Computational Intelligence Based Technique for Multi-DG Installation in Transmission System

Nowadays, Distributed Generation (DG) has become more popular as a viable element of electric power systems. DG was designed as a small scale generation sources in order to locate at or near load center. The installation of DG is usually deployed within the distribution system to get the positive impacts such as reducing power losses, enhancing voltage profiles, differing costly for upgrading process, and reducing transmission and distribution network congestion. The purpose of this paper is to present a new technique namely Adaptive Embedded Clonal Evolutionary Programming (AECEP). The objective of the study is to employ AECEP optimization technique for loss minimization and voltage profile monitoring. This technique was developed to optimally determine the location and sizing of DG for the same purpose. Test was performed on the IEEE 69-Bus RDS for several cases in terms of loading conditions. The proposed AECEP was implemented for single DG and two DGs installation. The result of the proposed AECEP technique was found in a good agreement with those obtained from the EP and AIS in terms of loss minimization and voltage profile improvement.

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

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).

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.

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.

Distributed Generation (DG) has emerged as best alternative to conventional energy sources in recent times. Decentralization of power generation, improvement in voltage profile and reduction of system losses are some of key benefits of DG integration into the grid. However, introduction of DG changes the radial nature of a distribution network (DN) and may affect both magnitude and direction of fault currents. This phenomenon may have severe repercussions for the reliability and safety of a DN including protection coordination failure. This paper investigates the impact of DG on protection coordination of a typical DN and proposes a scheme to restore the protection coordination in presence of DG. Moreover, impact of different DG sizes and locations on DN’s voltage profile and losses has also been analyzed. The sample DN with embedded DG is modelled in ETAP environment and the simulation results presented show the effectiveness of the proposed protection strategy in restoring relay coordination of the network in both isolated and DG connected modes of operation.

In today's deregulated environment, there is a rapid increase in the load demand of any power system. The existing transmission network is incapable of supplying such a huge power demand. This results in voltage instability, poor voltage profile, increased system losses, system insecurity, disturbance and unreliability of the power system. The distribution network should be properly designed to enhance the system stability and loss reduction. In recent years, the power sector has experienced changes in distribution systems because of the increment in the usage of Flexible AC Transmission Systems (FACTS) and Distributed Generation (DG). By using DSTATCOM, SVC, etc. the system voltages, losses, power flow and stability are affected. Similarly with the use of DG. In this paper, an IEEE 33 bus radial distribution network has been taken into consideration. The weak buses are identified by use of stability index, namely Fast Voltage Stability Index (FVSI). The performance of the network has been assessed by optimal placement of DG & DSTATCOM in the weak area/zone. The criterion for performance assessment are Line Loss Reduction and Voltage Profile Improvement. Again a detailed comparative analysis is done between devices DG & DSTATCOM in terms of technical parameters such as enhancement in voltage profile & decrement of line losses. Further the analysis of the system is done by integration of both DG & DSTATCOM over a single bus and the results are examined and compared The working environment has been created in MIPOWER 9.0.1 and analyzed.

Distributed generation (DG) units has become an essential part of the smart grid due to increased penetration of renewable energy sources. These units help to improve power quality according to their position and control. Grids could face unexpected faults which affects power quality most especially voltage profile and power losses. This paper presents the potential application of (DG) units as a solution for improving voltage profile and mitigating power losses in loop distribution systems in case of faults for different positions of DG unit. Load flow analysis of a 66/20 kV distribution line network is performed to calculate the various values of voltage and power flow at each bus for different faults in each position of DG unit. Simulation of this distribution substation network with DG units have been developed in the MATLAB/Simulink Environment. The objective of this study is enhancement of voltage profile at various buses and the improvement in power flows in the distribution network with reduction in branch losses in the presence of fault.

The electrical power is transmitted through a high-voltage transmission system to low-voltage consumers on the distribution side. Because of the high R/X ratio, high current, and low voltage in distribution systems, active power loss (TPL) is much higher as compared to transmission systems. The economic incentive for distribution power utilities is to reduce losses in their networks. This inducement is generally the cost differential between actual and standard losses. As a result, when actual losses exceed standard losses, the utilities are economically penalized, while when the reverse happens, they are getting benefitted. As a result, the loss minimization problem in distribution systems is an excellent research subject for academics. All the methods for the loss minimization are different from each other by the means of formulation of the problem, tools used to minimize the losses, constraints taken, solution methods, and results are obtained. In most of the cases, the distribution system is interconnected and due to increment in load demand and limitations of loading capacity of distribution lines, the idea of distribution side generation came to maintain the system parameters like voltage profile, power loss, cost of operation, etc. To minimize losses, several methods are available like distributed generation (DG) using renewable energy sources and combined heat and power systems, capacitor allocation, reconfiguration of the network, and D-FACTS devices like dynamic voltage restorer (DVR), distribution static compensator (D-STATCOM), unified power quality conditioner (UPQC) allocation, etc. In this chapter, 33 and 69 bus mesh distribution system analysis is proposed with implementation of multiple wind turbines (WT) and multiple D-STATCOM. Advantages of WT are cost-effective generation, clean fuel source, sustainability, and less land requirement. Here, the Artificial Bee Colony (ABC) algorithm is used for finding optimal size and location of WG and D-STATCOM. ABC is an optimization technique that offers population-based search procedure in which artificial bees change individual food locations over time, with the aim of the bees being to locate the places of food supply with the most nectar and recognize it as the one with the greatest nectar. Here direct approach-based load flow method is used to find load flow parameters like bus voltage, branch currents, and power loss. The outcome verifies effective improvement in the system voltage profile, reduction in distribution feeder line loss, and increment in cost savings.

Distributed generation (DG) has drawn the attention of researchers and industrialists for quite a time now due to its numerous advantages. This paper proposes an analytical approach for optimal installation of multiple type DGs in a radial distribution network with consideration of constant, ZIP load model (combination of constant impedance, current, power load models) and load growth. It mainly emphasises on: (i) optimal installation of multiple type DGs with a constant load, (ii) impact of considering ZIP load model on optimal installation of DGs, (iii) proposing load growth factor with load growth rate of 7.5% for the planning period of five years, (iv) impact of considering ZIP load model and load growth simultaneously and (v) cost–benefit analysis. The results are compared for IEEE 33 bus distribution network.

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)

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.

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.

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.

This paper presents an approach for the optimal integration of multiple distributed generation (DG) sources in a radial distribution system. The integration of DG sources poses various challenges such as can lead to higher power losses caused by reverse power flow, voltage exceeding secure limits, voltage stability, power quality, and economic operation. To address these challenges, a hybrid algorithm is proposed which combines the benefits of both Evolutionary Programming and Firefly Algorithm. The proposed hybrid Evolutionary - Firefly Algorithm is employed for the determination of the optimal size of the DG sources. The objective of the proposed algorithm is to minimize the total system power losses and improve the voltage profile. The algorithm considers various constraints including the DG capacity limits and voltage limits. A comprehensive case study is conducted on a radial distribution system to demonstrate the effectiveness of the proposed approach. The simulation results show that the hybrid algorithm can find the optimal size and location of DG sources while achieving the desired system performance. The integration of multiple DG sources leads to a significant reduction in power losses and improved voltage profile. Furthermore, the proposed approach provides a flexible framework for the optimal integration of DG sources in radial distribution systems, allowing for the accommodation of different types and capacities of DG sources. The proposed technique is tested on the IEEE Reliability Test systems, specifically the IEEE 69-bus. The combination of DG at bus 61 and bus 27 yields a loss reduction index of 94%.