Multiobjective placement and sizing of distributed generations in distribution system using global criterion method

Abstract

SummaryThis paper presents a multiobjective method for obtaining optimal size and site of distributed generation (DG), to reduce loss, and DG investment cost while improving the voltage profile in primary distribution networks. To solve the multiobjective problem, a classical technique known as global criterion method is implemented to form a novel objective function equation, which is custom-built for a distribution system. The application of the global criterion method is explained using a DG that is capable of injecting real power, ie, unity power factor DG, and then extended to DGs capable of injecting reactive power. The method is validated for 2 distribution systems. Results show that the proposed method satisfies the individual objectives simultaneously. The proposed method is also compared with existing methods to exhibit its effectiveness.

Optimal Siting of Distributed Generation Unit in Power Distribution System considering Voltage Profile and Power Losses

“…Different types of producing power facilities, such as hydropower plants, thermal power plants, and nuclear power plants, are employed to meet the ever-increasing demand for electric power by industrial and home consumers. Conventional power plants are often built in a remote location far away from the consumer load [2,3]. Power is transmitted from the generating station to the distributed load via transmission lines and feeders.…”

Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation

“…The following are the classical or conventional approaches used for identifying the optimal DG rating/or placing in RDS:linear programming [6], mixed non-linear programming (MNLP) [11] (consisting of two phases namely siting planning model for finding the candidate buses and capacity planning model for optimal location and sizing), sequential quadratic programming with trust region [12] method(which approximates the constraints as linear ones for reducing the optimization scale), dynamic programming (DP) [13] for loss reduction, and reliability improvement methods. The global criterion of multi-objective method was proposed in [14] for power loss minimization and DG investment cost reduction with optimal sizing and sitting of DG.…”

Cost–benefit analysis for optimal DG placement in distribution systems by using elephant herding optimization algorithm

“…The optimal placement and sizing of distributed generators for real power losses minimization in distribution systems over the past years were proposed with different algorithms called war optimization (Coelho et al 2018), global criterion method (Bhattacharya et al 2018), hybrid GMSA (Mohamed et al 2018), a multi-objective evolutionary algorithm based on decomposition (MOEA/D) (Biswas et al 2017), K-means clustering method (Penangsang et al 2018), shuffled frog leaping algorithm (SFLA) (Suresh and Belwin Edward 2017), a combination of a fuzzy multi-objective approach and bacterial foraging optimization (BFO) as a meta-heuristic algorithm is used to solve the simultaneous reconfiguration and optimal sizing of DGs and shunt capacitors in a distribution system (Mohammadi et al 2017), a multi-objective genetic algorithm (Tarôco et al 2016), grey wolf optimizer (GWO) for multiple DG allocation (i.e. siting and sizing) in the distribution system (Sultana et al 2016), shuffled bat algorithm (Yammani et al 2016a), hybrid optimization algorithm (Yammani et al 2016b), flower pollination algorithm (Sudabattula and Kowsalya 2016), hybrid big brunch big crunch algorithm (Saha and George Fernandez 2016), sensitivity analysis technique (Gopiya Naik et al 2013), a modified teaching-learning-based optimization (MTLBO) algorithm (Martín García and Gil Mena 2013), harmony search algorithm (HSA) (Rao et al 2013), combined genetic algorithm (GA)/particle swarm optimization (PSO) (Moradi and Abedini 2012), improved honey bee mating optimization (HBMO) algorithm (Niknam et al 2011), multi-objective index-based approach (El-Zonkoly 2011), particle swarm optimization (PSO) (Táutiva et al 2009;Kansal et al 2013), a genetic algorithm (Masoum et al 2004) capacitor placement, multi-objective particle swarm optimization (MOPSO) probability-based solar power DG into the distribution system (Mahesh et al 2017a, b), state-of-the-art models and methods applied to the ODGP problem (Georgilakis and Hatziargyriou 2013;Abdulwahhab Abdulrazzaq et al 2016;Warid et al 2017) and a binary particle swarm optimization (BPSO) algorithm…”