Dynamic optimization based reactive power planning for improving short-term voltage performance

Short term voltage stability in the form of delayed voltage recovery (FIDVR) poses a significant threat to system stability and reliability. This work examines the voltage instability issue in a power system with dense concentration of induction motor loads and applies dynamic VAR injection as a counter-measure to ensure short term voltage stability following a large disturbance. The dynamic behavior of motor loads, such as decelerating and stalling, is considered as the major cause of FIDVR incidents especially during summer peak load conditions in areas where low inertia single-phase air conditioning (A/C) motors comprise a significant portion of the load. If system dynamics are not taken into account properly, the proposed control solution may be an expensive over design or an under design which is not capable of mitigating FIDVR problems completely. This work aims to provide a comprehensive dynamic VAR planning strategy for handling short term voltage stability problems by proper consideration of system dynamics, multiple contingencies, multiple scenarios and operating conditions. In addition, this approach aims to provide valuable system insights such as behavior of different contingencies and dynamic voltage control areas. Contingencies are clustered together according to their behavioral similarity with respect to voltage performance using an entropy based metric called Kullback-Liebler (KL) measure. Using the information of contingency clusters, a new concept called dynamic voltage control areas is derived. The concept of dynamic voltage control area will address the importance of the location of dynamic reactive reserves. Control vector parameterization (CVP), a dynamic optimization based approach is used to identify the optimal locations and amount of dynamic VARs required to mitigate short term voltage problems. The main idea of CVP approach is to solve the system dynamics separately and utilize the system dynamics results in the constraints evaluation during optimization routine. Also this method is applicable to large scale systems because of the utilization of commercial power system and large scale optimization solvers. Simulations have been carried out on modified IEEE 162 bus system to show the working of contingency clustering, dynamic voltage control area identification and CVP method for single contingency case. The CVP method has also been tested on a large scale realistic power system to show the scalability of the proposed approach.