Browsing by Author "Prusty, B."

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Multi-time instant probabilistic PV generation forecasting using quantile regression forests
(Institute of Electrical and Electronics Engineers Inc., 2020) Tripathy, D.S.; Prusty, B.; Jena, D.; Sahu, M.K.
Long-term planning for the reinforcement of power systems with PV-integration requires multi-time instant PV generation uncertainty modeling. Probabilistic forecasting of PV generation plays a vital role in the uncertainty management in power systems with PV penetration. An ensemble approach for probabilistic PV generation forecasting, such as the quantile regression forests, proves to be a suitable model because it models the uncertain PV generation more accurately compared to single mean models. The inherent nature of forests to prevent over-fitting by "bagging" the training data is an advantage. Also, the optimal choice of the model hyper-parameters adds to its efficiency as a forecaster. Further, the stochastic nature of weather conditions needs the selection of sensible regressors for the proposed quantile regression forests framework based on the physics of the underlying phenomenon. Real-world data for PV generation collected at multiple instants of time from the USA are employed to test the efficacy of the proposed probabilistic forecasting. The proposed model is compared against the basic quantile regression approach in terms of the accuracy of the quantile forecasts as well as prediction intervals using suitable scores and error metrics. Â© 2020 IEEE.
Preprocessing of Multi-Time Instant PV Generation Data
(Institute of Electrical and Electronics Engineers Inc., 2018) Prusty, B.; Jena, D.
For the evaluation of system overlimit risk indices in a PV-integrated power system, PV generation data at specific instants of time (in each day for several years) are required to be collected. Such data have inherent annual periodic variations, which are different at various places. These variations are skewed and/or multimodal, which contributes significantly toward the overall variance of data and is primarily attributable to the Sun's position. This letter proposes a regression model that assumes the observed data as a function of few influencing factors related to the Sun's position and trend in data. Finally, the estimated variations using the developed model are removed from the data to characterize the unpredictable components. © 1969-2012 IEEE.
Probabilistic Load Flow in a Transmission System Integrated with Photovoltaic Generations
(Springer Verlag service@springer.de, 2019) Prusty, B.; Jena, D.
This paper compares the performance (solution accuracy and computational efficiency) of two hybrid methods (HMs) for probabilistic load flow (PLF) considering a mixture of discrete as well as correlated Gaussian and non-Gaussian input random variables. The PLF is accomplished on IEEE 118-bus test system with photovoltaic arrays installed at specific buses. The results of the HMs are compared with that of the existing methods such as combined cumulant and Gram-Charlier method, combined cumulant and Cornish-Fisher method, dependent discrete convolution method, and Monte Carlo simulation. Â© 2019, Springer Nature Singapore Pte Ltd.
Uncertainty Management in Utility Grid Considering PV Generation: An Operational Planner's Perspective
(Institute of Electrical and Electronics Engineers Inc., 2023) Prusty, B.; Bingi, K.; Jena, D.; Badi, M.; Mohan Krishna, S.
This paper discusses the importance of adopting a spatiotemporal model-based scenario generation technique by approximating a multivariate continuous stochastic process in a discrete form. The computational steps for such a model development are comprehensibly discussed via a case study using PV generation data collected from the USA. The model accuracy comparison through a detailed discussion of obtained results is likely to help novice researchers to choose the best model amongst the available options and tactically decide to improvise the modeling framework further. This systematically prepared paper is expected to help power engineers with enough confidence to execute operational planning decisions realistically. Â© 2023 IEEE.
Uncertainty Modeling Steps for Probabilistic Steady-State Analysis
(Springer Verlag service@springer.de, 2019) Prusty, B.; Jena, D.
This paper endeavors to deliver a detailed probabilistic uncertainty modeling approaches for power system planning and operation. The conventional uncertainty modeling approaches are reviewed, and the modeling challenges under large-scale integration of renewable generations are described. The modeling steps in various timescales (of the time horizons) for different applications are clarified inclusively. It is believed that the paper will help the novice readers in the probabilistic uncertainty modeling area. Â© 2019, Springer Nature Singapore Pte Ltd.