Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators

Abstract

Connections of distributed generation (DG) systems to distribution networks are increasing in number, though they may often be associated with the need of costly grid reinforcements or new control issues to maintain optimal operation. Appropriate analysis tools are required to check distribution networks operating conditions in the evolving scenario. Load flow (LF) calculations are typically needed to assess the allowed DG penetration level for a given network in order to ensure, for example, that voltage and current limits are not exceeded. The present paper deals with the solution of the LF problem in distribution networks with photovoltaic (PV) DG. Suitable models for prediction of the active power produced by PV DG units and the power absorbed by the loads are to be used to represent the uncertainty of solar energy availability and loads variation. The proposed models have been incorporated in a radial distribution probabilistic load flow (PLF) program that has been developed by using Monte Carlo techniques. The developed program allows probabilistic predictions of power flows at the various sections of distribution feeders and voltage profiles at all nodes of a network. After presenting theoretical concepts and software implementation, a practical case is also discussed to show the application of the study in order to assess the maximum PV peak power that can be installed into a distribution network without violating voltage and current constraints. A comparison between Deterministic Load Flow (DLF) and PLF analyses is also performed.