Solving The Active Distribution Network Reconfiguration (ADNR) Problem Taking Into Consideration A Stochastic Wind Scenario and Load Uncertainty By Using HBFDE Method
Abstract
-Past literature has attempted to solve the problem
of network reconfiguration with Distributed Generators
(DGs) without taking into consideration the intermittent
renewable at a close proximity. Distribution Network
Reconfiguration (ADNR) must account uncertain behavior of
loads and wind when the commercial wind based DG, Doubly
Fed Induction Generators (DFIG) supports a significant part
of network. In this paper, a new Hybrid Bacterial Foraging
and Differential Evolution (HBFDE) algorithm is considered
for the ADNR problem with minimum loss and an improved
voltage profile. In the HBFDE algorithm the Differential
Evolution (DE) algorithm is combined with the Bacterial
Foraging (BF) algorithm to overcome slow and premature
convergence of BF. Indeed, the proposed algorithm is based
on the evolutionary natures of BF and DE, to take their
advantage of the compensatory property, and avoid their
corresponding drawbacks. In addition, to cope with the
uncertainty behavior of loads and wind, a stochastic model is
presented to solve the ADNR problem when the uncertainty
related to wind and load forecast is modeled in a stochastic
framework on scenario approach basis. The proposed
algorithm is tested on the IEEE 33-Bus Radial Distribution
Test Systems. The results of the simulation show the
effectiveness of proposed algorithm real time and real world
optimization problems facing the smart grid.