Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization

Jun Hua Zhao; Fushuan Wen; Zhao Yang Dong; Yusheng Xue; Kit Po Wong

doi:10.1109/TII.2012.2205398

Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization

Jun Hua Zhao
, Fushuan Wen
, Zhao Yang Dong
, Yusheng Xue
, Kit Po Wong

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

288 Citations (Scopus)

Abstract

In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system. © 2012 IEEE.

Original language	English
Article number	6222000
Pages (from-to)	889-899
Journal	IEEE Transactions on Industrial Informatics
Volume	8
Issue number	4
DOIs	https://doi.org/10.1109/TII.2012.2205398
Publication status	Published - 2012
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Economic dispatch
particle swarm optimization
plug-in electric vehicle
wind power

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title = "Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization",

abstract = "In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system. {\textcopyright} 2012 IEEE.",

keywords = "Economic dispatch, particle swarm optimization, plug-in electric vehicle, wind power",

author = "Zhao, \{Jun Hua\} and Fushuan Wen and Dong, \{Zhao Yang\} and Yusheng Xue and Wong, \{Kit Po\}",

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doi = "10.1109/TII.2012.2205398",

language = "English",

volume = "8",

pages = "889--899",

journal = "IEEE Transactions on Industrial Informatics",

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TY - JOUR

T1 - Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization

AU - Zhao, Jun Hua

AU - Wen, Fushuan

AU - Dong, Zhao Yang

AU - Xue, Yusheng

AU - Wong, Kit Po

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2012

Y1 - 2012

N2 - In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system. © 2012 IEEE.

AB - In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system. © 2012 IEEE.

KW - Economic dispatch

KW - particle swarm optimization

KW - plug-in electric vehicle

KW - wind power

UR - https://www.scopus.com/pages/publications/84867966709

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84867966709&origin=recordpage

U2 - 10.1109/TII.2012.2205398

DO - 10.1109/TII.2012.2205398

M3 - RGC 21 - Publication in refereed journal

SN - 1551-3203

VL - 8

SP - 889

EP - 899

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

IS - 4

M1 - 6222000

ER -

Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization

Abstract

Bibliographical note

UN SDGs

Research Keywords

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