Due to an exponential increase in load demand and lack of capital resources to improve the existing transmission line infrastructure, power utilities are focusing on methods to increase the efficiency and loadability of existing infrastructure. This study presents a new approach for simultaneous optimum distributed generation (DG) placement and optimum tie-switch allocation based on maximisation of system loadability using discrete artificial bee colony algorithm. The proposed algorithm is tested on 16-Bus, 33-Bus and 69-Bus radial distribution test system. The obtained results show that with simultaneous optimum DG placement and optimum tie-switch allocation, better results are achieved rather than solving two problems independently. Further it was also found that the proposed method has improved the efficiency of the system in terms of maximisation of system loadability, reduction in power system losses, improving the kVA margin to maximum loadability and voltage quality improvement.
Optimal placement of new sensors is of great importance to enhancing distribution system monitoring and resiliency. Utilities are in need of a platform for an optimal sensor placement strategy other than the traditional experience-based strategy. In this paper, a sensor placement optimization tool (SPOT) is developed. It contains two selected modules based on industry priority: distribution system state estimation (DSE) and recloser placement (RP). The DSE module incorporates three-phase system functionality to reflect practical distribution systems with asymmetrical topology and unbalanced loading. In the RP module, the impact of microgrids is modeled. SPOT is timely since it can assist utilities in developing their own optimal sensor allocation strategies.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.