Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Abstract

The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.Energies 2018, 11, 1018 2 of 27 generation [11,12]: reduction of power losses (due to transmission), improvement of voltage profiles and stability index, power factor enhancement, reduction of the harmonic distortion and increased line loadability, among others [13]. However, incorrect location or sizing procedures may result in voltage profiles out of conventional ranges, voltage fluctuations, line capacity violation, increased failure levels due to intermittent generation and higher costs associated to the DGs [14].Different methods have been developed to optimize the location and sizing of DGs. Those methods are aimed at reducing the computation time required and to improve the technical criteria of the grid, such as power losses, voltage profiles and power factor, among others [15,16]. For example, the work reported in [17] presents a hybrid method between the genetic algorithm proposed by Chu and Beasley (GACB) [18] and a heuristic approach to locate, select and size the feeders in a distributed generation environment. Such a strategy enables the reduction of the costs associated with the feeders (DGs) and the power losses, which is achieved by reducing the peak load using active power injection based on diesel-based DGs only. On the other hand, the work reported in [19] describes a multi-target approach based on the Particle Swarm Optimization (PSO) technique for locating the DGs, and an optimal flow analysis for sizing the DGs. That work considers several generators and different load models, thus enabling high penetration levels of distributed generation.A similar approach was presented in [20], which is a hybrid solution based on both the GACB and PSO. The main drawbacks of such a solution were the high level of power injection requested to the DGs and the lack of analysis of the computation time required by the proposed method. Another solution, based on the Loss ...

Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

“…This test feeder illustrated in Figure 7 is widely known in specialized literature as the Baran and Wu test system with 69 nodes and 68 branches with 12.66 kV of operating voltage [21]. This test feeder has 3890.7 kW and 2693.6 kVAr of total active and reactive power demand.…”

“…A mixture of the particle artificial bee colony with the HSA algorithm was shown in [20]. In [21], incremental learning and PSO algorithms were mixed. The authors of [22] present a PSO algorithm combined with a feasible solution search to optimize the reactive power dispatch in a wind farm test system.…”

Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

“…Javidtash et al [13] proposed a novel combination of nondominated sorting GA and fuzzy method to minimize four objective functions, namely, cost, emission, power losses, and voltage deviation, on a typical 34-bus test microgrid. Grisales-Noreña et al [14] proposed a population-based incremental learning (PBIL) algorithm to determine the optimal location of DGs and PSO to define the size those devices. The main objective is to reduce the computation time and active power losses and improve the nodal voltage profiles.…”

“…where DG Li represents the location of the DG in bus i and B L max represents the maximum location of the bus [4] CSA 33, 69, and 119 nodes Minimize active power losses and maximize voltage magnitude [5] Analytical and PSO 33 and 69 buses Minimize the power distribution loss [6] Analytical 33 and 69 buses Minimize power losses [7] LSF and IWO 33 and 69 buses Minimize losses and operational cost and improve the voltage stability [8] PSO 33 and 69 buses Minimize power losses [9] AGPSO 69 buses Minimize power losses [10] GWO 33 and 69 buses Minimize power losses [11] GA-PSO 33 and 69 buses Minimize losses and maintain acceptable voltage profiles [12] GA-ABC 33 and 69 buses Reduce the cost of the system and decrease RPLs [13] GA and Fuzzy 34 buses Minimize cost, emission, power losses, and voltage deviation [14] PBIL and PSO 33 and 69 buses Reduce active power losses and improve the nodal voltage profiles [15] PSO 30 buses Minimize the transmission losses [16] CSO 69 buses Maximize the reliability in the system [17] BSA 69 and 136 buses Reduce power losses and improve network voltage profile [18] BSA and Fuzzy expert rules 33 and 94 nodes Minimize the network power losses, consolidate the static voltage stability indices, and ameliorate the bus's voltage profile.…”

Voltage Regulation and Power Loss Minimization in Radial Distribution Systems via Reactive Power Injection and Distributed Generation Unit Placement

“…Distributed generation (DG) has a lot of advantages such as reduced investment costs, flexibility, reliability, peak power shaving and clean power [1][2][3][4][5][6][7][8]. Integrating DG units into the smart grid, however, causes the modern grid to encounter voltage control [9], power loss [10] and optimal placement [11] issues.…”