Optimal Allocation of FACTS devices to enhance power systems performance with different levels of wind Penetration under normal and contingency cases

 Project Plan

“Optimal Allocation of FACTS devices to enhance power systems performance with different levels of wind Penetration under normal and contingency cases”

 

 Allocation of FACTS with Demand and wind power generations are considered as sources of uncertainties

1- Load Uncertainty Modelling.

Due to stochastic nature of the load demand in electric power systems, it is required to model the load uncertainty in operation and planning of power systems. Generally, load uncertainty can be

Modelled using the normal of Gaussian PDF.

 

 Fig. The load PDF uncertainty

Demand estimation: To generate random demand values, a normal distribution is used to represent the probability density function (p.d.f.) of demand (d). Hence, the mean (µ) and standard deviation (σ) of the set of demand values in a year are required to represent annual demand profile.

The normal distribution is presented as:

 

2- Wind power generation uncertainty modelling:

The variation of wind power generation is an uncertain parameter which can be modeled probabilistically using historical data records of wind speed. In this work, variation of wind speed, V, is modeled using weibull probability density function (PDF) as follow:

 

k: is shape parameter and c is scaling parameter  m/s which is obtained by historical wind data.

 

 

Weibull distribution

The forecasted output power of the wind turbine for different wind speeds can be obtained using the following equation.

 

Problem formulation:

We will study the problem as two level ,uper level to find the allocation of FACTS devices and uper level for probablistic optimal power flow( P-OPF) to studey the uncertenity inputs (wind &load).

Objective Functions: -

The objective function of this case is 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 follow:

Where:

F₁ : Fuel generation cost

W₁   the weight of inertia

IC: investment cost of FACTS devices

P.F: Penalty function

The flowchart for the work plan as the follow:

The proposed method is applied to the modified IEEE 30-bus system

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

Results Evaluation:

1. Location of FACTS
2. Capacity
3. Reactive Loss

  Design Key Points:

• Variable reactant model of FACTS devices used in the program
• Wind farm bus connection used in design which include 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 hundreds or thousands of times, and the result is a probability distribution of possible outcomes.
• 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 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 genetic algorithm
Author’s Name: Vasileios A. Evangelopoulos, Pavlos S. Georgilakis
Source: IET Generation, Transmission & Distribution
Year:2013

Reference-3: Optimal flexible alternative 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

SIMULATION VIDEO DEMO