Optimization of distributed generation units in reactive power compensated reconfigured distribution network

Abstract

Capacitor Allocation (CA) and Network Reconfiguration (NR) are the traditional methods extensively applied by the researchers for power loss reduction and node voltage improvement in radial Distribution Network (DN) for the past four decades. In recent years, simultaneous optimization of CA and NR is considered to maximize the power loss reduction in a proficient manner in comparison to individual optimization of CA and NR. To solve the objective functions, this work proposes an application of Autonomous Group Particle Swarm Optimization (AGPSO) by optimal allocation and sizing of capacitors with and without NR, under four different cases, subject to satisfying operating constraints. In addition, to ascertain the impact of real power injection on further power loss reduction, this work considers placement and sizing of Distributed Generation (DG) units from single to three optimal nodes in capacitive compensated optimal DN. This proposed methodology is demonstrated using standard IEEE 33 and 69 bus test system and the results obtained by each test case have been compared with other optimization techniques. A significant amount of power loss gets minimized after optimal DG allocation in reactive power compensated optimal DN.

Using multi-objective sparrow search algorithm to establish active distribution network dynamic reconfiguration integrated optimization

“…Raut et al (2019) proposes a novel improved elite-Jaya algorithm to solve the simultaneous network reconfiguration and DG assignment problems, which converts the multi-objective into a single-objective optimization, ignoring the conflicting relationships between the objectives. Srinivasan et al (2021) proposes an autonomous group particle swarm optimization algorithm for active distribution network optimization, which first performs reactive power compensation and then network reconfiguration, but lacks dynamic analysis and without considering the impact of DG and dynamic loads on the system.…”

Adaptive particle swarm optimization approach to simultaneous reconfiguration and shunt capacitor allocation in radial distribution network

“…The fundamental loops obtained for the Ayepe 34-bus RDN are depicted in Table 3. The tie-switches displayed in the 9) 1032( 14) 344 (23) 433 (7) 783 (12) 736 (26) 531 ( 9) 1032 ( 14) 344 (23) 833 ( 12) 640 ( 21) 358 ( 28 The voltage profiles and VSIs for all the considered scenarios are displayed in Figures 9 and 10. It is clear from the figures that the bus voltages and VSIs significantly improve in scenario 2 to 6.…”

“…Juan et al proposed mixed-integer second-order cone programming formulation for the optimization problem using voltage dependent models [14]. Other optimization methods like mixed integer linear programming (MILP) [7], ordination optimization [15], bat algorithm [16], oppositional krill herd algorithm (OKHA) [17], cat swarm optimization (CSO) [18], modified flower pollinated algorithm (MFPA) [19], moth swarm algorithm (MSA) [20], chemical reaction optimization (CRO) [21], modified particle swarm optimization (MPSO) [22], and autonomous group particle swarm optimization (AGPSO) [23] have also been deployed to solve RDNR and SCA combinatorial optimization problem. The aforementioned studies in [7] - [23] implemented their solution techniques on standard IEEE radial distribution networks.…”

“…The proposed APSO is tested on standard IEEE 33-bus and a practical Nigerian Ayepe 34-bus RDNs. Although the authors in [13], [22] and [23] have worked on the simultaneous implementation of RDNR and SCA using variants of PSO, their techniques suffer from large number of infeasible configurations and radiality constraint violation. This is attributable to the random generation of the initial population of particles in their works.…”

Notes on the Dimension of the Solution Space in Typical Electrical Engineering Optimization Problems

“…The solution to these problems in electrical engineering contributes to improving the quality of the electricity service provided by distribution companies to all end-users, given that their objective functions are typically the minimization of the total grid energy losses [6]. Moreover, these are connected with the concept of energy efficiency due to the fact that energy loss minimization is directly linked to the reduction of power generation, which, in the case of the fossil fuels, implies minimizing greenhouse gas emissions from the electrical sector [7].…”