OPTIMAL DISTRIBUTED GENERATION PLACEMENT UNDER UNCERTAINTIES BASED ON POINT ESTIMATE METHOD EMBEDDED GENETIC, PSO AND HYBRID ALGORITHM
This project is design based on the paper "Optimal distributed generation placement under uncertainties based on point estimate method embedded the genetic algorithm". 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 optimization problem that is modeled 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 and 69 bus network 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). Design specifications as follows,
(1) Design based on 3 Point Estimate Method(3-PEM) and MCS.
(2) The load uncertainty is designed as per the base paper.
(3) The objective function is designed as per equation number (16) of the reference paper -1.
(4) Backward-Forward Sweep method is applied for load flow solutions.
(5) Algorithm-Genetic Algorithm, Particle Swarm Optimization and Hybrid (GA-PSO).
The IEEE 33 and 69-bus radial distribution system is used for demonstrating the proposed method. The GA, PSO, and HYbrid–PEM and GA, PSO and HYbrid–MCS algorithms were developed in Matlab 2017a and the computer program was executed in a PC having the following specifications: Processor Intel I3 2.3 GHz, 8 GB RAM, running under Windows 10 version.
Reference Paper-1: Optimal distributed generation placement under uncertainties based on point estimate method embedded the 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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SIMULATION VIDEO DEMO
