Optimal siting and sizing of multiple type DGs for the performance enhancement of Distribution System using Differential Evolution Algorithm

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.

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.

Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs

Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm

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 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.

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 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.

Distributed Generations (DGs) are most commonly used in Advanced Power Distribution System (APDS) for improving the voltage level and minimizing the power loss in distribution system. This paper represents a modified Teaching Learning Based Optimization Technique (TLBO) to reconfigure the distribution system and find the optimal placement and sizing of DGs to minimize the total system loss simultaneously. In modified TLBO radial trees are consider as a initial population and possible paths between nodes are considered as design variables for finding the optimal reconfigured route. A shift operator is used to change the switch position in order to find the desired switch combination. A simultaneous reconfiguration with DG placement is done by the proposed method with different sizes of DGs in search of optimal solution. Proposed approach is illustrated on the standard IEEE-33 bus test system considering real-time design practices.

Optimal placement and sizing of distributed generation (DG) is one of the important issues in the radial distribution system, playing a vital role in reduction power losses and total cost of the system. Optimal placement and sizing of DG have many advantages for the radial networks, which some of the important them are the reliability and voltage stability improvement, power loss and emission reduction. In this paper, Exchange market algorithm (EMA) as a new heuristic algorithm is used for solving multiple DG placement and sizing problem in the radial distribution system. EMA is consisted of two powerful searcher operators which are used to create and organize the random numbers of the initial population and it can be employed to find the optimum point in the optimization problems. In order to evaluate the purposes of this study, EMA is applied on three test systems, including 33, 69 bus IEEE test system and 94 bus Portuguese radial distribution system. The obtained numerical results of the optimization procedure are compared with recent studies in this regards and the resulting analyses indicated that the power losses in the radial network reduced to the minimum amount of ownership. The results also prove the effectiveness of the EMA as one of the powerful optimization tools in solving DG placement problem.

This study proposes probabilistic load flow (PLF) and chaotic lightning search algorithm (CLSA) solution optimal sizing and placement of distributed generator (DG) and distribution static compensator (DSTATCOM) in a radial distribution system (RDS). Minimization of active power losses in RDS has also been taken as an additional objective. The proposed CLSA based on Gauss/mouse map is proposed in this paper to avoid low convergence rate and trapping in local optima problems. The gauss/mouse map is used to change random LSA forking occurrence instead of using the random variable. Once the size and location of DG and DSTATCOM has been identified, PLF has been carried out to study their impact on the RDS performance. The performance of CLSA for loss minimization in RDS considering DG as well as DSTATCOM under various cases has been verified on the Matlab platform. To demonstrate the effectiveness of the proposed CLSA based scheme, some detailed simulation results are presented and analyzed for the 33-bus system (RDS-1) and the 69-bus test system (RDS-2). The obtained results demonstrate the superior performance of the proposed scheme in comparison with the conventional schemes.

In present days, power systems are more complex and highly stressed due to rise in population, and it requires more electrical demands. Presently, the distribution losses in India are approximately 25–30%. In recent days, distributed generation (DG) units are attracting the utility and consumers to use it largely in distribution system, to get more benefit from this like reduced power losses, increased reliability and maintain voltage stability. To get more benefits from DGs, it is necessary to find out its proper size and position. In this paper, a new analytical method is used to calculate the loss sensitivity factor at the buses, which is used to find best size and location of DG to increase the performance of distribution system. The mainly used DG units are PV unit and shunt capacitor for analysis of proposed method in one of the practical feeders with 41-bus emanating from 110/11 kV Alkola substation in Shivamogga, Karnataka, India. The loss sensitivity factor method is used to find optimal position and size of DG in the practical system. Power World Simulator (PWS) software is used to model the practical feeder, and simulated results are analysed for voltage magnitude and system losses. The result indicates that optimal position and size of DG will recover the voltage profile in the system within the suitable limits, and system losses are reduced.

Distributed Generation (DG) sources are becoming more noticeable in distribution systems, due to the increasing demands for electrical energy. Location and size of DG sources have effectively impact on the power losses in a distribution network. In this paper, imperialistic competition algorithm (ICA) is used for optimal locating and sizing of DG on distribution systems. The cost function is based on minimizing network power losses. Optimal sizing and allocation result in better voltage regulation. The performance analysis is executed on 33 and 69 bus systems with different scenarios to present the efficiency of the proposed method in comparison with other algorithms.

Distributed energy sources cadge today’s demand. The distributed generation is one of the alternative methods for reducing the gap between power generation and demand. The distributed generation confirms the efficacy of meeting escalating demand when placed at appropriate location with proper size. The objective of this work is to track the suitable place with appropriate size of the Distributed Generators to be installed in an unbalanced radial distribution test system to minimize the power losses while satisfying the operational constraints like rms value of bus voltage limit and the thermal limits of radial feeder lines. The proper location for placing the Distributed Generators is identified by estimating voltage stability index. A meta-heuristic approach based on Differential Evolution algorithm is utilized to get the optimal sizing of Distributed Generation. The proposed algorithm is implemented on the modified IEEE 37 bus unbalanced radial distribution network.

Distributed Generation (DG) is an auspicious solution to many power system problems such as voltage regulation, power loss minimization, effective operating power factor, etc. This paper presents a modified Artificial Bee Colony (ABC) algorithm to suit the searching for the optimal DG sizing and placement in a distribution network. The problem formulation and the proposed technique are tested on a 28-bus Malaysian distribution system. In confirming the effectiveness of this method to determine the minimum power loss which respects to its optimal size and placement, the proposed population-based meta-heuristic approach is compared with the Evolutionary Programming (EP) method for validation and accuracy. The analysis is focused on the sizing impact when considering the cost factor without neglecting the importance to maintain the minimal loss. The results have shown comparatively the same with EP in terms of the computational efficiency. Significance of savings could be achieved while having a low investment of DG installation with a minimum power loss.