Optimal distributed generation placement under uncertainties based on PEM, MCS embedded Genetic Algorithm, Particle Swarm Optimization and Hybrid(GA-PSO) Algorithm

         This project is design based on the paper "Optimal distributed generation placement under uncertainties based on point estimate method embedded genetic algorithm".  The scope of this study is the optimal siting and sizing of distributed generation within a power distribution network considering uncertainties. A probabilistic power flow (PPF)-embedded genetic algorithm (GA) particle swarm optimization (PSO) and HYBRID(GA_PSO) based approach is proposed in order to solve the optimisation problem that is modelled mathematically under a chance constrained programming framework. Point estimate method (PEM) and Monte Carlo simulation (MCS) is proposed for the solution of the involved PPF problem. The uncertainties considered include: (i) the future load growth in the power distribution system, (ii) the wind generation, (iii) the output power of photovoltaics, (iv) the fuel costs and (v) the electricity prices. Based on some candidate schemes of different distributed generation types and sizes, placed on specific candidate buses of the network, GA is applied in order to find the optimal plan. The proposed GA, PSO and HYBRID(GA-PSO) with embedded PEM (GA–PEM, PSO-PEM and HYBRID-PEM) is applied on the IEEE 33-bus network by considering several scenarios and is compared with the method of GA, PSO and HYBRID(GA-PSO) with embedded Monte Carlo simulation (GA–MCS, PSO-PEM and HYBRID-PEM).
Reference Paper-1: Optimal distributed generation placement under uncertainties based on point estimate method embedded genetic algorithm
Author’s Name: Vasileios A. Evangelopoulos, Pavlos S. Georgilakis
Source: IET Generation, Transmission & Distribution
Year:2013
Reference Paper-2: Optimal sizing and sitting of distributed generation using Particle Swarm Optimization Guided Genetic Algorithm
Author’s Name: V. Jagan Mohan and T. Arul Dass Albert
Source: Advances in Computational Sciences and Technology
Year:2017

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