Solar Irradiance Forecasting in Remote Microgrids Using Markov Switching Model
clear sky irradiance (CSI), fourier basis expansion, global horizontal irradiance (GHI), markov switching model (MSM), solar irradiance forecasting
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 availabilitywithloadreducesthebeneﬁtsofPVintegration.These challenges can be handled by introducing reserve. However, this leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. This paper presents a new solar irradiance forecasting method for remote microgrids based on the Markov switchingmodel.Thismethoduseslocallyavailabledatatopredict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers past solar irradiance data, clear sky irradiance, and Fourier basis expansions to create linear models for three regimes or states: high, medium, and low energy regimes for days corresponding to sunny, mildly cloudy, andextremelycloudydays,respectively.ThecasestudyforBrookings, SD, USA, discussed in this paper, resulted in an average meanabsolutepercentageerrorof31.8%forﬁveyears,from2001 to 2005, with higher errors during summer months than during winter months.
DOI of Published Version
Shakya, Ayusha; Michael, Semar; Saunders, Christopher; Armstrong, Douglas; Pandey, Prakash; Chalise, Santosh; and Tonkoski, Reinaldo, "Solar Irradiance Forecasting in Remote Microgrids Using Markov Switching Model" (2017). Electrical Engineering and Computer Science Faculty Publications. 5.