Analysis of Optimal Deployment of Several DGs in Distribution Networks Using Plant Propagation Algorithm

Abstract

In recent years, the substantial upsurge of electricity demand has directly impacted the performance of the distribution networks concerning the active power losses and voltage drops. In such circumstances, the distributed generators (DGs) could uphold these concerns if they are optimally deployed in terms of sizing and placement. For this reason, in current research, the optimal deployment of DGs has been proposed with the plant propagation algorithm (PPA) to simultaneously maximize the total active power loss reduction and to upgrade the magnitude of the minimum bus voltage. Alongside, the authors have examined four rounds of DGs. In that context, the optimal deployment of one DG is investigated in the first round. In each succeeding round, the number of DGs is increased: in the second round, this investigation is carried out for two DGs, for three DGs in the third round, and finally, for four DGs in the fourth round of the investigation. The effectiveness of the proposed PPA has been tested on IEEE 33 and 69-bus test networks in the load flow analysis, and results are compared with the standard optimization algorithms. Thereafter, a post deployment economic assessment based on loss calculation has been undertaken out as well. The ANOVA test has also been performed for statistical evaluation of standard algorithms. The simulation results exhibit that the proposed algorithm outdo other algorithms both technically and economically. It has been seen that as the deployment of DGs is increased, the total active power losses and voltage drops are also reduced. In terms of economic assessments, the total cost decreases with the increased deployment of DGs in IEEE 33-bus test network, whereas, the total cost increases with the increased deployment of DGs in IEEE 69-bus test network. INDEX TERMS Distributed generators, load flow analysis, sizing and placement of DGs, total active power losses, voltage drops, test networks, plant propagation algorithm (PPA).

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

“…The authors in [14] suggested the plant propagation algorithm (PPA) to maximize active loss reduction and minimum bus voltage by integrating 1-4 DGs in Type-I into IEEE 33 and 69-bus RDNs.…”

“…Also, comparison of the convergence and maximum iteration numbers with those in literature is listed in Table 9. No DG GA [8] EGOA [33] DAPSO [2] ALOA [59[ ALOA [36] PPA [14] GA PSO GA [8] EGOA [33] PSO GA DAPSO [2] ALOA [36] PPA [14] PPSO [1] 1DG 2DG…”

Fractional PSOGSA Algorithm Approach to Solve Optimal Reactive Power Dispatch Problems With Uncertainty of Renewable Energy Resources

“…In this section, ORPD is solved using FPSOGSA with RERs. With the uncertainties of the load, solar irradiance and the wind speed, voltage deviations and the expected stability index according to (17), (18) and (19). Table XI shows the percentage load demands for each scenario and the output powers from the 25 scenarios can be obtained.…”

“…However, they have some of difficulties to solve the intricated problem of ORPD such as trapping into the local minima, premature convergence and the algorithmic complexity. To resolve these problems, the development of the new meta-heuristic optimization techniques like differential evolution technique, whale optimization algorithm, sine cosine algorithm, moth-flame optimization, ant lion optimizer algorithm, cuckoo search algorithm, plant propagation algorithm, grey wolf algorithm and particle swarm optimization [14][15][16][17][18][19][20][21] are developed to overcomes these issues. The new hybrid techniques are used for the conventional thermal units to solve ORPD.…”

Optimal Allocation/Sizing of DGs/Capacitors in Reconfigured Radial Distribution System Using Quasi-Reflected Slime Mould Algorithm

“…The objective function formulated for the studied optimization methodology aims towards active and reactive power losses reduction, improvement in bus voltage profile and high reliability index of the system. The plant propagation algorithm is used in [16] for the optimal deployment of the DGs by simultaneously maximizing the total active power loss reduction and upgrading the magnitude of bus voltages. In [17], a two-stage gametheoretic approach is employed for residential PV panels placement planning integrated with the energy shaving mechanism to increase financial benefits.…”