Document Type

Thesis - University Access Only

Award Date

2011

Degree Name

Master of Science (MS)

Department / School

Electrical Engineering

Abstract

Interest in stand-alone power systems, known as microgrids, has increased significantly in the last few years as alternative energy, such as photovoltaics (PV), has become more cost effective. Stand-alone microgrids have existed since the advent of electric power but have been replaced to great degree by utility scale power. Examples of microgrids include submarines, mobile military bases, space shuttles, and renewable energy systems. These examples have lacked several aspects for efficient power management including: 1) modular hardware that allows both AC and DC sources; 2) interactive controls for configurable microgrids; and 3) fuel saving algorithms for generator-based microgrids. Therefore, a need exists for centralized and distributed control algorithms for configurable stand-alone microgrids containing variable load profiles. The objectives of this work were to develop control algorithms to manage power flow from multiple sources and loads, and to implement and validate control algorithms in hardware test-beds up to 30 kW. Algorithms were developed for parallel source sharing, maximum power point tracking (MPPT) of a PV array, prioritized load shedding, and generator control for fuel savings in a generator-based microgrid. Bench-top tests with a DC microgrid that included two 500 W sources, three resistive loads, and a generator-battery microgrid provided verification of three control algorithms including: an algorithm that allowed up to 43% fuel savings compared to a generator-only microgrid; a MPPT algorithm for a 200 W PV array that adaptively adjusted DC voltage droop control; and a configurable load shedding algorithm that removed lowest priority loads when generation capacity was exceeded. Future work should include logic to allow inclusion of additional renewable resources, such as PV, and also an adaptive controller for hybrid microgrids that can predict daily load profiles to autonomously maintain sufficient energy storage.

Library of Congress Subject Headings

Electric power distribution

Electric power-plants -- Efficiency

Microgrids (Smart power grids)

Format

application/pdf

Number of Pages

163

Publisher

South Dakota State University

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