Synergistic optimization scheduling of a wind–thermal power system considering V2G technology based on probability model. Issue 7 (5th June 2019)

• Record Type: Journal Article
• Authors: Qu, Zhengwei; Hou, Shuo; Wang, Yunjing; Wang, Yakun
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 14:Issue 7(2019)
• Page Start: 1008
• Page End: 1015
• Publication Date: 2019-06-05
• Subjects: probability model -- economic optimal scheduling -- V2G -- wind power; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22854
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : Large‐scale grid‐connected electric vehicles (EVs) and wind power generation have brought new challenges to economical optimal scheduling of power system because of their uncertainties. It is essential to establish accurate scheduling models and arrive at the economical optimal scheduling strategy. This paper develops a probability model of wind power output, which adopts the normal distribution to depict the randomness of wind speed, considering turbulence. The probability model of vehicle‐to‐grid (V2G) is also established, and the corresponding probability distribution is used to describe the initial charge/discharge time, daily mileage, and charge state of the EV. A collaborative scheduling model is built for the grid and consumers, taking account of the fuel cost of thermal power unit, the uncertainty cost of wind power output, and the cost of V2G service. The scheduling model is solved by an improved particle swarm optimization algorithm, which applies the inertia weight obeying the standard normal distribution and the nonlinear anticosine learning factors to avoid premature convergence and the loss of population space diversity. Finally, the validity and practicability of the proposed method are verified by the simulation of a ten‐machine power system integrated with wind power and EVs. Simulation results show that V2G can optimize the output of thermal power units and eliminate the surplus output of wind power. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.