Abstract
Flexible AC transmission systems and distributed generation units in power systems provide several benefits such as voltage stability, power loss minimization, thermal limits enhancement, or enables power system management close to the limit operation points; and by extension, economic benefits such as power fuel cost and power loss cost minimization. This work presents a multi-objective optimization algorithm to determine the location and size of hybrid solutions based on a combination of Flexible AC transmission systems devices and distributed generation. Further, the work expands the types of FACTS usually considered. The problem is solved by means of a Tabu search algorithm with good results when tested in a network of 300 nodes.
Highlights
In deregulated markets, power transactions in transmission and distribution networks lead the power systems to operate close to their limits to maximize the benefits [1,2,3,4]
Devaraj et al [13] use a radial basis function network model to estimate the voltage stability level of the power system based on the L-index, and this way, detect how far the nodes are from voltage collapse
Costs are presented in annualized values, with the objective to obtain benefits regarding the total investment cost (TIC) and the total generation cost (TGC), including the power losses cost (PLC)
Summary
Power transactions in transmission and distribution networks lead the power systems to operate close to their limits to maximize the benefits [1,2,3,4]. There is no doubt that the installation of DG has benefits for both the consumer, the supplier and the network [6], but the increase in the penetration of the DG can cause several problems in the operation of the network [7] Finding the optimal FACTS and DG devices location for power system enhancement is a non-linear complex problem which involves economical, environmental and electrical variables. Several authors utilise intelligent systems to improve the voltage stability in real time, during the operation of saturated electrical networks. Tomin et al [14] present an automatic intelligent system for voltage security control based on a decision trees model, and use the L-index for the localisation of critical nodes
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