Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer

Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

Section: Proposed Methodologymentioning

confidence: 99%

Section: Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

“…Jayabarathi et al proposed a hybrid grey wolf optimizer, which is implemented by adding differential evolution operators for solving economic load dispatch problem in power systems. Sanjay et al used the same hybrid grey wolf optimizer for solving optimal distributed generation allocation problem in distribution systems. Heidari and Pahlavani implemented an efficient GWO with levy flight and tested on various standard benchmark function for efficiency evaluation.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Sampangi

Jayabarathi

2020

Summary In recent years, reactive power compensation techniques are receiving more attention across the globe from both the distribution network operators and research groups due to its ability in the reduction of network power loss, voltage deviation, and enhancement of voltage stability. In this paper, two novel and robust optimization techniques implemented for solving optimal capacitor (OC) allocation problem in various distribution networks are water cycle algorithm (WCA) and grey wolf optimizer (GWO). To crisscross the feasibility, GWO and WCA techniques are tested on practical Indian 28‐bus, 47‐bus, and 52‐bus and standard 33‐bus, 69‐bus, and 85‐bus radial distribution networks with uniformly varying load levels. The simulation results obtained by WCA and GWO are compared with other techniques like inclusion and interchange variable (IIV), direct search algorithm (DSA), and teaching and learning–based optimization (TLBO) and found to be better. In addition, from the obtained results, the algorithm's efficiency is evaluated in terms of exploration and exploitation, and convergence characteristics and computational time are discussed.

“…Fuzzy logic control was used to find required reactive power to regulate bus voltage within the acceptable limits. Hybrid grey wolf optimization algorithm was used in Sanjay et al to determine location and size of DGs to minimize line losses. Voltage management in distribution systems was addressed in two levels by controlling reactive power of renewable‐based DGs.…”

Section: Introductionmentioning

confidence: 99%

Optimal coordinate control of OLTC, DG, D-STATCOM, and reconfiguration in distribution system for voltage control and loss minimization

Murty

Sharma

2018

Summary This paper presents optimal coordination and management of on‐load tap changer (OLTC), distribution static compensator (D‐STATCOM), distribution generation (DG), and reconfiguration to maximize annual cost savings and improve voltage profile using hybrid optimization. An analytical approach is developed to find optimal setting of OLTC. Optimal reconfiguration of the network is derived using genetic algorithm. Optimal location of DG and D‐STATCOM is determined using gravitational search algorithm, and optimal ratings are obtained using interface with MATLAB and GAMS environment. Fourteen realistic case studies are investigated in this paper work. In the first scenario, regulating devices are disabled to monitor the voltage profile and energy loss of the test system. In the remaining cases, influence of OLTC, DG, D‐STATCOM, and reconfiguration is independently and simultaneously studied. In the last scenario, OLTC, DG, D‐STATCOM, and reconfiguration are enabled to maintain voltage security and minimize power losses. Simulation results are obtained for IEEE 69‐bus test system and compared with other existing methods available in the literature. The proposed technique is reliable and efficient to obtain global optimal solution. Simulation results clearly indicate that optimal coordination and management among OLTC, DG, D‐STATCOM, and reconfiguration is required to maximize annual cost savings and maintain voltage at each node as per statutory requirements. Moreover, the analysis has been carried out for time‐varying distribution system with realistic load model.