Loss Sensitivity and Voltage Deviation Index Based Intelligent Technique for Optimal Placement and Operation of Distributed Generators

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.

The optimal location of renewable distributed generations (DGs) into a radial distribution system (RDS) has attracted major concerns from power system researchers in the present years. The main target of DG integration is to improve the overall system performance by minimizing power losses and improving the voltage profile. Hence, this paper proposed a hybrid approach between an analytical and metaheuristic optimization technique for the optimal allocation of DG in RDS, considering different types of load. A simple analytical technique was developed in order to determine the sizes of different and multiple DGs, and a new efficient metaheuristic technique known as the Salp Swarm Algorithm (SSA) was suggested in order to choose the best buses in the system, proportionate to the sizes determined by the analytical technique, in order to obtain the minimum losses and the best voltage profile. To verify the power of the proposed hybrid technique on the incorporation of the DGs in RDS, it was applied to different types of static loads; constant power (CP), constant impedance (CZ), and constant current (CI). The performance of the proposed algorithm was validated using two standards RDSs—IEEE 33-bus and IEEE 69-bus systems—and was compared with other optimization techniques.

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.

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.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

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.

This paper presents a simple analytical method for obtaining optimal sizing of Distributed Generator (DG) units considering reduction of power loss and improvement of voltage profile in distribution system. Sensitivity analysis is used to find out the best location of DG placement. The optimum power penetration by DGs operating at unity power factor and lagging power factor is carried out using analytical method for constant power (CP), constant current (CI), constant impedance (CZ) and composite (CPIZ) loads. Effectiveness of the proposed method is demonstrated through an example.

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 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 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)

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.

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.

Introduction. Constant changes in electrical system loads lead to increased power losses and voltage drops, requiring effective strategies to improve grid performance amid changing power demands. Problem. Many studies assume constant loads when determining optimal locations for distributed generation (DG) units, when in reality, loads change throughout the day. These changes affect network performance and require efficient solutions that adapt to changes in loads demand to maintain system efficiency and stability. Goal. This research aims to optimize the locations and sizes of DG units to reduce power losses and optimize voltage profile, taking into account changes in loads hourly over a 24-hour period. Methodology. The study analyzes 24 hourly scenarios using 2 optimization techniques: the conventional particle swarm optimization (PSO) algorithm and the hybrid-dynamic PSO algorithm. A multi-objective function is adopted to reduce power losses and improve voltage profile at the same time. Results. The modified IEEE 33 bus system was used to verify the effectiveness of the proposed method. The hybrid-dynamic PSO algorithm has shown superior performance in reducing active and reactive losses compared to the traditional algorithm. It also contributed to a significant improvement in the voltage profile, demonstrating its high efficiency in dealing with changes in loads demand during time. Scientific novelty of this work lies in the integration of hourly load changes into the process of allocating DG units and using a hybrid-dynamic PSO algorithm that combines the benefits of PSO traditional and adaptation mechanisms, leading to realistic and more efficient improvement. Practical value. This methodology enhances the performance of the smart grid by reducing power losses and voltage deviation under daily load, ultimately reducing operational costs and improving grid reliability. References 28, tables 4, figures 10.

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.

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.