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ORCID

https://orcid.org/0000-0001-7832-1099

Access Type

Campus-Only Access for One (1) Year

Document Type

thesis

Embargo Period

8-17-2020

Degree Program

Mechanical Engineering

Degree Type

Master of Science in Mechanical Engineering (M.S.M.E.)

Year Degree Awarded

2020

Month Degree Awarded

September

Abstract

Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting.

In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as the test bed. UMass has its own power plant producing 16 MW of power. In addition to this, Solar panels totaling 5.5MW and lithium ion battery bank of 1.32 MW/4 MWh are also available. An LSTM recurrent neural network is used for demand forecasting. In addition to a fully trained LSTM network, multi linear regression model, ARIMA and ANN model are also tested to compare the performance.

In addition to the Short Term Load Forecasting, the peak prediction accuracy of the model was also tested to run a battery discharge algorithm to shave peak demand for the microgrid. This will result in demand cost savings for the facility. Finally, the fully trained neural network was deployed on a raspberry pi computer.

DOI

https://doi.org/10.7275/18972960

First Advisor

Dragoljub Kosanovic

Second Advisor

Prashant Shenoy

Third Advisor

Hari Balasubramanian

Fourth Advisor

Benjamin McDaniel

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