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Title: A Fast Quasi Static Time Series Simulation Method using Sensitivity Analysis to Evaluate Distributed PV Impacts
Committee:
Dr. Santiago Grijalva, ECE, Chair , Advisor
Dr. Lukas Graber, ECE
Dr. Maryam Saeedifard, ECE
Dr. Sakis Meliopoulos, ECE
Dr. Godfried Augenbroe, COA
Abstract:
The desire to reduce carbon footprint coupled with government incentives has led to a massive deployment of renewable energy resources, solar photovoltaics (PV) in particular. Electric utilities in the United States face the challenge of numerous solar PV interconnection requests filed by customers, which seems to increase every day. The approval process for an interconnection request often requires a detailed impact analysis to ensure that the installed resource will not adversely affect the reliability of the distribution grid. Currently, impact analysis for a single PV system using high resolution Quasi-Static Time Series (QSTS) simulation can take anywhere between 10-120 hours to complete, which becomes an infeasible option for utilities, considering the ever-increasing number of interconnection requests. To streamline the process, utilities use static scenario-based simulations to determine the maximum hosting capacity of the distribution feeders. However, these hosting capacity estimates don’t take into account the voltage regulation (VR) equipment within the feeder. The VR devices, such as tap changing transformers and switched capacitor banks, have the capability to maintain the feeder voltage profile within the ANSI C84.1 limits. Therefore, excluding their impacts while determining the hosting capacity estimates can produce overly conservative results. The goal of this dissertation is to develop a fast, robust, scalable and accurate time series analysis tool that can overcome the limitations of existing PV impact evaluation techniques. The novel algorithm developed in this work leverages the local linearity of the AC power flow manifold to compute the sensitivity coefficients, using a regression-based framework. The proposed algorithm shows an average speed improvement of around 150 times, when compared to the traditional brute-force QSTS method, and is able to maintain high accuracy levels across a variety of different PV impact metrics.
