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Intra-Day Solar Irradiance Forecasti...

Bajracharya, Abhilasha.

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Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model./
作者:	Bajracharya, Abhilasha.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	76 p.
附註:	Source: Masters Abstracts International, Volume: 81-05.
Contained By:	Masters Abstracts International81-05.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22616805
ISBN:	9781088394366

Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model.
Bajracharya, Abhilasha.

Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 76 p.

Source: Masters Abstracts International, Volume: 81-05.

Thesis (M.S.)--South Dakota State University, 2019.

This item must not be sold to any third party vendors.

Accurate solar irradiance forecasting is the key to accurate estimation of solar power output at any given time. The accuracy of this information is especially crucial in diesel-PV based remote microgrids with batteries to determine the setpoints of the batteries and generators for their optimal dispatch. This, in turn, is related directly to the overall operating cost because both an overestimation and an underestimation of the irradiance means additional operating costs for either suddenly ramping up the backup resources or causing under-utilization of the available PV power output. Accurately predicting the solar irradiance is not an easy task because of the sporadic nature of the irradiance that is received at the solar panel surfaces. Handling the dynamic nature of the irradiance pattern requires a strong and flexible model that can precisely capture the irradiance trend in any given location at a given time. Usually, such a robust model requires a lot of input variables like weather data including humidity, temperature, pressure, wind speed, wind direction, etc. and/or large inventory of satellite images of clouds over a long period of time. The expensive sensors and database tools for collecting and storing such huge information may not be installed in remote locations. Therefore, this thesis prioritizes on developing a simple method requiring a minimum input to accurately forecast the solar irradiance for remote microgrids.Essentially, this thesis is an extension of the work by Shakya et al. [1], which is the implementation of day-ahead solar irradiance forecasting using a Markov switching model. In this work, a hidden Markov model (also known as the Markov switching model) is developed using the past irradiance data, clear-sky irradiance, and Fourier basis functions to generate three energy states: low, medium, and high. Each of the three states corresponds to a different cloud cover conditions and thereby a different level of irradiance that is collected at the solar panels. In this thesis, four different methods are described to select the closest state to the actual irradiance in every hour. In other words, four different intra-day forecasting methods are proposed to forecast the 1-hour ahead irradiance. Updating the forecast every hour greatly reduces the forecast error compared to the beforehand mentioned day-ahead forecast. The proposed methods are named as Past4Hr method, Slope1 method, Slope2 method, and PastHr method. A case study for Brookings, South Dakota, is considered to both train and validate the described methods. Additionally, the performance of each of the methods is compared on the basis of the time of the forecast, the time of the year, and at different years to assess their consistencies. The simulations results show that the performance of the Past4Hr method and the PastHr method outperforms the remaining two.

ISBN: 9781088394366Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Energy Management System

Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model.
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500 $a Source: Masters Abstracts International, Volume: 81-05.
500 $a Advisor: Tonkoski, Reinaldo.
502 $a Thesis (M.S.)--South Dakota State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Accurate solar irradiance forecasting is the key to accurate estimation of solar power output at any given time. The accuracy of this information is especially crucial in diesel-PV based remote microgrids with batteries to determine the setpoints of the batteries and generators for their optimal dispatch. This, in turn, is related directly to the overall operating cost because both an overestimation and an underestimation of the irradiance means additional operating costs for either suddenly ramping up the backup resources or causing under-utilization of the available PV power output. Accurately predicting the solar irradiance is not an easy task because of the sporadic nature of the irradiance that is received at the solar panel surfaces. Handling the dynamic nature of the irradiance pattern requires a strong and flexible model that can precisely capture the irradiance trend in any given location at a given time. Usually, such a robust model requires a lot of input variables like weather data including humidity, temperature, pressure, wind speed, wind direction, etc. and/or large inventory of satellite images of clouds over a long period of time. The expensive sensors and database tools for collecting and storing such huge information may not be installed in remote locations. Therefore, this thesis prioritizes on developing a simple method requiring a minimum input to accurately forecast the solar irradiance for remote microgrids.Essentially, this thesis is an extension of the work by Shakya et al. [1], which is the implementation of day-ahead solar irradiance forecasting using a Markov switching model. In this work, a hidden Markov model (also known as the Markov switching model) is developed using the past irradiance data, clear-sky irradiance, and Fourier basis functions to generate three energy states: low, medium, and high. Each of the three states corresponds to a different cloud cover conditions and thereby a different level of irradiance that is collected at the solar panels. In this thesis, four different methods are described to select the closest state to the actual irradiance in every hour. In other words, four different intra-day forecasting methods are proposed to forecast the 1-hour ahead irradiance. Updating the forecast every hour greatly reduces the forecast error compared to the beforehand mentioned day-ahead forecast. The proposed methods are named as Past4Hr method, Slope1 method, Slope2 method, and PastHr method. A case study for Brookings, South Dakota, is considered to both train and validate the described methods. Additionally, the performance of each of the methods is compared on the basis of the time of the forecast, the time of the year, and at different years to assess their consistencies. The simulations results show that the performance of the Past4Hr method and the PastHr method outperforms the remaining two.
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653 $a Microgrids
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