Control strategies for power distribution networks with electric vehicles integration.

Abstract

Demand side resources, like electric vehicles (EVs), can become integral parts of a smart grids because instead of just consuming power they are capable of providing valuable services to power systems. EVs can be used to balance the intermittent renewable energy resources such as wind and solar. EVs can absorb energy during periods of high electricity production and feed the electricity back into the grid when the demand is high or in situations of insucient electricity generation. However, extra loads created by the increasing number of EVs may have adverse impacts on the distribution network such as congestion. These factors will bring new challenges to the distribution system operator. Typically, the challenges are solved by expanding the grid to t the size and the pattern of the demand. As an alternative, the capacity problem can also be solved smartly using advanced control strategies supported by an increased use of information and communication technology. This is the idea of the smart grid. The smart grid is a next-generation electrical power system that is typied by the increased use of communications and information technology in the generation, delivery and consumption of electrical energy. A smart grid can also be dened as an electricity network that can intelligently integrate the actions of all users connected to it - generators, consumers and those that do both - in order to eciently deliver sustainable, economic and secure electricity supplies. This thesis focuses on designing control strategies for congestion control in distribution network with multiple actors, such as the distribution system operator (DSO), eet operators (FO), and electric vehicle owners (or prosumers), considering their self-interests and operational constraints. Note that the control problem investigated here deals with \higher level" control, e.g., optimization strategy algorithms related scheduling instead of \lower level" direct process control. The thesis starts with reviewing innovative control strategies for large scale management of EVs in the power systems including centralized direct control, market based control, and price control. The thesis investigates new approaches for distribution networks congestion management. It suggests and develops a market based control for distribution grid congestion management. The general equilibrium market mechanism is utilized in the operation of the ii market. To build a complete solution for integration of EVs into the distribution network, a price coordinated hierarchical scheduling system is proposed which can well characterize the involved actors in the smart grid. With this system, we demonstrate that it is possible to schedule the charging scheme of EVs according to the users' energy driving requirements and the forecasted day-ahead electricity market price. Several electric vehicle eet operators are specied to manage the electric vehicle eets. The method of market based control can then be used by the DSO to interact with the electric vehicle eet operators to eliminate the grid congestion problem. Note that the electric vehicle eet operator can manage the EVs based on the three aforementioned control strategies. To test and evaluate the proposed control strategies, multi-agent concepts is used to model the price coordinated hierarchical scheduling system. To implement and demonstrate the multi-agent systems, a novel simulation platform has been developed based on the integration of JACK (a Java based agent-oriented development environment) and Matlab/Simulink software.