Optimal placement of distributed generators in distribution system using backtracking search optimization for various load models

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.

A new emerging trend of distribution networks is to use small generating units, known as distributed generation (DG), operating in parallel with the main grid. This kind of distribution networks has enabled DG to support power systems in fulfilling their requirements to increase power output as well as quality of power supply. In order to maximise benefits from the DG system, proper DG planning is necessary. The main purpose of this research is to maximise voltage support through optimal sizing and location of DG. A new methodology is developed to determine an optimal DG size and DG location on the distribution feeder for optimising system voltages. The developed technique is tested on a long radial feeder of a practical system and results are reported.

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.

The necessity of Distributed Generation (DG) in the modern power system has increased greatly. Optimal placement and sizing of DGs have a significant impact on the objectives of voltage stability enhancement, real and reactive power loss minimization and power system security and reliability. Different types of DGs such as Type I, Type II, Type III and Type IVare placed. The optimal placement of these DGs is found using Voltage Stability Index (VSI). The optimal sizing of these DGs is done using Differential Evolution (DE) algorithm. MATLAB simulation is carried out in standard IEEE 33-bus test system. The test is performed for various combinations of different types of DG. The results show that the voltage stability index along with Differential Evolution algorithm provides better voltage profile and power loss minimization as compared to the system without DG.

This paper presents a new framework to analyze the penetration of distributed generation (DG) in distribution systems (DS) based on modified Monte Carlo simulation. The placement and capacity of DG are generated as random variables with probability density functions following the optimality principle of the system. The method of power flows analysis is employed to evaluate the economic and technical aspect of the network both with and without DG sources inside. The results in different scenarios are combined with weighted factors to form the performance indices. The proposed framework is testified in a practical 15 MVA, 22 kV, 48-bus DS in Thai Nguyen, Vietnam. The simulation result shows that many advantages can be achieved with DG such as lower power losses, smaller voltage deviation, over-load reduction, etc. The proposed framework can serve as a platform for DS planning with DG sources in practice.

Minimizing power loss and improving voltage stability are crucial aspects of power systems, driven by transmission line contingencies, financial losses for utilities, and potential power system blackouts. Optimal allocation comprising the sizing and operating power factor—of Distributed Generation (DG) units and capacitor banks (CBs) significantly enhances power system efficiency. Efforts by power system operators and researchers focus on addressing issues related to power loss, energy loss, voltage profiles, and voltage stability through the strategic placement of DGs and CBs. Additionally, optimal DG and CB allocation protects the distribution system from unforeseen events and enables operators to run the system in islanding mode when necessary. The integration of DG units and CBs in distribution systems aims to enhance overall system performance. This research paper introduces a Water Cycle Algorithm (WCA) for the optimal placement and sizing of DGs and CBs. The proposed method targets both technical and economic benefits, considering multiple objective functions: minimizing power losses, reducing voltage deviation, lowering total electrical energy costs, and improving the voltage stability index. The WCA emulates the natural water cycle, from streams to rivers and rivers to the sea. Five different operational scenarios are evaluated to test the performance of this methodology. Simulations are conducted on distribution systems: the IEEE 69-bus test system and the Sankhu feeder network, a real system. The results demonstrate the superior performance of the proposed WCA compared to other optimization algorithms. The findings highlight the WCA's flexibility, efficiency, and significant improvements in economic benefits, establishing it as a promising approach for optimizing the placement of DG and CB in distribution systems.

The reliability of power supply to consumers is the main factor that is to be considered while designing, planning and operation of a distribution network. This problem can be solved by incorporating distributed generation (DG) at the consumer side itself. In order to enhance service availability, power quality, reliability, and loss reduction, DG is positioned optimally at the consumer end within the system. The optimal DG location and size is to be calculated in such a way that the total loss in the system should be minimal and cost savings should be maximal. In this study, for the investigation of effects of DG on reliability of distribution system, a practical 11 KV feeder with 41-bus, was employed. The selected practical feeder is modelled using the power world simulator (PWS) software and reliability of the system is verified by calculating various reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average system utility index (ASUI) and average service availability index (ASAI). After applying proposed loss sensitivity factor (LSF) method, the active and reactive power loss reduction are 41.30 % and 38.64 % respectively, least bus voltage is enhanced to 0.9405 pu from 0.8978 pu and the reliability indices are also improved.

Distributed generations (DG) can be used to enhance power generation systems and improve distribution system efficiency. However, the installation of DG units at non-appropriate location and sizing can result in negative impacts such as an increasing in power losses and violations of system constraints. In this paper, a new DG allocation planning tool is developed for determining the optimal location and sizing of DG in practical distribution systems. The proposed planning tool is developed by writing DIgSILENT Programming Language (DPL) script in DIgSILENT PowerFactory software. The optimally placed optimal power flow (OPF) with DG is formulated as a minimization of energy losses function and a maximization of benefit to cost ratio, subjected to system constraints including real and reactive power generation, line and transformer loading, voltage profile, step voltage change, reverse power, energy losses, short circuit level, and DG operating limits. All possible locations and sizes of DG installations are evaluated and then the optimal solution is selected from the best objective function value obtained. Test results on the practical 9-bus and 437-bus distribution systems taken from Provincial Electricity Authority (PEA) of Thailand indicate that the proposed planning tool can be used to determine the optimal DG allocation without violation of system constraints.

The continuous growth of power demand poses new challenge to the efficiency of power system. Therefore, distributed generation (DG) is widely concerned because it can provide clean, reliable and economic power. However, the location and sizing of DG connected to the grid shall be properly selected. In this paper, the voltage stability index and the line loss reduction rate are considered synthetically, and the two indexes are transformed into a single index (VSLR) to determine the optimal location of DG. According to this index, the node with high sensitivity and high average power loss reduction rate are selected as the optimum location of DG connected to the power grid, and the optimal access capacity of DG is determined by the algorithm proposed in this paper. The simulation experiments of IEEE30-bus are carried out with MATLAB\\ Matpower toolbox, and the results are compared with the existing research results. The results show that the method of studying the optimal location and sizing of DG in this paper emerges better performance.

In smart grid distribution systems, the applications of Shunt Capacitor Banks (SCBs) and Distributed Generations (DGs) are widely known and installed for system loss reduction, voltage stability improvement of system buses, and to free up the system loading. However, the installation of SCBs and DGs in distribution systems with distorted waveforms will increase the harmonic distortion level if they are not placed at appropriate locations. In this paper, a Multiobjective Grey Wolf Optimizer (MOGWO) is proposed to find the optimal size and locations of SCBs and DGs simultaneously with regard to the impact of ambient temperature on distorted distribution systems. Electric utilities will be able to reduce system active losses, enhance the voltage stability for system buses, release the section loading, and reduce the total harmonic distortion level. Fuzzy set theory is utilized to choose the optimum compromise solution from the Pareto front solutions of the MOGWO method. The proposed method is applied to IEEE 69-bus and real data taken from the Saudi Electricity Company (SEC). The results show a higher capability in finding optimum solutions for SCBs and DGs placement in distorted Radial Distribution Systems (RDSs) compared to conventional methods.

Optimal distributed generation placement in distribution system to improve reliability and critical loads pick up after natural disasters

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)

A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems

Actual operating conditions of Secondary Radial Distribution System are unbalanced in nature and for stable operation of the system, the distribution system engineer should account for operational imbalances within the system. With the continuing increase in load demand, future expansion of the network is to be done basing on the load flow study of the distribution system network and thus, it is one of the most important research fields for electrical engineering. Optimally placed Distributed Generation (DG) units reduce system losses and lead an improvement in the voltage profile, system reliability, load ability, voltage stability, voltage security and power quality. There are several techniques available for the optimum size and placement of DGs in the unbalanced radial secondary distribution system (URSDS). In this paper, in URSDS, optimal placement of DGs is determined by using modified sensitivity factor method and optimal size is determined by using Adaptive Particle Swarm Optimization (APSO) technique. The proposed technique is tested on 25 & 37 bus URSDS. Results show that network power losses are reduced and voltage profiles are improved in each phase with optimal allocation.

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.