Optimal Placement and Sizing of Distributed Generations for Power Losses Minimization Using PSO-Based Deep Learning Techniques

The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch

The reactive power dispatch （RPD） problem is a very critical optimization problem of power system which minimizes the real power loss of the transmission system. While solving the said problem, generator bus voltages and transformer tap settings are kept within a stable operating limit. In connection with the RPD problem, solving reactive power is compensated by incorporating shunt capacitors. The particle swarm optimization （PSO） technique is a swarm intelligence based fast working optimization method which is chosen in this paper as an optimization tool. Additionally, the constriction factor is included with the conventional PSO technique to accelerate the convergence property of the applied optimization tool. In this paper, the RPD problem is solved in the case of the two higher bus systems, i.e., the IEEE 57-bus system and the IEEE ll8-bus system. Furthermore, the result of the present paper is compared with a few optimization technique based results to substantiate the effectiveness of the proposed study.

Reliability and efficiency enhancement of a radial distribution system through value-based auto-recloser placement and network remodelling

The electric distribution system(EDS)is prone to faults leading to power interruptions.The present energy market demands that electricity utilities invest more in different measures to improve the performance of the EDS.The approach proposed here details a composite dual-phased methodology to improve the reliability and efficiency of the power delivered by the EDS.In the first phase,the optimal allocation of auto-reclosers(AR)is undertaken by employing a newly formulated algorithm.The determination of the total number and location for AR placement is based on the economic analysis of two factors,i.e.,AR investment-maintenance cost and total benefit earned in terms of reliability improvement due to AR placement.The analysis also takes into account the impact of power outages on different load types,the load growth rate,and the inflation rate.Further,to enhance the efficiency of the AR-incorpo-rated EDS,the technique of Radial Distribution System Remodelling is employed in the second phase.This method searches for a radial configuration that delivers power at minimum line losses.These phases comprising complex combinatorial operations are aided by a fresh hybrid of the Sine Cosine Algorithm,Krill Herd Algorithm,and a genetic operator of Differential Evolution.The results obtained from its application on the IEEE 69-bus distribution test system prove the credibility of the suggested formulation.