Forecasting of photovoltaic power using probabilistic approach

Sreenivasulu, J.; Dukkipati, Sudha; Marthanda, A.V.G.A.; Pandian, A.

doi:10.1016/j.matpr.2020.12.910

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The multiple probability states of solar energy are approximated using standard probability density functions (PDFs). However, the pattern of solar irradiance varies with changes in season, and hence it is difficult to determine the PDF to accurately represent them (Sreenivasulu et al, 2021). Therefore, an accurate generalized probabilistic model to represent the uncertainty associated with solar power needs a careful approach.…”

Section: Motivationmentioning

confidence: 99%

Impact Assessment of Solar Power Generation Uncertainty on Smart Grid Reliability and Carbon Neutrality

et al. 2022

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The global concerted effort toward achieving carbon neutrality has given impetus to the accelerated growth of renewable energy sources with government patronage. The smart grid has the mandate to integrate renewable energy sources adeptly toward meeting the vision of carbon neutrality by 2050 in many countries. Solar energy generation has emerged as one of the most viable options due to the rapid stride of innovations in this domain as well as due to the decreasing trend of the cost of photovoltaic (PV) technology. However, the uncertain sunlight leading to uncertain solar energy generation with a low-capacity factor has been a challenge to maintain the legacy reliability of the power system. Unlike a two-state model being used for analyzing conventional generators, a multistate model has been proposed for incorporating the random variation of solar energy generation correlating with random irradiance. An innovative approach for formulating probabilistic modeling of solar energy is implemented to evaluate different reliability indices such as loss of load probability (LOLP) and expected energy not served (EENS). Different case studies with results prove the efficacy of the proposed probabilistic model–based availability of solar power generation due to solar irradiance uncertainty along with interaction with stochastic load model on smart grid reliability and carbon neutrality.

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Section: Motivationmentioning

confidence: 99%

Impact Assessment of Solar Power Generation Uncertainty on Smart Grid Reliability and Carbon Neutrality

et al. 2022

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“…As a result, high accuracy in time series prediction is required to ensure continuous power generation and avoid system failure for wind energy systems (WES). However, due to the seasonal variation in wind speed patterns, determining the probability distribution function (PDF) that correctly represents the uncertainty in the wind is difficult [4]. As a result, developing a detailed generalized probabilistic framework for modeling the uncertainty associated with wind power should be treated with caution.…”

Section: Motivationmentioning

confidence: 99%

Situational Awareness based Operational Reliability of Wind Energy System Incorporating Uncertainties

Kumar

Mishra

Kumar

2022

Preprint

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The widespread deployment of wind power generation (WPG) has adversely affected the flexibility and reliability of the power system during the operational period. Variable wind speed leads to the variable generation of wind energy systems (WES), which directly affects the operational reliability. This necessitates assessing WES's situational awareness (SA) to predict the day-ahead operational reliability for effective power system operational planning. The edifice of SA for WES is the monitoring of wind speed and WPG for the perception about the state of WES, comprehension about state dynamics, and consequent prediction of operational reliability. The wind system has been modeled using Simulink and interfaced with dSPACE hardware using a real-time interface (RTI) for real-time validation as well as the implementation of a cloud-based IoT platform for the acquisition of wind speed and WPG for the evaluation of SA. The WES is being represented using a multi-state wind speed model, considering the randomness of wind energy generation. For the prediction of wind speed and wind power for SA based operational reliability, a time series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is being used for the prediction of wind speed and WPG. The dSPACE-based real-time implementation of WES for the assessment of SA with IoT integration validates the efficacy of the proposed approach for operational planning, incorporating the wind generation infested with uncertainty.

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Forecasting of photovoltaic power using probabilistic approach

Cited by 2 publications

References 5 publications

Impact Assessment of Solar Power Generation Uncertainty on Smart Grid Reliability and Carbon Neutrality

Impact Assessment of Solar Power Generation Uncertainty on Smart Grid Reliability and Carbon Neutrality

Situational Awareness based Operational Reliability of Wind Energy System Incorporating Uncertainties

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