Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves.
","abstract_html":"<p>Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves.</p>","abstract_has_math":false,"creators":["Shakya, Ayush"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Reinaldo Tonkoski","Semhar Michael"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T04:28:36Z","subjects":["Clear Sky Irradiance Energy Management System Fourier Basis Expansion OPAL-RT","Real-time Digital Simulator","solar irradiance forecasting","Electrical and Computer Engineering","Power and Energy"],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/1068","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reinaldo Tonkoski","Semhar Michael"]},{"key":"dc:creator","label":"Author","values":["Shakya, Ayush"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-08-24T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Clear Sky Irradiance Energy Management System Fourier Basis Expansion OPAL-RT","Real-time Digital Simulator","solar irradiance forecasting","Electrical and Computer Engineering","Power and Energy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/1068"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves.
"]},{"key":"dc:title","label":"Title","values":["Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System"]}]}],"canonical_facts":{"dc:contributor":["Reinaldo Tonkoski","Semhar Michael"],"dc:creator":["Shakya, Ayush"],"dc:date.available":["2017-08-24T07:00:00Z"],"dc:description.abstract":["Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1068"],"dc:language":["en"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Clear Sky Irradiance Energy Management System Fourier Basis Expansion OPAL-RT","Real-time Digital Simulator","solar irradiance forecasting","Electrical and Computer Engineering","Power and Energy"],"dc:title":["Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T04:28:36Z"}