Improving the resilience of the power grid

• Online Access: http://hdl.handle.net/2047/D20409481

The power grid constitutes one of the most critical infrastructures that have significant interdependencies with various other infrastructures such as communication, transportation, water, emergency, and health-care delivery systems. A disruption in the operation of the power grid may affect the operation of all others in an undesirable manner. Therefore, improving the resiliency of power grids can also help increase the resiliency of other critical infrastructures. As a critical infrastructure, power systems have to be prepared to survive rare but extreme events to guarantee energy needs. This dissertation presents methods to improve the resilience of power grids against extreme events and/or system changes. In order to maximize the resilience of the overall power grid against extreme events, first, generation dispatch, adaptable load shedding strategy, and pro-active line switching are combined in this dissertation. The moving event is monitored, and the control actions are adjusted accordingly to improve the resilience under changing conditions affected by the natural disaster during its active period. Then, that study is further extended and made it robust against voltage instability. Since intentional line switching and load shedding actions may provoke voltage instabilities, this issue is addressed by this dissertation. To reduce the probability of voltage problems and line flow limit violations, and to improve power quality, distributed generators (DG) are placed strategically ahead of the event using outage forecasts based on historical outage data. Therefore, a possible set of outage scenarios is considered, and a minimum number of required DG placements are determined to maintain system feasibility for all considered scenarios. Finally, reactive power sources are placed to solve the voltage instability problems, which are caused by the lack of reactive power in the system. The computational burden of optimal placement problem presents a practical limitation for applying it to very large scale systems considering multi-contingency cases. This part presents a practical and easily implementable solution that will address this limitation.--Author's abstract