Optimization of Capacitor Bank Allocation in Radial Distribution Systems in the Presence of Voltage-Dependent Loads Using Genetic Algorithm

This study presents a pelican optimization algorithm (POA) based technique for optimizing the integration of capacitor banks in radial power distribution systems. The proposed approach adopts a hybrid approach that combines the loss sensitivity factors (LSF) and the POA to determine the appropriate placement and sizing of the capacitor banks, respectively, for best system performance. The proposed methodology is tested on the IEEE 33-bus distribution system using MATLAB software. The performance for integrating two capacitor banks is compared to that of the particle swarm optimization (PSO) and the genetic algorithm (GA) methods. The proposed POA method achieved a significant improvement in system performance. Specifically, it reduced active power losses (PLoss) by 68.08%, which surpasses the reductions achieved by PSO (61.79%) and GA (57.73%). In the case of reactive power loss (QLoss), the POA-based approach also proved superior with a 39.035% reduction, while PSO and GA-based methods achieved 28.50% and 22.59% reductions, respectively. These results underscore the effectiveness of the POA-based approach in capacitor bank placement and sizing, making it a promising candidate for enhancing voltage profiles and minimizing power losses in radial distribution systems.

In this paper a practical power system planning and control strategy based on a new adaptive sine cosine arithmetic optimization algorithm (ASC_AOA) is proposed to enhance the technical performances of radial distribution (RD) systems. The main objective considered in this study is to optimize the location and the size of shunt compensators based STATCOM devices and multi distributed generation to reduce the total power losses, and to maximize the loading margin stability of practical RD systems. As well confirmed in many recent researches, the success of metaheuristic algorithms is related to the interactivity between intensification and diversification stages. In this study, an adaptive process based on sine and cosine functions is incorporated within the standard AOA to guide the search process toward the best solution. The particularity of the proposed variant (ASC_AOA) has been validated on many benchmark functions, and also applied for optimal automation control of multi DGs and shunt compensators based STATCOM Controllers to enhance the performances of various types of RD systems, such as the 33-bus, the 69-bus, and 85-bus. Obtained results are compared to many recent optimization methods. It is confirmed that the proposed variant achieves the best solution in all of the cases studies elaborated. The proposed variant seems to be a competitive technique and an alternative tool for solving various combinatorial planning and control problems of modern RD systems.

DGs Technology has become the center of attention of the researchers and engineers because it is considered as an appropriate solution to overcome the shortage of electricity supply and power quality problems. DGs placement in appropriate locations with the right type and size is a challenging issue. This research discusses the simultaneous placement and sizing of multi-types of DGs in unbalanced radial distribution systems for power loss reduction and voltage deviation improvement while maintaining harmonics at the standard limit. The optimizations are performed simultaneously on three parameters; the locations, the types and the sizes of DGs. Genetic Algorithm (GA) is employed to ensure the optimal point is reached. The proposed method is applied on Kaliasin East Java radial distribution systems. The results show that the placement of four DGs type III are the most effective in reducing power loss and voltage deviation than the other scheme of optimization. The size of each DG is 175 kW+166.25 kVar. The active power loss reduction is 51.16%. All of individual harmonic (iHD) and total harmonic distortion (THD) are below the standard limit.

The integration of distributed generation is one of the most effective alternative methods for enhancing the voltage profile and reducing power loss in radial distribution systems. This study investigates the impact of integrating photovoltaic (PV) generation on distribution systems, considering radial distribution networks. The IEEE 33 bus radial distribution system and Panbari feeder of the Dharan substation are selected as candidate systems. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are proposed for determining the optimal location and sizing of single and multiple PV installations, considering minimum power loss as the objective function. The results are analyzed in terms of voltage profile improvement and power loss minimization with PV integration. Additionally, a comparative analysis is conducted for the system with PV integration and PV with capacitor bank for reactive power injection. An economic analysis is performed to determine the optimal number of Distributed Generator (DG) integrations. Through sensitivity analysis, the most sensitive buses are identified, and Power-Voltage (P-V) curves are plotted for these buses in the base case and cases after integration of PV only and PV with capacitor banks. The results are examined and compared in terms of load margin limit and critical voltage point. Among single and multiple PV integrations, the integration of two PV installations is found to be most suitable based on economic analysis. Simulation results demonstrate that the integration of PV sources in existing radial distribution systems has a positive impact on steady-state voltage stability. Furthermore, improvements in voltage profile and significant reductions in system power loss are observed.

This paper presents optimized distribution loss evaluation in ring main distribution system with Distributed Generation (DG) placement, reconfiguration and capacitor placement using Genetic Algorithm (GA). This paper compare results obtained with Radial Distribution System (RDS) and Ring Main Distribution System (RMDS) and proves that loss can be minimized just by modifying the type of distribution network from RDS to RMDS and can further be reduced to high level by DG and capacitor placements at strategic locations. Genetic algorithm (GA) is used to evaluate highly non-linear problem of evaluating total power loss of RDS and RMDS which are subjected to operational equality & inequality constraints. The efficacy of this method is illustrated by comparative analysis of the results with Harmony search algorithm (HSA) and GA from the literature. The solution to the objective of minimization of total loss is obtained using MATLAB optimization toolbox and MATLAB programming environment on standard IEEE 33-bus test system with variety (different/various combinations) of case studies.

The increasing greatly demand leads to increase in system power losses and voltage profile disturbances. The need for more effective power distribution system has become more urgent. In this regards, shunt capacitors installation on radial distribution has always been an important research area to get the optimal allocations required for voltage profile improvement, system losses reduction and power factor correction. The developed study provides two-stages to evaluate the optimal allocation of capacitors in radial distribution system. In this paper, two stages are introduced. In the first stage, the loss sensitivity analysis with two methods is employed to select the most appropriate candidate capacitor placement. In the second stage, a novel hybrid particle swarm optimization (HPSO)algorithm is implemented to find optimal sizing and allocations of capacitors and their settings from the selected buses. The proposed algorithm has been tested on different test systems as 15-bus, and 34-bus IEEE standard radial distribution systems. Moreover, the proposed algorithm has been tested on different sizes of other radial distribution systems. In order to validate the proposed approach, the obtained results have been compared with other methods. The numerical results have been proved the capability of the proposed approach to find the optimal solutions with system losses reduction, voltage profile improvement and power factor correction. Numerical results have been evaluated by MATLAB package.

Optimal capacitor allocation in radial distribution systems for loss reduction: A two stage method

Voltage stability and reliability is the major concern in radial distribution system (RDS). Customer far away from the substation faces voltage reliability and system fluctuation issues. These issues should be resolved through different methodologies under smart grid paradigm. Studies proved that loop and mesh distribution systems (MDS) show better voltage reliability and stability than RDS. A comparative study of different load models is done in terms of losses, voltage profile and cost of active power losses in both radial and mesh distribution system. Two different multiple-criteria decision analysis (MCDA) techniques PROMETHEE and Weighted product model (WPM) are used to identifying the best decision by considering active power loss, voltage and cost of active power cost attributes. A distinctive voltage stability index (VSI) is used to find the critical bus which is highly sensitive to voltage collapse. The bus having lowest VSI value is the most critical bus which is optimal location for siting the distributed generation unit (DG). VSI is evaluated for both radial and mesh distribution network. Different load models; constant power (CP) and constant impedance (CZ) models are used in this paper. The impacts of CZ and CP load models have been observed on voltage profiles and system losses on IEEE 33 bus system.

Optimum Location and Size of DG Using GA with Sensitivity Analysis for a Real Time Radial Distribution System

The back-tracking search algorithm (BSA) is a new heuristic algorithm. BSA has two especially important properties: it is not sensitive to the initial value and has a single control parameter. This study presents the BSA-based optimal sizing and placement of distributed generations (DGs), capacitor banks (CBs), and thyristor-controlled series compensator (TCSC) in a radial distribution system (RDS). These elements are integrated separately and simultaneously in RDS. The objective function is power loss. The BSA is executed on IEEE 33 bus RDS. The obtained results are compared to a genetic algorithm (GA) and other algorithms in the literature. The results demonstrate that the BSA is more efficient and has the potential to find optimal solutions with less power loss. In this paper, optimal placement and sizing of DGs, TCSC, and CBs in a RDS is solved simultaneously using BSA for the first time.

This paper proposes procedures for optimal planning and operation of radial distribution systems (RDS). Optimal planning involves selection of the best cross-section of branches to be operated, such that the resulting RDS yields the desired performance for the existing and expanding load demand. A genetic algorithm (GA) is used to obtain the optimal branches of RDS by minimizing the sum of branch cost and present worth of the feeder energy costs, while keeping the voltage regulation within a prescribed limit and satisSying the growth factor. The multi-objective genetic algorithm (MOGA) is suggested to achieve more than one objective and satisfy the RDS constraints. After selection of the optimal branches, an optimal operation procedure of RDS is proposed using a distribution system software programming @SSP). This program is applied to obtain the optimal switched point to find the best radial reconfiguration system with minimum energy loss costs and satisfy the RDS constraints such as: branch voltage drop, thermal current carrying capacity and balanced power generation-load demand equation. Different studies are presented to illustrate the capability of the proposed optimal procedures using two real life power distribution systems.

Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network

For reliability enhancement of radial distribution systems, normally open tie switches are located in feeders. Using tie switches and sectionalizers, the configuration of feeders can be changed in fault conditions for supplying customers from other routes. This is called load restoration in radial distribution systems. In this paper, it is shown that reliability and success of load restoration have a connection with location and number of tie switches. Also, a novel approach for optimal determination of the number and location of tie switches using a genetic algorithm is proposed. In the optimization procedure, load importance using fuzzy membership functions, cost of energy not supplied and cost of tie switches are considered. The effectiveness of the proposed method is shown by simulation results in a radial distribution network.

For reliability enhancement of radial distribution systems, normally open tie switches are located in feeders. Using tie switches and sectionalizers, the configuration of feeders can be changed in fault conditions for supplying customers from other routes. This is called load restoration in radial distribution systems. In this paper, it is shown that reliability and success of load restoration have a connection with location and number of tie switches. Also, a novel approach for optimal determination of the number and location of tie switches using a genetic algorithm is proposed. In the optimization procedure, load importance using fuzzy membership functions, cost of energy not supplied and cost of tie switches are considered. The effectiveness of the proposed method is shown by simulation results in a radial distribution network.

This paper presents minimization of total power loss, improvement in bus voltage profile and branch currents using network reconfiguration, capacitor banks and optimum number of distributed generation (DG) units placement in radial distribution system (RDS). Genetic algorithm (GA) is used to determine highly non-linear problem of calculating total power loss of RDS which are subjected to operational equality and inequality constraints. The effectiveness of this method is illustrated by comparing the results with Harmony search algorithm (HSA) and GA from the literature. The solution to the objective of minimization of loss is obtained using MATLAB optimization toolbox and MATLAB programming environment on standard IEEE 33-bus test system with different case studies.