Abstract
In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an appropriate level of accuracy.
Highlights
The great proliferation of integration of renewable energy into power systems has introduced additional uncertainty into power system studies in conjunction with the conventional sources of uncertainty from the loads and the availability of resources and transmission assets
Probabilistic power flow can provide a complete range of all possible values of desired variables and other useful statistical information for power system security analysis under uncertainty
The goal of this paper is to develop a fast and accurate probabilistic power flow methodology based on MCS for large-scale power systems interconnected with renewable energy sources
Summary
The great proliferation of integration of renewable energy into power systems has introduced additional uncertainty into power system studies in conjunction with the conventional sources of uncertainty from the loads and the availability of resources and transmission assets. Conventional Deterministic Power Flow (DPF) is one of the best-known mechanisms in the literature. It makes a computation for a specific operating point of the power system using constant values of loads, generations and network configuration, ignoring uncertainty in the computation. In analytical approaches [2,3,4,5,6], power flow equations are linearized and arithmetic algorithms such as convolution and cumulant techniques are used to obtain probability density functions (PDFs) or/and cumulative distribution functions (CDFs) of output random variables based on PDFs and CDFs of input random variables
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