Cumulant-based correlated probabilistic load flow considering photovoltaic generation and electric vehicle charging demand
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Date
2017
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Publisher
Higher Education Press Limited Company
Abstract
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.
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Keywords
Buses, Charging (batteries), Electric load flow, Electric power distribution, Electric power transmission, Intelligent systems, Monte Carlo methods, Plug-in electric vehicles, Probability distributions, Secondary batteries, Vehicle transmissions, Battery-electric vehicles, Charging demands, Cumulant methods, Cumulants, Electric vehicle charging, Extended cumulant method, Photovoltaics generations, Plug-In Hybrid Electric Vehicle, Probabilistic load flow, Uncertainty, Plug-in hybrid vehicles
Citation
Frontiers in Energy, 2017, 11, 2, pp. 184-196