An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids

Journal article (2023)

Authors

Amirhossein Akbari K.N. Toosi University of Technology
Ahmadreza Alavi-Koosha K.N. Toosi University of Technology
M. Moradi Sepahvand Intelligent Electrical Power Grids - EEMCS
Mohammadreza Toulabi K.N. Toosi University of Technology
Turaj Amraee K.N. Toosi University of Technology
Seyyed Mohammad Taghi Bathaee K.N. Toosi University of Technology
Research Group
Intelligent Electrical Power Grids (EEMCS) (TU Delft)
Electric vehicles (EVs) Incentive mechanism Security constrained unit commitment (SCUC) Sensitivity factors
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

Intelligent Electrical Power Grids

Abstract

This article presents an electric vehicle (EV)-integrated security constrained unit commitment (EV-SCUC) based on N−1 criteria for single line outage contingency under intermittency of load and renewable energy sources (RESs). Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. Under EV spatiotemporal constraints, results confirm that the system operator can decisively control commitment decisions, total cost, incentive payments, and pre/postcontingency congestion criteria under different EV control parameters and RES penetration deterministically or stochastically.