OPTIMAL POWER FLOW-MCS AND 2PEM WITH TLBO OPTIMIZATION ALGORITHM FOR FACTS DEVICES

    This project is design based on the concept of "Optimal Allocation of FACTS devices to enhance power systems performance with different levels of wind Penetration under normal and contingency cases".  The objective function of this case is the minimization of fuel generation cost takes into account improve the voltage profile and minimization the investment cost of FACTS devices the objective function can be formulated as the following:
Min F=F_1+W_1  IC+P.F
Where,
F_1: Fuel generation cost
W1: The weight of inertia
IC: Investment cost of FACTS devices
P.F: Penalty function

Design:1 IEEE 30 Bus System with Multi FACTS(SVC, TCSC, UPFC) location with TLBO Optimization Algorithm and Mono Carlo simulation(MCS) Power Flow Method
Design:2 IEEE 30 Bus System with Multi FACTS (SVC, TCSC, UPFC) location with TLBO Optimization Algorithm and POPF Power Flow Method using 2 PEM
Design Key Points

•    Variable reactant model of FACTS devices used in the program
•    Wind farm bus connection used in design which includes correlation load
•    The location of FACTS used in the design is variable in TLBO
•    Model of FACTS used for each device in power flow power loss minimize
•    Design based on 2PEM-OPF, MCS-OPF method
•    Random values are generated based Weibull distribution and normal distribution
•    Monte Carlo simulation, values are sampled at random from the input probability distributions.
•    Each set of samples is called an iteration, and the resulting outcome from that sample is recorded.
•    Monte Carlo simulation does this hundred or thousands of times, and the result is a probability distribution of possible outcomes.
•    The 2PEM method based on Hong’s 2-point estimate process
•    Randomly generate initial value (example 10000 values) based on distribution from this initial find 2 best values using the 2PEM method
•    Look the chapter 4.2, 4.31.,4.3.2 for more details about the design from the reference paper-1.
Reference-1: Probabilistic optimal power flow for power systems considering wind uncertainty and load correlation
Author’s Name: Xue Li, Jia Cao, Dajun Du
Source: 2014 Elsevier-Neurocomputing
Year:2014
Reference-2: 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-3: Optimal flexible alternating current transmission system device allocation under system fluctuations due to demand and renewable generation
Author’s Name: S.J. Galloway, I.M. Elders, G.M. Burt, B. Sookananta
Source: IET Generation, Transmission & Distribution
Year:2009
Reference-4: Probabilistic Load Flow Considering Wind Generation Uncertainty
Author’s Name: Morteza Aien,Reza Ramezani, and S. Mohsen Ghavami
Source: ETASR - Engineering, Technology & Applied Science
Year:2011

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SIMULATION VIDEO DEMO                                                                                                                                     


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