Document Type

Thesis - Open Access

Award Date

2025

Degree Name

Master of Science (MS)

Department / School

Electrical Engineering and Computer Science

First Advisor

Timothy Hansen

Abstract

The primary objective of this thesis is to accurately estimate the capacity of distributed energy resources (DERs) using the spatiotemporally downscaled local solar irradiance for their efficient integration into transmission-level grid operations. Accurate estimation of DER capacity is crucial for their enhanced integration into real-time electricity markets. Different methods of spatiotemporally downscaling the solar irradiance are presented in this thesis, followed by their integration into the 12 house distribution network – a low voltage residential distribution system. Grid support functions (GSFs) like Volt-Watt Control, Volt-VAR control, and Frequency-Watt Control are applied to effectively regulate the frequency and maintain the voltage within the safe operating limits as set by the American National Standard Institute (ANSI) standards. The first part of the work focuses on spatiotemporally downscaling the solar irradiance using two different machine learning (ML) techniques named Nearest Neighbor Gaussian Process (NNGP) and Nearest Neighbor Random Forest (NNRF) . The performance comparison of these two methods is carried out and efficiency of both methods are demonstrated clearly. Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB) , provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing DERs penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates NNRF and NNGP models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs. The second part of the work leverages the downscaled solar irradiance to convert it into AC PV output through the PV performance modeling guide provided by Sandia National Laboratories. This PV output is incorporated into each house of the 12-house residential low-voltage distribution network and voltage profile of each house is analyzed. When the voltage crosses the threshold and exceeds the safe operating limits, GSFs like Volt-Watt control, Volt-VAR control, and Frequency-Watt control are applied to maintain the voltage regulation. This enables DERs to participate in real-time voltage control, thereby contributing towards efficient and resilient power system. The third part of this work performs the comparative study between global data and locally downscaled data for capacity estimation. The objective is to demonstrate the enhanced effectiveness and accuracy of using downscaled data instead of global irradiance for estimating DER capacity. Without accounting for the fluctuations in local data, the estimated PV output of DERs using global data will be highly susceptible to misalignment with the actual potential of generation. This mismatch can result in inaccurate estimation of DER’s ability to provide frequency support. In electric markets and utility operations, issues like insufficient frequency reserves can arise; especially when provision of ancillary service in precision is significant. This can lead to instability of electric grid and even frequency collapse in the events of contingencies. Accurately estimating the capacity of DERs to provide GSFs is critical for their effective integration into transmission-level grid operations. Traditional weather forecasting methods typically provide irradiance data over large spatial grids, neglecting localized variations essential for precise DER capacity estimation. The second part of this thesis addresses this limitation by employing spatiotemporally downscaled solar irradiance data, enabling the generation of realistic, time-varying PV output profiles for each residential unit within a 12-house feeder network. The capability of residential solar PV systems to offer voltage and frequency support through inverter-based droop control methods (Volt-Watt, Volt-VAR, and Freq-Watt) is evaluated. A 24-hour simulation was conducted to assess voltage deviations resulting from varying solar conditions, followed by the activation of Volt-Watt and Volt-VAR control functions to mitigate these deviations and ensure compliance with ANSI voltage standards. Due to complexities in translating PV outputs to frequency deviations in grid-following inverter setups, Freq-Watt control was only evaluated under constant solar conditions and not applied to the time-varying scenarios. Results demonstrate that integrating spatiotemporally downscaled solar irradiance significantly enhances the accuracy of DER capacity estimation and confirms the effectiveness of inverter-based droop controls in maintaining grid voltage stability. This thesis contributes to the power system research community by offering publicly available resources and an effective method to accurately estimate the capacity of DERs, allowing them to actively participate in the real-time electricity markets and their efficient dispatch. The spatiotemporal downscaling methods and control methods for DERs integration to distribution networks that we propose can be leveraged by utilities for effective planning, operation, dispatch, and mitigating power grid issues like voltage deviations (overvoltage and undervoltage), and unintentional inverter disconnections. This contributes to a robust, stable, and effective power system during high penetration of DERs like solar PV, and varying load demand.

Library of Congress Subject Headings

Distributed resources (Electric utilities) 
Distributed generation of electric power.
Solar radiation -- Forecasting. 
Renewable energy sources.      

Publisher

South Dakota State University

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Rights Statement

In Copyright