Placement Of Multiple Distributed Generators Research Articles

Optimal placement and sizing of multiple distributed generators in power distribution networks

The primary objective of power systems is to meet the total power requirement, as well as to retain system reliability and security with the economic operation of the system. In recent years, exponential growth in power demand, lack of investments in up-gradation of electrical infrastructure, shortage in power generation and environmental concerns have been the key factors for unreliable and poor operation of the power systems. To tackle these problems, there is a need to rely on supplementary sources of generation and fortification of transmission capacity. Whereas, installation of large scale new generating units is not economical. An adequate number of small generating units incorporated is a more effective solution to meet the growing power demand. This research work focuses on the enhancement of voltage profiles while curtailing power loss by the optimal sizing and optimal placement of multiple distributed generators (DGs) at different buses. Different types of DG sizing and placement are carried out at 100%, 120% and 140% loading conditions.

Optimal WTGUs Placement for Performance Improvement in Electrical Distribution System Using Improved TLBO

The optimal location and size for Distributed Generation (DG) to get maximum advantages and improve the performance of electrical distribution systems (EDS) is a difficult challenge to solve. With EDS performance indices, this research offers a constrained generalized multi-objective performance index (MOPI) objective function. Improved Teaching Learning Based Optimization (ITLBO) is used to solve the proposed objective function by removing the convergence issue of basic Teaching Learning Based Optimization (TLBO). By optimizing the MOPI, the Wind Turbine Generation Unit (WTGU) is examined for single and multiple DG placement and sizing in EDS performance improvement. The ideal approach reduces the burden of EDS consumer loss allocation by minimizing power losses, improving the consumer voltage profile and voltage stability, increasing the line loadability margin (LLM), and increasing the line loadability margin (LLM). The performance test was carried out on a 33-node EDS and used MATLAB software to demonstrate the efficacy of optimal solution.

Optimal placement of multiple DGs in radial distribution systems to minimize power loss using BSA

: Distributed generation (DG) sources are becoming more important in electrical networks due to the increase of electrical energy demands. However, DG sources can have a profound effect on network power loss. Hence, optimal placement and size of DGs are extremely important. This study presents a backtracking search algorithm (BSA) based on optimal placement and size of multiple DGs within distribution systems so to reduce power loss. The BSA is a new heuristic algorithm. Two main DGs, photovoltaic and synchronous compensator, were used in the selected systems. To demonstrate the effectiveness of the proposed method, the results obtained by BSA are compared with a genetic algorithm (GA) as well as other results in the literature.

Multi-objective optimization based optimal sizing & placement of multiple distributed generators for distribution network performance improvement

Integration of Distributed Generations (DGs) into radial distribution network (RDN) is an emerging need to explore the benefits of renewable energy sources (RES). Increasing penetration of RES based DGs in RDN without proper planning leads to several operational problems such as excessive energy losses, poor voltage quality and load balancing. Hence, in this work, multi-objective optimization (MOO) problem is formulated by carefully chosen three conflicting objectives such as power loss minimization, enhancement of load balancing index (LBI) and aggregate voltage deviation index (AVDI). Teaching-Learning-Based-Optimization (TLBO) is used to optimize MOO problem considering placement of DGs at multiple locations in RDN satisfying the constraints on bus voltage magnitude, branch flows and DG size. Comprehensive simulation studies have been carried out to obtain optimal performance for 69-nodes RDN with the increasing penetration of DGs at multiple locations. It is shown that determination of optimal sizing of DGs at multiple locations in RDN with MOO results in lesser power losses, improved voltage profiles and better load balancing as compared to placement of single DG in RDN. Performance measures such as spacing and spread indicators are used for characterizing Pareto solutions for MOO problem. Such set of non-dominated solutions obtained from Pareto front during multi-objective TLBO gives proper guidelines to the utility operator about sizing and placement of DGs based on the assigned priorities to the objectives.

Optimal Placement Algorithm of Multiple DGs Based on Model-Free Lyapunov Exponent Estimation

The implementation of distributed generation (DG) becomes more common while causing a challenge to maintain the voltage stability in a power system. The optimal placement of multiple DGs in a planning stage can effectively solve this problem. In many cases, the best placement has been determined by the small-signal study like the modal analysis. However, it is acceptable only if the linearization of large-scale nonlinear power system is accurate. To overcome this, this paper proposes the new optimal placement algorithm for multiple DGs based on the model-free Lyapunov exponent estimation to maintain the voltage of system stable. In other words, the individual Lyapunov exponents of all buses are firstly estimated to determine the candidates for optimal placement of DGs. Then, the maximum Lyapunov exponent for each candidate is calculated to decide which bus is the best place to improve the voltage stability of system. Several time-domain simulation studies are carried out to verify the effectiveness of proposed algorithm. In particular, its performance is compared with that of the conventional method. The results show that the optimal placement of multiple DGs determined by the proposed algorithm improves the voltage stability of system much more effectively.

Reliability based optimal DG planning for a meshed distribution network

Distributed generation (DG) sources are becoming more popular nowadays because of their technoeconomic and environmental benefits. These benefits are maximized only when the DG sources are properly planned and installed in the distribution networks. Several studies have been carried out in this field considering various objectives and constraints, for single as well as multiple DG placement and sizing. In this paper, an optimization methodology based on particle swarm optimization is used for the optimal planning of multiple DG sources in a meshed distribution network. The solution consists of the possible DG locations, DG capacities, and its operating power factor. The objectives considered are the improvement of reliability indices namely the system average interruption frequency index and system average interruption duration index, reduction of real power loss, and voltage profile improvement. Unlike other works, reliability evaluation is done considering many realistic constraints and it is used as one of the criteria for DG planning. Reliability indices are evaluated using the encoded Markov cut set algorithm. The real power loss and the voltage index are obtained by means of a simple load flow technique. The stochastic nature of the renewable DG output and the load are taken into account during the reliability evaluation along with islanding probability. The optimal planning of DG sources is done for bus 4 of the Roy billinton test system consisting of seven feeders, 102 nodes, and 106 components. Optimal planning for solar based DG sources is done considering three different load models namely residential, commercial, and mixed load.

A bio-geography-based algorithm for optimal siting and sizing of distributed generators with an effective power factor model

In this paper, a bio-geography-based optimization techniqueis proposed for optimal sizing and placement of multiple distributed generators to reduce system loss and to improve system voltage profiles in electric distribution systems. Distributed generation paves the way for installing generating sources in the vicinity of the loads, for improving the power factor of the system, and thereby significantly reducing total system losses. An effective power factor model is proposed to pre-set the power factor of each distributed generator placed at various locations in a distribution system. The bio-geography optimization algorithm is enhanced with a differential learning scheme to deal with the problems of high dimensionality and complex constraints. A sensitivity factor approach is presented to reduce the search space for the placement of distributed generators in the system. Numerical simulations are performed on the IEEE 33-bus and IEEE 69-bus systems and the results of the proposed method are compared with other methods reported in the literature.

A multi objective approach for placement of multiple DGs in the radial distribution system

Insertion of distributed generation (DG) in existing distribution system has effectively improved its performance and operation. Many different approaches for the planning of distribution system with DG insertion are presented by researchers. In this paper a multi objective approach has been proposed to maximize the mutual benefits of both the distribution system operator and DG owner. The contradictory relationship between reduction in MVA rating of DGs and reduction of power losses of the system is the motivation for this multi-objective approach. The best compromised size of DGs in MVA, their operating power factors and positions are obtained to reduce the system active power loss along with the reduction of DGs size. The 69-bus and 85-bus radial distribution system are considered as test systems. The Pareto-front of non-dominated solutions is obtained by using multi-objective differential evolution (MODE) optimization algorithm. The performance of MODE algorithm is also compared with that of multi-objective particle swarm optimization (MOPSO) algorithm. The different system operating indices such as active power loss, reactive power loss and voltage deviation are evaluated to show the effect of the best compromised solution of DGs placement in distribution system.

Multiple Distribution Generation Location in Reconfigured Radial Distribution System Distributed generation in Distribution System

Distribution system reconfiguration is one of the major approaches to reduce the losses in the distribution system. With the integration of Distributed Generation (DG) in distribution system, there can be further improvement in voltage profile and further loss reduction in the reconfigured system. The main contribution of this paper is: (i). Proposing novel and modified novel power loss sensitivity methods for finding optimal locations for placement of multiple DGs, (ii) obtaining the sizes of distributed sources for reduced losses and better voltage profile, and (iii) Study of variation of multiple DG sizes taking the effect of reconfiguration of the distribution system, (iv) Study of the impact of DG sizes taking into consideration the power factor. The proposed method has been implemented and tested on IEEE 33 bus distribution system.

Stud Krill herd Algorithm for multiple DG placement and sizing in a radial distribution system

In this paper a new meta-heuristic technique, Stud Krill herd Algorithm (SKHA) is employed for the solution of optimal placement and sizing of Distributed Generation (DG) in radial distribution system. The main objective is to minimize the line losses considering various constraints like voltage limit, DG real power generation limit, power balance constraint and DG location constraint. Krill movement is based on the two factors – minimum distance of the Krill individual from food and highest density of the herd and for better performance adaptive genetic operators, stud selection and crossover (SSC) are included. The proposed algorithm is implemented on 33, 69 bus IEEE test system and 94 bus Portuguese radial distribution system. The results are compared with recently developed heuristic and analytical methods from the literature. The outcomes reveal the effectiveness of the algorithm.

Improved Sequential Quadratic Programming Approach for Optimal Distribution Generation Deployments via Stability and Sensitivity Analyses

Integrating distributed generation into an electric power system has an overall positive impact on the system. This impact can be enhanced via optimal distributed generation placement and sizing. In this article, the location issue is investigated through stability and sensitivity analyses. Distributed generation rating, on the other hand, is formulated as a non-linear optimization problem subject to high non-linear equality and inequality constraints. Sizing the distributed generation optimally is performed using a modified sequential quadratic programming method. The sequential quadratic programming is improved by incorporating the fast and flexible radial power flow routine, which was developed in an earlier work, to satisfy the power flow requirements. The proposed equality constraints satisfaction approach drastically reduces computational time requirements. This hybrid method is compared with conventional sequential quadratic programming, and the results are in favor of the proposed technique. The approach is designed to handle optimal single and multiple distributed generation placement and sizing with specified and unspecified power factors. A 69-bus distribution system is used to investigate the performance of the proposed approach.