An Adaptive NRLF with PQV and P/Q Buses for Loss Minimization and Voltage Stability Improvement of Optimally Reconfigured and DG Integrated Radial Distribution Systems

Voltage stability has a major concern in power system operation. It is the ability of the power system to maintain acceptable voltages at all buses in the system under normal conditions and after being subjected to a disturbance. Voltage instability may result in voltage collapse of the system. Hence, assessment of voltage stability is important. Implementation of new equipment including high-power electronics-based technologies such as flexible AC transmission systems (FACTS) has become essential for improvement of operation and control of power systems. The project work aims at the enhancement of voltage stability in the radial distribution system by using shunt capacitor and FACTS controller. A stability index named line stability indicator (LSI) is formulated for voltage stability analysis. This indicator is tested on a standard IEEE 33 bus radial distribution system. The indicator is used to find the weak lines in the system. Placement of shunt capacitor and FACTS controller at the receiving end side of the weak bus results in improvement of voltage profile of the system. Cuckoo search (CS) algorithm is applied for optimal sizing of shunt capacitor and FACTS controller. Program is coded in MATLAB for the enhancement of voltage stability in the radial distribution system.

This paper presents a method for locating and sizing Distributed Generation (DG) units based on the enhancement of voltage stability and network total loss reduction in radial distribution systems. In this method, a voltage stability index is used to determine the weakest buses from the voltage stability point of view as the best candidates for installing DGs. Also, an evolutionary algorithm based on Pareto Strength is used for solving the DG sizing problem. This problem is configured as a multi-objective optimization problem. The objective functions are defined as minimizing total power loss, enhancement of voltage stability and voltage profile improvement. Also, the effects of DG operating power factor and DG penetration level on total network loss and voltage stability are investigated and the DGs with the ability of generating reactive power are used. The selected DG penetration level is considered as a constraint in the evolutionary algorithm for determining the optimal size of DGs. It is shown that the selection of optimum DG penetration level can optimize the objective functions simultaneously. The proposed method is tested on a standard IEEE 69-bus radial distribution network. The obtained results are compared with similar previous methods and the appropriate performance of the proposed method is verified.

This article presents a comparative study on two algorithms (Novel Cat Swarm Algorithm and Differential Evolution Algorithm) for voltage stability enhancement by decreasing the power loss in the radial distribution system. The novel Cat Swarm Algorithm (CSA) and Differential Evolution Algorithm (DEA) are utilized for improvising the voltage stability in the radial distribution system. The sample size is calculated using Gpower for two groups and there are 14 samples used in this work. Based on the results obtained CSA produces better results than DEA in terms of voltage stability enhancement. The significant value obtained is 0.148 (p>0.05, statistically insignificant). CSA based optimization method provides significantly better voltage stability enhancement (0.7334 p.u) than HSA based method (0.7099 p.u),

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.

This paper address an effective intelligent computational algorithm of Ant Lion Optimizer (ALO) is applied for solving reconfiguration problem in a Radial Distribution System (RDS). The proposed ALO method inspired by imitates the hunting apparatus of ant lions in nature. The prime objective of the problem is identifying optimal network reconfiguration simultaneously with DC allocation and sizing for power loss minimization and voltage stability enhancement in a Radial Distribution System. Numerical example with IEEE 33-bus system is considered to validate the performance of ALO. The simulation results of voltage profile, power loss, optimal location and size of DG are numerically and graphically presented. The proposed ALO approach effectively minimizes the power loss and enhances the voltage stability of RDS. The assessment of solutions in MATLAB software shows the usefulness of the ALO in the distribution network. It is observed that the proposed method performed well compared to the other available methods in literature.

This research investigates the impact of electric vehicle charging station on power loss, voltage stability, and reliability within radial distribution systems. With the escalating use of electric vehicles, understanding these effects is crucial for ensuring the stability and efficiency of radial distribution systems infrastructure. The study formulates the electric vehicle charging station allocation problem as a multi-objective function, integrating power loss, voltage stability, and reliability metrics expressed as the PSR index. Using the IEEE 69-bus system as a test system, the research explores the ramifications of electric vehicle charging station integration and proposes strategies to mitigate their effects. An innovative combination of distributed generation and distribution static compensator are employed to alleviate the impact of electric vehicle charging station on radial distribution systems. A recent optimization methodology called spotted hyena optimizer algorithm is introduced to determine optimal electric vehicle charging station locations, with results compared to other existing algorithms. The study assesses potential power losses induced by additional electric vehicle loads, explores strategies to optimize charging rates for minimizing resistive losses, and evaluates voltage stability and reliability implications. The important results include objective function values obtained using various algorithms: Spotted hyena optimizer algorithm (0.7683), cuckoo search algorithm (0.7709), bat algorithm (0.8035), and African vultures optimization algorithm (0.7856). The findings demonstrate that the proposed algorithm outperforms other algorithms in minimizing the objective function value, indicating its efficacy in optimizing electric vehicle charging station placement within radial distribution systems. In conclusion, this research contributes valuable insights into the challenges associated with integrating electric vehicle charging station into radial distribution systems and provides innovative solutions for optimizing electric vehicle charging station placement, thereby advancing the understanding and management of distribution systems in the context of electric vehicle adoption.

Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits

This paper presents the Stud krill herd algorithm (SKHA) implementation to find the optimal location and sizing of DG units in a radial distribution system. In recent years, distributed generation (DG) plays an important role in the electric power industry. DG units are connected to the system mainly for improving voltage stability, real power loss reduction, reliability, grid strengthening, and reduction of SO2, CO2 gas emission. The problem formulation is based on the multi-objective function. This work focuses on real power loss reduction, cost minimization, and improvement of voltage stability and formulates a single objective function with suitable weightage preference subject to various constraints like voltage limit, DG real power limit, power balance constraint, and DG location limit. The algorithm has been tested on IEEE 33 bus, 69 bus, and 94 bus radial systems at different load levels with DG placement. The obtained simulation results are compared with other methods. The comparison reveals that the proposed algorithm offers a better solution for the optimal placement of DG in the radial distribution system with faster convergence.

Distributed generation (DG) allocation is the most promising source for reducing network loss and enhancing bus voltage stability in a distribution system. Because of the vast availability and nonpolluting character of renewable energy resource, it is gaining more attention nowadays. The most widely used renewable-based DG (RDG) is wind turbine (WT) and solar photovoltaic (SPV). Power generation patterns of the WT and SPV modules are random and nonlinear because the power output of WT and SPV modules are dependent on wind speed and solar irradiation. These require a probabilistic model to represent the actual power generation. The present paper reflects the potency of WT and SPV modules for reducing system losses and enhancing voltage stability. A new hybrid gray wolf optimizer (HGWO) is proposed to solve the DG allocation problem. The proposed optimization method is tested on IEEE 12- and 15-bus radial distribution system (RDS) and it is found that the proposed HGWO has more potency in terms of loss reduction and voltage stability enhancement compared to the existing techniques.

Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement

Voltage instability and power loss are significant problems in distribution Systems (DS). However, these problems are usually mitigated by the optimal integration of distributed generation (DG) units in the DN. In this regard, the optimal location and size of the DGs are crucial. Otherwise, System performance will deteriorate. This study is conducted to place the DGs in the radial DS. Mayfly Algorithm (MA) is used to determine the optimal placement and size of the DGs to minimize power loss, increase voltage stability in radial DS. The simulation results showed a reduction in the percentage of power loss is 69.14% for three PV-type DG unit integration. The corresponding percentage of power loss reduction is 98.09 % for three WT-type DG units by installing DG units to the test System. Similarly, the minimum bus voltage stability improves to 0.959 per unit for three PV type DG unit integration. The VSI after DG allocation increases to 0.989 per unit for three WT type DG units by optimal installing DG units. Comparative studies have been conducted, and the results have shown the effectiveness of the proposed method in reducing the power loss and improving the voltage stability of the DS. The proposed algorithm is evaluated in the IEEE-69 bus radial DS using MATLAB.

Reduction of Line Losses and Enhancement of Voltage Stability in a Radial Distribution System with Renewable Distributed Generation - written by Martha Mbenge Kyule , Moses Peter Musau , Nicodemus Odero Abungu published on 2019/11/21 download full article with reference data and citations

This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.

The present paper describes a multi-objective optimization approach to determine the optimal placement and sizing of Distributed Generation (DG) units in radial distribution systems. The main objectives are to minimize the active power loss and to enhance the voltage stability. Non-dominated Sorting Genetic Algorithms II (NSGA-II) is used to obtain the set of Pareto optimal solutions, which form a numerous set of non-dominated solutions and will be employed by the Decision Maker (power system operator) to select the best compromise solution. A detailed performance analysis is applied on 12, 33, 69 and 85 bus systems to illustrate the effectiveness of the proposed method.

Stochastic assessment and enhancement of voltage stability in multi carrier energy systems considering wind power