Graduation Year

2020

Document Type

Thesis

Degree

M.S.Cp.

Degree Name

MS in Computer Engineering (M.S.C.P.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Ken Christensen, Ph.D.

Committee Member

Srinivas Katkoori, Ph.D.

Committee Member

Yao Liu, Ph.D.

Keywords

Control Mechanism, Microgrids, Nanogrids, Rural Electrification, Supply and Demand

Abstract

Electricity availability has a profound impact in day to day life. Activities such as lighting homes, charging cellphones, and running appliances all depend on electricity use. Around 1 billion people around the world do not have access to electricity, and the majority of these people hail from developing countries in remote areas. Moreover, many of these remote areas lack utility grid access due to the infeasible cost of extending the grid to remote communities. To combat this, various projects on microgrids have been implemented in order to distribute power to these off-grid areas. Nanogrids are defined as “a single domain for voltage, price, reliability, quality, and administration.” These nanogrids are small microgrids that supply electricity to small remote buildings, given a limited electricity supply. Furthermore, nanogrids provide electricity to remote areas in order to increase the livelihood of people living in these unfavorable conditions. In this thesis, a new method is introduced to nanogrids using a locally determined price shared with devices with a goal to match electricity demand to locally generated supply to reduce the number of blackouts while maximining quality of experience in a nanogrid system.

This thesis provides a review of current rural electrification projects done in developing countries, specifically remote areas in Africa and Asia. A series of algorithms is introduced that sets the price for available electricity use in a household, controlling the power consumption of electrical devices. The topics of Internet of Things (IoT), prices to devices and smart appliances are covered in enabling and implementing the proposed algorithms.

A simulation model considers various parameters, including the battery charge and discharge rate, weather forecast, time of day, and time of year with respect to price allocation and power consumption. Simulation results display that the nanogrid paradigm using the controlled price algorithm eliminates the occurrence of blackouts in the rainy season with a reduced battery size (the worst case scenario) as compared to the nanogrid paradigm without price control.

The outcome of this simulation suggests that the distribution of power using price control levels off the electricity demand with supply and allows for smaller battery and smaller solar panels all the while maintaining a high quality of experience. It shows that a remote household using an off-grid system, with control of electricity use through pricing given a limited power supply, can effectively allocate electricity use and match electricity supply with demand. This reduces the occurrence of unwanted blackouts, positively influencing the economical aspect of remote areas with respect to electricity access in developing countries.

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