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Title: Advanced optimal scheduling methods for integrating plug-in electric vehicles into power systems
Author: Yang, Zhile
ISNI:       0000 0004 6496 7309
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2017
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This thesis focuses on developing new scheduling strategies for the integration of plug-in electric vehicles from power system scheduling perspectives. Economic and environmental load dispatch and unit commitment problems are combined with fixed load profiles as well as intelligent scheduling of plug-in electric vehicles charging and discharging scenarios. In this thesis categories of electric vehicles and the potential scheduling capacity of plug-in electric vehicles are first addressed. Then the state-of-the-art scheduling methods to integrate plug-in electric vehicles are surveyed, examined and categorised based on their computational techniques. The preliminaries of mete-heuristic algorithms preliminary including continuous and discrete methods which would be adopted in the scheduling strategies development. Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimising fossil fuel costs and air pollution emissions subject to operational and licensing requirements. It is of significant importance to achieve the optimal result for the economic and environmental load dispatch considering the impact of plug-in electric vehicles. Therefore, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environmental dispatch model. A novel self-learning teaching-learning based optimisation is proposed to solve the non-convex non-linear dispatch problems. To simultaneously solve the unit commitment and hour based scheduling problem of the plug-in electric vehicles aggregators, a novel hybrid mixed coding meta-heuristic algorithm is proposed, combining five variants of binary symmetric particle swarm optimisation with various transfer functions, a real valued self-adaptive differential evolution and a lambda iteration method. The impact of the transfer function utilised in binary optimisation to solve the unit commitment and plug-in electric vehicle integration is investigated in a 10 unit power system with 50,000 plug-in electric vehicles.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available