Analysis and Planning of Power Transmission System Subject to Uncertainties in the Grid
Abstract
Power transmission systems frequently experience new power flow pattern due to several factors that increase uncertainties in the system. For instance, load shape uncertainty, uncertainty due to penetration of renewable sources, changing standards, and energy de-regulation threaten the reliability and security of power transmission systems. This demands for more rigorous analysis and planning of power transmission systems.
Stability issues in power transmission system are more pronounced with the penetration of utility-scale Photo-Voltaic (PV) sources. Synchronous generators provide inertia that helps in damping oscillations that arise due to fluctuations in the power system. Therefore, as PV generators replace the conventional synchronous generators, power transmission systems become vulnerable to these abnormalities. In this thesis, we study the effect of reduced inertia due to the penetration of utility-scale PV on the transient stability of power transmissions systems. In addition, the effect of increased PV penetration level in the system during normal operating condition is also analyzed. The later study illustrates that the PV penetration level and the placement of PV sources play crucial roles in determining the stability of power transmission systems.
Given increasing uncertainties in power transmission systems, there is a need to seek an alternative to deterministic planning approach because it inherently lacks capability to cover all the uncertainties. One practical alternative is the probabilistic planning approach. In probabilistic planning approach, an analysis is made with a wide variety of scenarios by considering the probability of occurrence of each scenario and the probability of contingencies. Then, the severity of the contingencies risk associated with each planning practice is calculated. However, due to the lack of techniques and tools to select wide varieties of scenarios along with their probability of occurrence, the probabilistic transmission planning approach has not been implemented in real-world power transmission systems. This thesis presents a technique that can select wide varieties of scenarios along with their probability of occurrence to facilitate probabilistic planning in Electricity Reliability Council of Texas (ERCOT) systems.
General Audience Abstract
Reliability of power transmission systems are threatened due to the increasing uncertainties arising from penetration of renewable energy sources, load growth, energy de-regulation and changing standards. Stability issues become more prevalent than in past due to increasing load growth as the demand for reactive power increases. Several researchers have been studying the impact of increased load growth and increased penetration of renewables on the dynamic stability of the distribution system. However, far less emphasis has been given to the power transmission system. This thesis presents the transient stability analysis of power transmission systems during overloading conditions. Our study also facilitates identification of weak areas of the transmission system during overloading condition. In addition, the impact of replacing conventional synchronous generator by Photovoltaics (PV) on voltage stability of the system is also analyzed. With increasing uncertainties in transmission systems, it is necessary to carefully analyze a wide variety of scenarios while planning the system. The current approach to transmission planning i.e., the deterministic approach does not sufficiently cover all the uncertainties. This has imposed the need for the probabilistic transmission planning approach where the overall system is planned based on the analysis of wide varieties of scenarios. In addition, by considering the probability of occurrence of a scenario, the probability of contingencies and severity of contingencies risk associated with each planning practice is calculated. However, there is no well-established approach that is capable of selecting wide varieties of scenarios based on their probability of occurrence. Due to this limitation, probabilistic approach is not widely implemented in real-world power transmission systems. To address this issue, this thesis presents a new technique, based on K-means clustering, to select scenarios based on their probability of occurrence.
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