Optimal Placement of Various Distributed Generations in Distribution Systems using Firefly Algorithm

The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

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.

This paper utilize two novel optimization techniques namely Particle Swarm Optimization (PSO) and Craziness based Particle Swarm Optimization (CRPSO) to find the optimum location and optimum size of single and two Distributed generator (DG) in distribution system. Minimization of active power loss in distribution network is carried out through DG placement. The method is tested with two standard IEEE test cases namely 33-node radial distribution network and 69-node radial distribution network. The node with minimum system power loss is considered as the optimum location for DG placement which in turn enhances the voltage profile of overall system. The results of two optimization techniques for optimum size and optimum location of DG placement are found competitive.

This work proposed a method for the best placement and sizing of the Distributed Generations (DGs) in the distribution systems network with aim to minimize the the total system losses. The total system losses includes the resultant of real power loss and reactive power loss. The main aim of presented work is to minimize the system losses and also to enhance the system's voltage profile. This paper is made from the fact that DGs are more advantageous to achieve the demand of power that are close to the load centers compared to those at the centralized power generation. This work considers 85-bus radial distribution network (RDN) for DG planning based on objective to minimize overall MVA losses of system. The desired objective is carried out with the help of MATPOWER/MATLAB and the comparison of obtained results with DG using cuckoo search algorithm is presented related to the base case results. At last, it can be concluded that cuckoo search is an efficient methodology for optimal size and placement of DGs.

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)

In modern power system, distributed generators (DGs) have become an effective method to reduce active power loss, improve voltage stability, etc.. In this paper, a new Improved Firefly Algorithm (IFA) is proposed to obtain optimal placement and capacity of DGs to improve power flow distribution and voltage stability, reduce active power loss in distribution system. Compared with the standard Firefly Algorithm (FA), the proposed IFA has better convergence speed and can obtain a smaller active power loss. In addition, the power loss sensitivity factors are introduced to steer the algorithm to seek the best position about the DGs. In order to verify the performance of IFA, IFA and FA are tested on the standard IEEE-33 and IEEE-69 systems to solve the problem of joint configuration optimization of multiple generators. The results of simulation show the active power loss of the distribution system decreases and the voltage stability of the system node improves. By comparing the results of the IFA and FA, a conclusion can be reached, the IFA is more effective and more suitable to reduce active power loss by reasonably allocating the capacity and placement of DGs than FA.

The power losses in distribution system are high, which form 70 – 80% of total transmission and distribution losses. High losses have severe impact on stability, reliability as well as economy. Therefore, minimization of these losses is very necessary. In this paper proposed various schemes to reduce the active power losses in distribution network, given as:- Optimum reconfiguration network,- Optimum Distributed Generation (DG) placement and- Optimum reconfiguration with optimum (DG) placement.Using Cymdist software to implement the optimal reconfiguration algorithm and proposed Genetic Algorithm (GA) to find the size and location, which programmed under MATLAB software package. Whereon the proposed methodology (GA) simplifies the problem by dividing it in two phases, namely Placement Planning Model (PPM) and Size Planning Model (SPM) thereby reducing the search space. It was the integration of the two methods were used after each method individually to obtain minimum real power losses with better bus voltage (better efficiency for network).To verify the proposed algorithms, IEEE 33-bus system and al – jihad neighborhood distribution system (Baghdad distribution sector) are tested. The simulation results are compared with proposed works in literature.

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.

This paper presents an approach to coordinated planning of distribution network (DN) and distributed generation (DG) considering uncertainties of output of DG and load. To overcome shortage of traditional separated planning of DN with DG and maximize positive influence brought by DGs, the multi-objective integrated planning model of DN and DG is established, in which the types, location, capacity of DGs, lines in need of being upgraded and lines to be built for access of new load point are taken into account as variables, and investment, power purchasing, network loss and power failure are considered as objectives. In face of uncertainties of load and output of DGs, first the corresponding probability models are established, then latin hypercube sampling (LHS) based on those is adopted to get samples and research scenarios are obtained through samples reduction. A multi-objective chaos improved PSO based on theory of pareto optimal solution set is proposed to solve the model. Finally, the method is applied in improved IEEE 33-node distribution system and effectiveness and feasibility of the model and algorithm are verified.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Turatbek Omorov, Beishenaly Takyrbashev, Taalaibek Koibagarov, Tilebaldy Zhanybaev; Identification and monitoring of power losses in distribution networks as a composition of AMR. AIP Conf. Proc. 5 January 2023; 2552 (1): 020002. https://doi.org/10.1063/5.0114527 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

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.

Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current flow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value.

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.

Distributed generations (DGs) have significant benefits in the electric power industry, such as a reduction in CO2 and NOX emissions in electricity generation, improvement of voltage profile in distribution feeders, amending voltage stability in heavy load levels, enhancement of reliability and power quality, as well as securing the power market. Despite the numerous advantages of DG technologies, weak capability in dispatching and management of DGs is a major challenge for distribution system operators. Hence, during recent years, several studies about various aspects of control, operation, placement, and sizing of DGs have been conducted. This paper presents a novel application of multiobjective particle swarm optimization with the aim of determining the optimal DGs places, sizes, and their generated power contract price. In the proposed multiobjective optimization, not only are the operational aspects, such as improving voltage profile and stability, power-loss reduction, and reliability enhancement taken into account, but also an economic analysis is performed based on the distribution company's and DG owner's viewpoints. The simulation study is performed on the IEEE 33-bus distribution test system and the consequent discussions prove the effectiveness of the proposed approach.

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.