Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
8 results
Search Results
Item 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.Item 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. © 2016 IEEE.Item 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. © 2017 IEEE.Item 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 LtdItem 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.Item 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.Item 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 LtdItem 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.