Optimum Size Of Distributed Generation Research Articles

Optimum Location and Size of DG Using GA with Sensitivity Analysis for a Real Time Radial Distribution System

Distributed Generation (DG) must be of appropriate size and location for getting the benefits in the distribution system. Most of the renewable generation is directly grid to a distribution substation to fulfill the load demand; this may not give techno-economic benefit to the utility. A radial distribution system is identified where the wind generation DG is connected at substation bus to fulfill the load requirements. From the study, it is observed that the system does not have any effect on loss as well as voltage profile with the inclusion of DG. This paper discusses a technique to reduce active and reactive power losses in an existing radial distribution system with wind generation connected to the grid. For obtaining minimum loss the evolutionary technique Genetic Algorithm (GA) is implemented. GA includes some parameters which have to be adjusted for getting reliable results. This work is part of the answer to this approach addressing this connective problem. The proposed methodologies being tested on practical radial system representing load with constant impedance models. The optimal size and location of DG in a radial distribution feeder obtained through the techniques with variation in options for GA operators for different values of population size, crossover fraction, and generation. After having a set of optimal location and size of DG by this methodology, with the available generation, the only location of DG changed as per optimal location, and afterward, both optimal location and size are implemented to observe the effect on active and reactive power loss. Test results prove that utilizing available generation at an optimal location reduction in a power loss of 47.81%and 83.66%with an optimum size of DG at an optimal location obtained by the proposed methodology can be obtained. Also, exceptional improvement in voltage at the bus is observed.

Gbest-guided artificial bee colony algorithm based simultaneous placement of distributed generation and shunt capacitor in distribution networks

This paper presents a combined placement of distributed generation (DG) and shunt capacitor in distribution network for the purpose of power loss reduction. The locations of DG and shunt capacitor are evaluated by using power loss index approach and index vector method, respectively. The population based Gbest-guided artificial bee colony meta-heuristic optimisation technique has been implemented to evaluate the optimum size of DG and shunt capacitor simultaneously. This methodology is being demonstrated on 33-bus and 85-bus distribution network. In addition to that, the two load scenarios are considered in this study. The technical analysis has been carried out for different combinations of DG and shunt capacitor. Comparison between various cases for power loss, minimum system voltage, voltage stability index and total voltage deviation are also mentioned in this paper. The numerical results obtained through proposed approach have been compared to the published research papers for showing its effectiveness.

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Nowadays the optimal allocation of distributed generation (DG) in the distribution network becomes the popular research area in restructuring of power system. The capacitor banks introduced in the distribution networks for reactive power compensation also have the capacity to minimize the real and reactive power losses occurred in the system. Hence, this research integrates the allocation of renewable energy DG and capacitor banks in the radial distribution network to minimize the real power loss occurred in the system. A two-stage methodology is used for simultaneous allocation of renewable DG and capacitor banks. The optimum location of renewable energy DG and capacitor banks is determined using the distributed generation sitting index (DGSI) ranking method and the optimum sizing of DG and capacitor banks is found out for simultaneous placement using weight improved particle swarm optimization algorithm (WIPSO) and self adaptive differential evolution algorithm (SADE). This two-stage methodology reduces the burden of SADE and WIPSO algorithm, by using the DGSI index in determining the optimal location. Hence the computational time gets reduced which makes them suitable for online applications. By using the above methodology, a comprehensive performance analysis is done on IEEE 33 bus and 69 bus RDNs and the results are discussed in detail.

Optimum placement and sizing of DGs considering average hourly variations of load

Abstract This paper presents a multiobjective technique for obtaining optimal sizing of Distributed Generation (DG) units considering both technical and economical factors of the distribution system. The technical factors include real power loss reduction, line load reduction and voltage profile improvement and the economical factors consider optimal DG investment cost. Three different Distributed Generation systems solar photovoltaic, biomass and wind system are considered for integration with the existing distribution system. Since solar photovoltaic system is not available at night time, only biomass and wind systems are operated and for day time operation all the three distribution generation systems are considered. A new sensitivity index based on voltage sensitivity and apparent load power is proposed for identification of optimal locations for DG placement. The optimum sizing of DG units operating at unity power factor and lagging power factor is obtained using GA for different load levels considering daily average hourly loading aiming at improving the technical performance of the distribution system with optimum investment on DG units. Simulation results are presented to show the advantage of the proposed methodology in terms of technical performance and annual economical savings of the distribution system.