Two States for Optimal Position and Capacity of Distributed Generators Considering Network Reconfiguration for Power Loss Minimization Based on Runner Root Algorithm

Abstract

Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration.

The Influence of Power Quality Indices on Active Power Losses in a Local Distribution Grid

“…Formula (24) represents the total PL of the MV three-phase system. Based on previous formula, it can be said that the inductive reactance of the power line contributes to the total PL in an MV grid, but does not participate in the APL.…”

Enhanced sunflower optimization for placement distributed generation in distribution system

“…To solve the DG optimization problem, not only common methods such as GA, ABC, HBMO and PSO are used, but also many recently developed algorithms have been successfully applied such as whale [9,10], harmony search (HS) [11,12], modified crow search (MCS) [13], adaptive cuckoo search (ACS) [14], fireworks algorithm (FA) [15], coyote algorithm [16], uniform voltage distribution algorithm (UVD) [17], hyper cube ant colony optimization (HCACO) [18], runner root [19] and modified plant growth simulation (MPGS) [20]. Compared with classical methods such as dynamic programming [21], linear programming [22] and mixed integer linear programming [23], methods based on general knowledge such as GA, ABC and the aforementioned methods often get better quality results when applied to the DG optimization problem.…”

A Reliability-Based Network Reconfiguration Model in Distribution System with DGs and ESSs Using Mixed-Integer Programming

“…Moreover, customers have more strict demands for service quality. Energy Storage Systems (ESSs) and DGs could restore power supply to affected customers when the distribution network is separated from the upper power grid due to a failure, which would improve the reliability and resilience of system [3,4]. Therefore, ESSs and DGs play an important role in restoring more customers in distribution system reconfiguration (DSR).…”