Integration of Multiple Distributed Generation Sources in Radial Distribution System Using a Hybrid Evolutionary Programming-Firefly Algorithm

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.

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.

Proliferation of electric demand and congestion in the existing network intensify challenges and problems for researchers and power system planners. Currently, these problems are mostly solved by Distributed Generation (DG). DG is placed near the customer end to meet the load demand economically, hence reducing the existing network burden. This paper attempts to present an overview of technological development carried out in the DG field. Numerous technical constraints must be considered while integrating DG in the system. The improvement in voltage stability, voltage profile, and reduction in power losses are the main benefits posed by DG integration. This paper also highlights the key issues associated with the DG allocation problem, types of DG, and methodologies for optimal DG units allocation.

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)

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 paper contributes the optimal allocation of DSTATCOM (distribution static compensator) and DG (distributed generation) in radial distribution systems using exhaustive search method (ESM) to reduce the power loss and improvement of the voltage profile. The certain range of active and reactive powers have been injected simultaneously at each node by incorporating the corresponding size of DSTATCOMs and DGs respectively into the radial distribution system (RDS). On the basis of best reduction in power loss, the Size of DSTATCOM and DG are determined. Forward-Backward sweep load flow algorithm was used for the load flow solutions. The results obtained by proposed approach shows the optimal allocation and sizing of DSTATCOM and DG in RDS efficaciously reduces the power loss and improves the voltage profile. The IEEE-30bus RDS was used as a test system.

A major research is going on distributed generation (DG) which utilise renewable energy sources such as solar, wind, fuel cells, etc., for electric power generation. As the renewable energy sources are geographically widely spread and of low capacity, they can be connected to distribution networks. This paper proposes a three-phase power flow algorithm (PFA) for unbalanced radial distribution networks (URDN) with multiple installations of DGs and distribution static synchronous compensator (D-STATCOM). The proposed PFA utilised the special topology of radial distribution networks (RDN) to divide the network into different sections. The bus numbers and branch numbers of these sections are stored in row entries of BUS_NUM and BRANCH_NUM matrices, respectively. These matrices make the implementation of PFA simple. The accuracy of the three-phase PFA without the integration of DGs and D-STATCOM is tested on IEEE-13 bus URDN and the results are found to be very accurate with the literature. Several case studies are carried on IEEE-13 bus URDN with integration of DGs and D-STATCOM. The test results show that the proposed PFA can effectively handle different DG modelling and D-STATCOM and overall energy saving, reduced power loss and improved voltage profile are achieved with these integrations.

This chapter presents Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a swarm intelligence-based optimization technique, to solve Multi-Objective Reactive Power Management (MORPM) problem for minimization of active power losses, improvement of voltage profile, and minimization of total capacity of Reactive Power Sources (RPS) in Radial Distribution Systems (RDS). In an RDS having Distributed Generators (DGs), reactive power management problem can be solved by regulating reactive powers of DGs and of the reactive power compensation devices like FACTS devices, capacitor banks, etc. Efficacy of the proposed NSGA-II has been established by solving MORPM problem in IEEE 33-bus RDS penetrated with DGs and RPS units and by comparing the multi-objective reactive power management results with those obtained using multi-objective dragonfly algorithm, multi-objective differential evolution algorithm, and modified differential evolution algorithm.

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

The recent trends in electrical power distribution system operation and management are aimed at improving system conditions in order to render good service to the customer. Reforms in the distribution sector have given major scope for employment of distributed generation (DG) resources which will boost system performance. This article proposes a heuristic technique for allocation of multiple distribution generation sources in a distribution system. The allocation is determined based on overall improvement in network performance parameters like reduction in system losses, improvement in voltage stability, improvement in voltage profile. The hybrid of Genetic Algorithm with the proposed Network Performance Enhancement Index (NPEI) along with the heuristic rules facilitates determination of feasible location for insertion of DG sources. A priority list is prepared with decreasing values of NPEI so that the designer can select most feasible locations. The developed approach is tested with different t...

<p><span>Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.</span></p>

Power loss reduction is one the main reasons for placing distributed energy resources in distribution systems. In addition, improvement in voltage profile is another advantage which the distributed energy resources offers. Heuristic optimization techniques are robust and provide near optimal solutions. In this paper, two different heuristic optimization techniques namely differential evolution (DE) and teaching learning combined with harmony search (TLCHS) have been used for placement of distributed generation units at appropriate locations in a radial distribution system (RDS) with an objective to minimize power losses and to improve bus voltage profiles. TCLHS optimization technique is a combination of two well-known techniques namely teaching learning (TL) and harmony search (HS). The (DE) is an evolutionary algorithm, which is a powerful global optimizer for solving continuous optimization problem. In DE only three input parameters are needed to control the search process, namely the size of population (N), the mutation parameter (F) and the crossover rate (CR). The TLCHS and DE optimization algorithms have been applied on IEEE 33-bus and 69-bus systems. The results exhibit the effectiveness and feasibility of the two algorithms along with the considerable reduction in power losses.

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).

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 study presents a methodology using Particle Swarm Optimization (PSO) for the placement of single Distributed Generation (DG) in the radial distribution systems to reduce the power loss. The single DG placement is used to find the optimal DG location and sizing which is corresponded to the maximum power loss reduction. The proposed method is tested on the 26-bus radial power distribution system which modified from the Provincial Electricity Authority system in Thailand. The load flow analysis on distribution system used forward-backward sweep methodology. The simulation results show that PSO can obtain the maximum power loss reductions. The total consumption power is 8.49 MW and 5.97 MVAR and total power loss is 11.68 kW and 26.08 kVAR. This study can be verify that the PSO method can solve the best placement and sizing on the real system.