Date of Award

2018

Document Type

Open Access Dissertation

Department

Electrical Engineering

First Advisor

Andrea Benigni

Abstract

The increasing penetration of distributed solar photovoltaic (PV) generation presents both challenges and opportunities for distribution systems. The intermittent nature of solar irradiance may lead to power quality degradation. At the same time, PV inverters –if properly designed and operated– can be used to improve power quality. The goal of this dissertation is to develop power flow optimization methods for power distribution networks with high penetration of PV generation. The approaches proposed in this dissertation have been tested using the modified version of the IEEE 34-node distribution system and the IEEE 123-node distribution system, as well as the validated model of a section of the distribution grid in Walterboro, SC. We first focus on the probabilistic assessment of PV penetration in distribution networks. A stochastic approach based on kernel density estimation is proposed to identify the optimal location for the PV plant installation so that the voltage deviations and network losses are minimized. In the second part, we develop a two-stage hierarchical structure to seek the optimal solutions of a fully centralized optimal power flow (OPF) problem. In the first stage, the OPF problem is formulated as a day-ahead optimal scheduling problem with both continuous and discrete design variables. The direct search algorithms are applied to solve this mixed-integer nonlinear programming (MINLP) problem. In the second stage, to compensate for the uncertainties of the PV output and load demand, a real-time PV inverter reactive power control scheme is proposed and tested using a Hardware-In-theLoop (HIL) approach. Due to the limited availability of real-time measurement devices in distribution systems, an artificial neural network (ANN) approach is used to estimate the operating states of distribution systems. In the final part, we present a decentralized state estimation approach to support real-time decentralized Volt/Var optimization. The network is divided into sub-areas according to the location of measurement devices and the mutual information (MI) between the states of interest and the available measurements. In each sub-area, an artificial neural network (ANN) is used to estimate the loads consumption, and each estimator only relies on local information and on a limited amount of information from neighboring areas. A minimum redundancy-maximum relevance (mRMR) feature selection method is utilized for choosing the optimal subset of the input variables. The presented approach also has been validated using an HIL approach.

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