Browsing by Author "Prusty, B.R."

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A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach
(Elsevier Ltd, 2017) Prusty, B.R.; Jena, D.
A power system with large integration of renewable energy based generations is inherently associated with different types of uncertainties. In such cases, probabilistic load flow is a vital tool for delivering comprehensive information for power system planning and operation. Efforts have been made in this paper to perform a critical review on different probabilistic load flow models, uncertainty characterization and uncertainty handling methods, since from its inspection in 1974. An efficient analytical method named multivariate-Gaussian mixture approximation is proposed for precise estimation of probabilistic load flow results. The proposed method considers the uncertainties pertaining to photovoltaic generations and load demands. At the same time, it effectively incorporates multiple input correlations. In order to examine the performance of the proposed method, modified IEEE 118-bus test system is taken into consideration and results are compared with univariate-Gaussian mixture approximation, series expansion based cumulant methods and Monte Carlo simulation. Effect of various correlation cases on distribution of result variables is also studied. The effectiveness of the proposed method is justified in terms of accuracy and execution time. © 2016 Elsevier Ltd
A detailed formulation of sensitivity matrices for probabilistic load flow assessment considering electro-thermal coupling effect
(IEEE Computer Society, 2017) Prusty, B.R.; Jena, D.
In recent times, use of an analytical method (AM) is prevalent in solving probabilistic load flow (PLF) problem for better computational efficiency. AMs are employed to power system models that endure linear relations between the result variables and input random variables via sensitivity matrices. The accuracy of a sensitivity matrix-based PLF model can be improved by considering the effects of environmental conditions on line parameters. Looking out for an opportunity to upgrade existing PLF model to foresee the strength of thermal resistance model, a temperature-augmented model is presented. A detailed mathematical formulation of the aforesaid model is deliberated. The influence of temperature-augmentation on distributions of resistances, temperatures, power flows, and power losses of the temperature dependent branches is studied in detail. Finally, a note on applicability of the proposed model in the assessment of various power system studies is discussed. Â© 2017 IEEE.
A sensitivity matrix-based temperature-augmented probabilistic load flow study
(Institute of Electrical and Electronics Engineers Inc., 2017) Prusty, B.R.; Jena, D.
This paper proposes a hybridmethod for probabilistic load flow (PLF) study to analyze the influence of uncertain photovoltaic generations and load demands on transmission system performance. Besides, the paper focuses on accurate approximation of multimodal distributions of result variables in a temperatureaugmented PLF model without using any series expansion methods. The effect of uncertain ambient temperature on result variables is discussed. Multiple correlation cases between the input bus powers are considered. The performance of the proposed method is investigated on modified New England 39-bus power system. The results are compared with four well-established analyticalmethods and Monte Carlo simulation. The effect of multiple input correlations on probability distributions of result variables is analyzed. © 2017 IEEE.
A spatiotemporal probabilistic model-based temperature-augmented probabilistic load flow considering PV generations
(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Prusty, B.R.; Jena, D.
The probabilistic steady-state forecasting of a PV-integrated power system requires a suitable forecasting model capable of accurately characterizing the uncertainties and correlations among multivariate inputs. The critical and foremost difficulties in the development of such a model include the accurate representation of the characterizing features such as complex nonstationary pattern, non-Gaussianity, and spatial and temporal correlations. This paper aims at developing an improved high-dimensional multivariate spatiotemporal model through enhanced preprocessing, transformation techniques, principal component analysis, and a suitable time series model that is capable of accurately modeling the trend in the variance of uncertain inputs. The proposed model is applied to the probabilistic load flow carried out on the modified Indian utility 62-bus transmission system using temperature-augmented system model for an operational planning study. A detailed discussion of various results has indicated the effectiveness of the proposed model in capturing the aforesaid characterizing features of uncertain inputs. © 2019 John Wiley & Sons, Ltd.
An efficient hybrid technique for correlated probabilistic load flow study with photovoltaic generations
(Institute of Electrical and Electronics Engineers Inc., 2017) Prusty, B.R.; Jena, D.
This paper proposes an efficient hybrid technique for probabilistic load flow study. A mixture of correlated Gaussian and non-Gaussian as well as discrete distributions is considered for input random variables. Distributions of desired random variables pertaining to the input random variables are found to be multimodal. Analysis using Gaussian mixture approximation is promising in this context, but computational burden increases significantly with the increase in number of discrete random variables. In contrast, the proposed method precisely obtains distribution of desired random variables in considerably less time without compromising accuracy. Multiple input correlations are effectively incorporated. Accuracy of the proposed method is examined in IEEE 14-bus and 57-bus test systems. Results are compared with combined cumulant-Gaussian mixture approximation method and Monte-Carlo simulation. Â© 2016 IEEE.
An improved sliding window prediction-based outlier detection and correction for volatile time-series
(John Wiley and Sons Ltd, 2021) Ranjan, K.G.; Tripathy, D.S.; Prusty, B.R.; Jena, D.
Steady-state forecasting is indispensable for power system planning and operation. A forecasting model for inputs considering their historical record is a preliminary step for such type of studies. Since the historical data quality is decisive in edifice an accurate forecasting model, data preprocessing is essential. Primarily, the quality of raw data is affected by the presence of outliers, and preprocessing refers to outlier detection and correction. In this paper, an effort is made to improve the existing sliding window prediction-based preprocessing method. The recommended reforms are the calculation of appropriate window width and a new outlier correction approach. The proposed method denoted as improved sliding window prediction-based preprocessing is applied to the historical data of PV generation, load power, and the ambient temperature of different time-steps collected from various places in the United States and India. Firstly, the method's efficacy through detailed result analysis demonstrating the proposed preprocessing as a better way than its precursor and k-nearest neighbor approach is presented. Later, the improved out-of-sample forecasting accuracy canonizes the proposed method’s concert compared to both the above techniques and the case without preprocessing. © 2020 John Wiley & Sons Ltd
An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling
(Elsevier Ltd, 2018) Prusty, B.R.; Jena, D.
In this paper, the risk assessment of a PV integrated power system is accomplished by computing the over-limit probabilities and the severities of events such as under-voltage, over-voltage, over-load, and thermal over-load. These aspects are computed by performing temperature-augmented probabilistic load flow (TPLF) using Monte Carlo simulation. For TPLF, the historical data for PV generation, ambient temperature, and load power, each collected at twelve specific time instants of a day for the past five years are pre-processed by using three linear regression models for accurate uncertainty modeling. For PV generation data, the developed model is capable of filtering out the annual predictable periodic variation (owing to positioning of the Sun) and decreasing production trend due to ageing effect whereas, for ambient temperature and load power, the corresponding models accurately remove the annual cyclic variations in the data and their growth. The simulations pertaining to the aforesaid risk assessment are performed on a PV integrated New England 39-bus test system. The system over-limit risk indices are calculated for different PV penetrations and input correlations. In addition, the changes in the values of TPLF model parameters on the statistics of the result variables are analyzed. The risk indices so obtained help in executing necessary steps to reduce system risks for reliable operation. © 2017 Elsevier Ltd
Comparison of two data cleaning methods as applied to volatile time-series
(Institute of Electrical and Electronics Engineers Inc., 2019) Ranjan, K.G.; Prusty, B.R.; Jena, D.
Out-of-sample forecasting of historically observed time-series inevitably necessitates the application of a suitable data cleaning method to assist improved accuracy of the obtained results. The existing data cleaning methods though work amply with nonvolatile time series; fail when applied to a volatile time-series. In this paper, the suitability of the k-nearest neighbor approach and sliding window prediction approach is tested on a set of nonvolatile and volatile time-series. The performance comparison is carried out considering the historical record of furniture sales data, PV generation, load power, and ambient temperature data of different time-steps collected from various places in the USA. Further, the effect of parameters allied with both the methods on the preprocessing result is also analyzed. Finally, possible reforms are suggested for the appropriate preprocessing of volatile time-series. Â© 2019 IEEE.
Cumulant-based correlated probabilistic load flow considering photovoltaic generation and electric vehicle charging demand
(Higher Education Press Limited Company, 2017) Bhat, N.G.; Prusty, B.R.; Jena, D.
This paper applies a cumulant-based analytical method for probabilistic load flow (PLF) assessment in transmission and distribution systems. The uncertainties pertaining to photovoltaic generations and aggregate bus load powers are probabilistically modeled in the case of transmission systems. In the case of distribution systems, the uncertainties pertaining to plug-in hybrid electric vehicle and battery electric vehicle charging demands in residential community as well as charging stations are probabilistically modeled. The probability distributions of the result variables (bus voltages and branch power flows) pertaining to these inputs are accurately established. The multiple input correlation cases are incorporated. Simultaneously, the performance of the proposed method is demonstrated on a modified Ward-Hale 6-bus system and an IEEE 14-bus transmission system as well as on a modified IEEE 69-bus radial and an IEEE 33-bus mesh distribution system. The results of the proposed method are compared with that of Monte-Carlo simulation. © 2017, Higher Education Press and Springer-Verlag Berlin Heidelberg.
A detailed formulation of sensitivity matrices for probabilistic load flow assessment considering electro-thermal coupling effect
(2018) Prusty, B.R.; Jena, D.
In recent times, use of an analytical method (AM) is prevalent in solving probabilistic load flow (PLF) problem for better computational efficiency. AMs are employed to power system models that endure linear relations between the result variables and input random variables via sensitivity matrices. The accuracy of a sensitivity matrix-based PLF model can be improved by considering the effects of environmental conditions on line parameters. Looking out for an opportunity to upgrade existing PLF model to foresee the strength of thermal resistance model, a temperature-augmented model is presented. A detailed mathematical formulation of the aforesaid model is deliberated. The influence of temperature-augmentation on distributions of resistances, temperatures, power flows, and power losses of the temperature dependent branches is studied in detail. Finally, a note on applicability of the proposed model in the assessment of various power system studies is discussed. � 2017 IEEE.
An efficient hybrid technique for correlated probabilistic load flow study with photovoltaic generations
(2017) Prusty, B.R.; Jena, D.
This paper proposes an efficient hybrid technique for probabilistic load flow study. A mixture of correlated Gaussian and non-Gaussian as well as discrete distributions is considered for input random variables. Distributions of desired random variables pertaining to the input random variables are found to be multimodal. Analysis using Gaussian mixture approximation is promising in this context, but computational burden increases significantly with the increase in number of discrete random variables. In contrast, the proposed method precisely obtains distribution of desired random variables in considerably less time without compromising accuracy. Multiple input correlations are effectively incorporated. Accuracy of the proposed method is examined in IEEE 14-bus and 57-bus test systems. Results are compared with combined cumulant-Gaussian mixture approximation method and Monte-Carlo simulation. � 2016 IEEE.
Estimation of optimal number of components in Gaussian mixture model-based probabilistic load flow study
(2017) Prusty, B.R.; Jena, D.
Gaussian mixture approximation (GMA)-based probabilistic load flow (PLF) is an efficacious approach for quantifying the uncertainties associated with non-Gaussian and discrete input random variables (RVs). GMA approximates these input RVs by an equivalent weighted finite sum of Gaussian components. Expectation maximization (EM) algorithm is a well-established approach to estimate the parameters of the mixture components. The critical aspect is to know a priori the optimal number of components approximating the non-Gaussian distributions. The estimation of optimal number of parameters is essential because the parameters with inappropriate components may not evaluate the mixture model accurately. This paper adopts a cluster distortion function-based approach to determine the optimal number of mixture components. The k-means clustering result pertaining to that optimal number is then used for EM initialization. PLF using multivariate-GMA is performed on two IEEE test systems, considering various types of input RVs and their multiple correlations. � 2016 IEEE.
Estimation of optimal number of components in Gaussian mixture model-based probabilistic load flow study
(Institute of Electrical and Electronics Engineers Inc., 2017) Prusty, B.R.; Jena, D.
Gaussian mixture approximation (GMA)-based probabilistic load flow (PLF) is an efficacious approach for quantifying the uncertainties associated with non-Gaussian and discrete input random variables (RVs). GMA approximates these input RVs by an equivalent weighted finite sum of Gaussian components. Expectation maximization (EM) algorithm is a well-established approach to estimate the parameters of the mixture components. The critical aspect is to know a priori the optimal number of components approximating the non-Gaussian distributions. The estimation of optimal number of parameters is essential because the parameters with inappropriate components may not evaluate the mixture model accurately. This paper adopts a cluster distortion function-based approach to determine the optimal number of mixture components. The k-means clustering result pertaining to that optimal number is then used for EM initialization. PLF using multivariate-GMA is performed on two IEEE test systems, considering various types of input RVs and their multiple correlations. Â© 2016 IEEE.
Modeling of correlated photovoltaic generations and load demands in probabilistic load flow
(2016) Prusty, B.R.; Jena, D.
This paper performs probabilistic load flow under the consideration of uncertainty pertaining to conventional generation, photovoltaic (PV) generation and aggregate load demand in power systems. Effect of PV penetration and bus power correlations on distribution of desired random variables (bus voltages and line power flows) is analyzed with the help of an efficient analytical method named modified cumulant method. Generation-generation and load-load correlation cases are considered. Effectiveness of the proposed method has been tested on three test systems such as Ward-Hale 6 bus, IEEE 14 bus and IEEE 30 bus. Results are compared with Monte-Carlo simulation. The effectiveness of the proposed method is justified in terms of accuracy as well as execution time. � 2015 IEEE.
Modeling of correlated photovoltaic generations and load demands in probabilistic load flow
(Institute of Electrical and Electronics Engineers Inc., 2016) Prusty, B.R.; Jena, D.
This paper performs probabilistic load flow under the consideration of uncertainty pertaining to conventional generation, photovoltaic (PV) generation and aggregate load demand in power systems. Effect of PV penetration and bus power correlations on distribution of desired random variables (bus voltages and line power flows) is analyzed with the help of an efficient analytical method named modified cumulant method. Generation-generation and load-load correlation cases are considered. Effectiveness of the proposed method has been tested on three test systems such as Ward-Hale 6 bus, IEEE 14 bus and IEEE 30 bus. Results are compared with Monte-Carlo simulation. The effectiveness of the proposed method is justified in terms of accuracy as well as execution time. Â© 2015 IEEE.
Modeling of power demands of electric vehicles in correlated probabilistic load flow studies
(Institute of Electrical and Electronics Engineers Inc., 2017) Bhat, N.G.; Prusty, B.R.; Jena, D.
In this paper, extended cumulant method (ECM) is applied to probabilistic load flow analysis. Input uncertainties pertaining to plug-in hybrid electric vehicle and battery electric vehicle charging demands in residential community as well as charging stations are probabilistically modeled. Probability distributions of the result variables such as bus voltages and branch power flows pertaining to these inputs are accurately approximated; and at the same time, multiple input correlation cases are incorporated. The performance of ECM is demonstrated on the modified IEEE 69-bus radial distribution system. The results of ECM are compared with Monte-Carlo simulation. Â© 2016 IEEE.
Review of preprocessing methods for univariate volatile time-series in power system applications
(Elsevier Ltd, 2021) Ranjan, K.G.; Prusty, B.R.; Jena, D.
Outlier detection and correction of time-series referred to as preprocessing, play a vital role in forecasting in power systems. Rigorous research on this topic has been made in the past few decades and is still ongoing. In this paper, a detailed survey of different preprocessing methods is made, and the existing preprocessing methods are categorized. Also, the preprocessing capability of each method is highlighted. The well-established methods of each category applicable to univariate data are critically analyzed and compared based on their preprocessing ability. The result analysis includes applying the well-established methods to volatile time-series frequently used in power system applications. PV generation, load power, and ambient temperature time-series (clean and raw) of different time-step collected from various places/weather zones are considered for index-based and graphical-based comparison among the well-established methods. The impact of change in the crucial parameter(s) values and time-resolution of the data on the methods’ performance is also elucidated in this paper. The pros and cons of methods are discussed along with the scope for improvisation. © 2020
Sequence operation theory based probabilistic load flow assessment with photovoltaic generation
(2015) Prusty, B.R.; Jena, D.
This paper proposes a probabilistic load flow approach considering source and load uncertainties. Usually influence of these uncertainties is not considered in deterministic load flow. These uncertainties are a challenge to identify a competent and accurate method for load flow studies. Source uncertainty such as photovoltaic (PV) generation and load uncertainty are modelled as probabilistic discrete sequences and sequence operation theory is applied for load flow analysis. The disturbance in load flow pattern is studied in the presence of PV generation. Correctness of assuming a specific parametric distribution for real PV generation data is verified. DC load flow model is used to implement the proposed method to save memory and reduce computational time. Probabilistic distribution of output random variables (RVs) using proposed method and cumulant method are compared with the distributions obtained using Monte-Carlo simulation. The analysis is carried out on Wood and Woollenberg 6 bus system. The results have clearly established the fact that, application of the proposed method has accurately evaluated the distribution of output RVs.
Sequence operation theory based probabilistic load flow assessment with photovoltaic generation
(Institution of Engineering and Technology, 2015) Prusty, B.R.; Jena, D.
This paper proposes a probabilistic load flow approach considering source and load uncertainties. Usually influence of these uncertainties is not considered in deterministic load flow. These uncertainties are a challenge to identify a competent and accurate method for load flow studies. Source uncertainty such as photovoltaic (PV) generation and load uncertainty are modelled as probabilistic discrete sequences and sequence operation theory is applied for load flow analysis. The disturbance in load flow pattern is studied in the presence of PV generation. Correctness of assuming a specific parametric distribution for real PV generation data is verified. DC load flow model is used to implement the proposed method to save memory and reduce computational time. Probabilistic distribution of output random variables (RVs) using proposed method and cumulant method are compared with the distributions obtained using Monte-Carlo simulation. The analysis is carried out on Wood and Woollenberg 6 bus system. The results have clearly established the fact that, application of the proposed method has accurately evaluated the distribution of output RVs.
Short-term PV generation forecasting using quantile regression averaging
(Institute of Electrical and Electronics Engineers Inc., 2020) Tripathy, D.S.; Prusty, B.R.; Jena, D.
The globally increasing demand for energy to carry out the various day-to-day activities needs renewable sources in conjunction with existing power plants. PV technology has seen tremendous growth over the past decades. However, the integration of PV generation to the power systems invites numerous planning and operational challenges. In the short-term, the real-time operation of PV-integrated power systems requires the characterization of the uncertainties associated with the PV generation. A probabilistic framework, such as the quantile regression averaging (QRA), has been successful in forecasting load power and electricity spot prices. This paper applies QRA to accomplish a probabilistic forecast of PV generation using its historical record from a rooftop installation at Lincoln, USA. This paper's main contribution is the use of two appropriate individual point forecasters, i.e., autoregressive conditional heteroscedastic and multiple linear regression models, to complement each other and make accurate quantile forecasts. The proposed model is used in the short-term forecasting of PV generation for the four major seasons up to two weeks ahead. A detailed result analysis shows that the combination of both models improves overall forecasting performance rather than using any of the models alone. Â© 2020 IEEE.