Adaptive Embedded Clonal Evolutionary Programming for optimal DG installation

Distributed Generation (DG) has gained increasing popularity as a viable element of electric power systems. DG as a small scale generation sources located at or near load center is usually deployed within the distribution system. Presence of DG has many positive impacts such as reducing transmission and distribution network congestion, differing costly upgrades, and improving the overall system performance by reducing power losses and enhancing voltage profiles. To achieve these positive impacts from DG installation, the DG has to be optimally placed and sized. This paper presents a technique for DG location and sizing using an computational intelligence based on Evolutionary Programming (EP). The objective of this paper is to minimize the power losses which will also monitor the voltage profile at all the buses. The proposed technique was tested on the IEEE 69 bus radial distribution system. The results obtained by the presented technique are compared with the other technique namely Artificial Immune System (AIS).

Distributed Generation (DG) is commonly used to reduce active power losses on a distribution network. The optimal location and size of DG will result in minimum active power losses and voltage profile improvement. This research proposes Novel Voltage Sensitivity Index (NVSI) and Stability Index (SI) methods to determine the optimal location and analytical expression to find optimal size and location of DG in Makassar distribution system, Feeder Kima, 76 buses to minimize active power losses and to improve voltage profile. Therefore, DG interconnection has a significant effect on improving the quality of the distribution network. The results show that the sensitivity method does not lead to the best placement DG in reducing active power losses. However, it is an analytical expression, which is very effective in determining optimal location and size of DG to reduce active power losses and to improve voltage profile in Makassar distribution system, Feeder Kima, 76 buses. The most optimal location for DG placement is on Bus 73 (Mega Sakti Pyramid), with a DG size of 0.8515MW. These combinations reduce 45.77% active power losses and increase 1.7336% voltage profile.

Distribution system is the crucial part of the power system which needs to be monitored for quality since it is the last stage in delivering electricity to consumers. The main aim of suppliers is therefore to maintain excellent quality of supply with minimal support at the network level. This includes dealing with issues around voltage regulation and minimization of real power losses on the network which are reflected in a uniform voltage profile through various load points on the feeders. However, with increase in decentralization, Distributed Generation (DG) becomes the focal point of distribution networks and can be used to achieve the above mentioned objectives. DGs are used in this paper to minimize the losses along with an improvement in the voltage profile. The location of these DG units on the network is addressed as an optimization problem using the loss sensitivity index and voltage deviation index. The size of power injected by DG units is obtained with using an intelligent approach that combines Genetic Algorithm (GA) with neural networks. Different cases of DG in terms of real and reactive power injected are considered resulting in an improvement in voltage profile. The loss sensitivity and voltage deviation indices show the best result for the location of DG needed to minimize the power loss along with improvement in voltage regulation. This approach is tested on a 13-bus distribution network with different types of loads placed throughout the feeder.

The increasing electricity demand in transmission system has caused the power transmission system experiencing stress condition. This phenomenon has forced the system to need urgent additional supply to maintain system adequacy, in particular the de-regulated power system environment. Distributed generation (DG) has been identified as one of the possible solutions to address this issue. DG installation has the capability to reduce transmission loss and improve the voltage profile. This paper presents evolutionary programming (EP) technique for optimizing the sizing and locations in DG installation. In this study, several DGs have been installed to address the voltage profile improvement and loss minimization; implemented on the IEEE 30-Bus Reliability Test System (RTS). Results obtained from the study revealed that, installation of multi-DGs in a transmission system has significantly minimized the transmission loss along with voltage profile improvement.

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Distribution system is more prone to power losses as compared with the other part of the power system. Optimal integration of distributed generation (DG) in distribution system results in reduced power losses and improved bus voltages. In this paper, a combination of two well-known techniques namely teaching learning (TL) and harmony search (HS), known as teaching learning combined with harmony search (TLCHS) algorithm has been used to determine the appropriate sizes and locations of DG in distribution network considering power loss reduction and improvement in system voltage profile as the key objectives. In TLCHS algorithm, some strategies such as teaching-learning strategy, random mutation, local pitch adjustment and harmony memory consideration are employed for maintaining the balance between exploitation and exploration, and the parameters are changed dynamically. The TLCHS technique is applied to IEEE 33-bus and IEEE 69-bus test distribution networks and obtained results are compared with other well-established algorithms available in the literature which show the effectiveness and robustness of the TLCHS algorithm.

This paper deals with the optimal energy management approach by using multi-objective optimization algorithm for two modes of micro grid operation: non-autonomous and autonomous which includes the optimally allocated and sized Distributed Generation (DG) units working at optimal power factor. For micro grid operation, objectives are optimization of DGs sizes with loss minimization and voltage profile improvement. A multi objective optimization algorithm is proposed to achieve the targets. Voltage stability indices based priority list has developed to optimally allocate the DGs. After siting DGs, optimal sizing has been executed in terms of loss minimization for both modes of micro grid. Numerical experimentations have been carried out on DGs penetration levels increasing gradually from 0% to 100%. The proposed methodology is programmed under MATLAB software and it is tested for standard IEEE-15 bus and IEEE-33 bus radial distribution systems. 33-bus radial system with 100% of penetration level of DGs has been converted into autonomous micro grid for verification of the suggested algorithm. Reduction in losses and improvement in voltage profile proves the effectiveness of the proposed method.

Small electric generating units known as distributed generators (DG) resources are capable of supplying homes, businesses, and industrial sites with electricity through distribution feeders. We can decrease power loss and upgrade the voltage profile by placing DG in the best possible location. However, because they were installed in distribution feeders, the types, sizes, and locations of DGs have a critical effect on their values. Yet, in order for the power system to maximise the gains from such an installation, it is crucial to choose the correct size and location for a DG. In this research, we investigate the ideal positioning and scale of distributed generation (DG) for lowering losses and enhancing voltage profile in distribution networks. The optimal position and size of the DG are determined using the Gray Wolf Optimization (GWO) algorithm. A thorough investigation of IEEE 33-bus distribution systems is conducted. The results show a significant decrease in real power loss, reactive power loss and voltage profile improvement.

In Indonesia, electricity is a basic need with demand that continues to grow. PT PLN (Persero) projects an increase in electricity consumption of 8.9% by early 2022, highlighting the urgent need to address frequent problems such as blackouts, power losses, and voltage sags in the power distribution system. Effective solutions, including Static VAR Compensator (SVC) and Distributed Generation (DG), have been proposed to improve voltage stability and reduce power losses. This study evaluates and compares the performance of SVC and DG on a standard IEEE 14-bus system under increased load conditions. Using power flow analysis in ETAP, we simulate the installation of SVC at 15.99 Mvar and DG at 20.58 Mvar on bus 9, which shows optimal results. The findings show that DG slightly outperforms SVC in improving voltage stability and reducing power losses, with a 0.16% greater voltage increase and a 3.2 MW or 17.3% reduction in power losses. These results indicate that although both devices meet PLN’s voltage standards and improve power system efficiency, DG provides a slightly superior improvement in overall system performance.

In order to reduce power losses in a power system and to improve voltage profile, researchers have examined and studied optimizing Photovoltaic Power distributed generation (DG) which is location and size in a power system, but the results have some drawbacks. Several approaches developed to make this important issue more efficient, including coming up with new algorithms and improving those already in existence. Many of the proposed algorithms are only concerned with the real power loss, however. Voltage stability control is a critical factor in modern power systems, which makes incorporating reactive power losses in optimizing DG allocation for voltage profile improvement necessary. The goal of this work is to solve this issue by combining Genetic Algorithm and Improved Particle Swarm optimization to optimize DG size and location by considering both real and reactive power losses. Power loss sensitivity factors and real and reactive power flow factors used in identifying which buses will receive DGs. A MATLAB-based program was developed and tested on a test system using distributed generators, considering the proposed method. As compared to Genetic Algorithm, Particle Swarm optimization and Improved Particle Swarm optimization methods, the Hybrid Genetic Algorithm Improved Particle Swarm optimization method is better for reducing both real and reactive power losses.

The Point of Common Coupling (PCC) where suppliers’ responsibility and customers demand meet is of great concern due to increase degree of voltage variation assessment; valuable indicator of system conditions (voltage profile). Unstable condition of the power system outside operational or statutory limit, an adverse effect of nonlinear loads usually generate harmonics as well as fundamental frequency voltage variations and increase rate of power losses. These loads need to be compensated for. The major concerns of utility operations is to mitigate adverse effect of this system conditions. This research work focuses on optimal siting and sizing of Distributed Generation (DG) in a 43 bus distribution system. Power losses coupled with voltage deviation, considering objective function that compute present percentage losses in 11kV Dikko feeder, Abuja Electricity Distribution Company (AEDC), Suleja Distribution Network, Nigeria. We identified buses with poor voltage profile without DG installation and determined optimal sizing and siting of DGs where losses can be mitigated and power quality improved. ETAP version 12.6 2014 was used for load flow analysis to establish a decisive based case. The total load of the system considered was (3490 + j2700) kVA. Active and Reactive power losses in the system before DG installation were 246.300 kW and 289.903 kVAR respectively. DGs installation in the case study, has a considerable effects on loss reduction in the network. It is observed that 8.10% and 7.20% active and reactive power loss reduction was achieved while bus voltage improved by 0.4%. Genetic Algorithm Optimization techniques programmed in MATLAB 2015 software was used for optimal placement and sizing of the DG in the system.

Rapid depletion of fossil based oil, coal and gas reserves and its greater demand day by day necessitates the search for other alternatives. Severe environmental impacts caused by the fossil fuel based power plants and the escalating fuel costs are the major challenges faced by the electricity supply industry. Integration of Distributed Generators (DG) especially, wind and solar systems to the grid can offer viable solutions to address these challenges. This paper focuses on a new simple and fast load flow algorithm for finding the voltage profile and power losses in a radial distribution system (RDS). Backward Forward Sweep Algorithm (BFSA) has been practiced to find the voltage profile and power losses with the integration of various size of DG at different locations. Genetic Algorithm (GA) based BFSA is adopted in finding the optimal location and sizing of DG to attain an improved voltage profile and considerable reduced power loss. Simulation results show that the proposed algorithm is more efficient in finding the optimal location and sizing of DG in 15-bus radial distribution system. The obtained results are compared with the system modeled in Simulink.

Distribution Electric power system is the largest and the most complex system made by the mankind. The distribution network power system is being encountered by quickly rising load demand and it is detected that under certain critical loading circumstances, the distribution system poses maximum power losses and poor voltage profile and collapse in convinced areas. To overcome these problems integrating distributed generation (DGs) on the grid near to the load center is the better solution as compared to others. Though, for the DG to serve its purpose, its location and size have to be determined optimally. In this paper, Grid-Based Multi-Objective Harmony Search Algorithm (GrMHSA) has been utilized to determine the size and location of DG in the distribution system in Debre Markos town. By placing DG optimally, in addition to the reduction of the power loss in the distribution network, the proposed mechanism improves the node (bus) voltage profile of the system under consideration. A MATLAB program is developed to mitigate power losses and improve the voltage profile by optimally sizing and placing a DG in the distribution network. After sizing and placing the DG in the network, the total voltage deviation, active and reactive power losses are reduced by 93.42%, 81.63% and 82.45% for Debre Markos Feeder 3 and 85.20%, 84.94% and 85.73% for Debre Markos Feeder 4 respectively. The performance comparison of GrMHSA and MOPSO has been made and GrMHSA has been found better in terms of reducing voltage deviation and power losses in the system.

Now a days the usage of electrical power is increasing enormously and it is also becoming highly impossible in imagining the world without the electricity. So here is one of the techniques for maintaining the continuous supply i.e., by generating the electrical power in the distribution premises. Distribution Generation has been growing rapidly in power systems due to their capable solution for the issues like power system deregulation, to meet the demand of electric power and shortages in the electric power transmission capacities. Concept of Distributed generation (DG) plays an effective key role in the distribution networks. Among many of their merits, real power loss reduction, THD reduction voltage sag and voltage swell reduction and voltage profile improvement can be the salient merits of DG. Improper location of DG sources in to the electrical power system would lead to the increase energy or power loss, and also jeopardize the system operation. In this paper, determination of DG location is done by using Sensitivity Analysis and corresponding DG size is found by using Particle Swarm Optimization (PSO) to mitigate the real power losses in the distribution networks. Standard IEEE 15 bus test system is chosen to test the proposed PSO — Sensitivity Analysis approach. The results so obtained show the enhancement of voltage profile, reduction of real power loss using MATLAB.

Proliferation of electric demand and congestion in the existing network intensify challenges and problems for researchers and power system planners. Currently, these problems are mostly solved by Distributed Generation (DG). DG is placed near the customer end to meet the load demand economically, hence reducing the existing network burden. This paper attempts to present an overview of technological development carried out in the DG field. Numerous technical constraints must be considered while integrating DG in the system. The improvement in voltage stability, voltage profile, and reduction in power losses are the main benefits posed by DG integration. This paper also highlights the key issues associated with the DG allocation problem, types of DG, and methodologies for optimal DG units allocation.