Freestanding TiO2 Nanotube Arrays Coated with Ag Nanoparticles and Carbon Materials for Dye-Sensitized Solar Cells
Previous Article in Special Issue
Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(6), 573; doi:10.3390/app7060573

Multi-Objective Optimization of Voltage-Stability Based on Congestion Management for Integrating Wind Power into the Electricity Market

Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Amjad Anvari-Moghaddam and Josep M. Guerrero
Received: 7 April 2017 / Revised: 22 May 2017 / Accepted: 30 May 2017 / Published: 2 June 2017
View Full-Text   |   Download PDF [2527 KB, uploaded 2 June 2017]   |  

Abstract

This paper proposes voltage-stability based on congestion management (CM) for electricity market environments and considers the incorporation of wind farms into systems as well. A probabilistic voltage-stability constrained optimal power flow (P-VSCOPF) is formulated to maximize both social welfare and voltage stability. To reflect the probabilistic influence of CM in the presence of wind farms on voltage stability, Monte Carlo simulations (MCS) are used to analyze both the system load and the wind speed from their probability distribution functions. A multi-objective particle-swarm optimization (MOPSO) algorithm is implemented to solve the P-VSCOPF problem. A contingency analysis based on the voltage stability index (VSI) for line outages is employed to find the vulnerable line of congestion in power systems. The congestion distribution factor (CDF) is also used to find the optimal location of a wind farm in CM. The optimal pricing expression, which is obtained, with respect to preserving voltage stability, by calculating both the locational marginal prices (LMPs) and the nodal congestion prices (NCPs), is demonstrated in terms of congestion solutions. Simultaneously, the voltage stability margin (VSM) is considered within the CM framework. The proposed approach is implemented on a modified IEEE 24-bus system, and the results obtained are compared with the results of other optimal power flow methods. View Full-Text
Keywords: congestion management; probabilistic voltage-stability-constrained optimal power flow; congestion distribution factor; voltage stability margin; multi-objective particle swarm optimization; wind farm congestion management; probabilistic voltage-stability-constrained optimal power flow; congestion distribution factor; voltage stability margin; multi-objective particle swarm optimization; wind farm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Choi, J.-W.; Kim, M.-K. Multi-Objective Optimization of Voltage-Stability Based on Congestion Management for Integrating Wind Power into the Electricity Market. Appl. Sci. 2017, 7, 573.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top