Title

Intelligent Unit Commitment with Vehicle-to-grid —a Cost-emission Optimization

Abstract

A gridable vehicle (GV) can be used as a small portable power plant (S3P) to enhance the security and reliability of utility grids. Vehicle-to-grid (V2G) technology has drawn great interest in the recent years and its success depends on intelligent scheduling of GVs or S3Ps in constrained parking lots. V2G can reduce dependencies on small expensive units in existing power systems, resulting in reduced operation cost and emissions. It can also increase reserve and reliability of existing power systems. Intelligent unit commitment (UC) with V2G for cost and emission optimization in power system is presented in this paper. As number of gridable vehicles in V2G is much higher than small units of existing systems, UC with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to balance between cost and emission reductions for UC with V2G. PSO can reliably and accurately solve this complex constrained optimization problem easily and quickly. In the proposed solution model, binary PSO optimizes on/off states of power generating units easily. Vehicles are presented by integer numbers instead of zeros and ones to reduce the dimension of the problem. Balanced hybrid PSO optimizes the number of gridable vehicles of V2G in the constrained parking lots. Balanced PSO provides a balance between local and global searching abilities, and finds a balance in reducing both operation cost and emission. Results show a considerable amount of cost and emission reduction with intelligent UC with V2G. Finally, the practicality of UC with V2G is discussed for real-world applications.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

CAREER Grant
National Science Foundation (U.S.)

Keywords and Phrases

UC; V2G; Emission; Gridable Vehicles; Particle Swarm Optimization

Library of Congress Subject Headings

Cost

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2010 Elsevier, All rights reserved.

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