Monte Carlo Simulation for Power Flow Analysis IEEE 14 Bus Matlab

[@Mahmoud_Hemeida]

Thanks a lot for this video, it gives me an important information about uncertainty and probabilistic.

[@souravmondal1767]

Thanks for this video.

[@architecture4189]

Of course 😀

[@saeed8952]

Hello.. can i use this code to simulate distribution network with rooftops pv systems?

[@aidinfarar]

hi i need to simulate active distribution network on matlab, of course part of network as a micro grid with monte carlo method can you help me ?!

[@nirupamae8710]

Is there code available for 42 bus system in matpower??? Can u please provide it if u have ?

[@pameladelao5474]

Hi Luis, Im wondering if you run the OPF routine only once but considering the mean value of the random variables would you get as a result the average you estimate of the results for each iteration?

[@luisgustavocorderobautista7959]

I will try it, thanks for comment. As soon as I get the results I will answer it.

[@luisgustavocorderobautista7959]

Hello Pamela, I have been thinking what you said, I am using cumulants instead of Monte Carlo, and I realized that the mean value of load data (normal distribution) provides almost same results. Angle and voltages= (inv(J_det).^1)*(mean_values_PQcal-PQcal_initial+inv(J_det).^1*[delta_teta;delta_voltage]). The expression mean_values_PQcal represents average value of calculated power (data) and PQcal_initial the expected value (average point). So, The mean values work well if you consider the mean values of data (normal distribution) and run a deterministic power flow. The purpose to execute Monte Carlo simulations or another probabilistic technique aims to know the *behaviour* of voltages, branch power flow, current. This behaviour is represented by PDF, CDF and confidence intervals, etc. So, these techniques provides statistical information such as, mean, std, kurtosis, skewness, etc. And by doing so, it is possible to approximate the behaviour of variable of interest.

[@pameladelao5474]

@@luisgustavocorderobautista7959 Thanks Luis for taking the time to reply. Your video is a great tutorial on probabilistic OPF. Do you have the source code on a git repository? I would like to give it a go myself.

[@luisgustavocorderobautista7959]

​@@pameladelao5474 thanks for your comment, I re-stated my answer. I will upload on github. Then , I will place the link on the description of the video.

[@pameladelao5474]

@@luisgustavocorderobautista7959 Sounds great. With respect to the your updated initial answer. I would say that because the model is linear whatever distribution you feed as input you will get the same distribution in the output. This means that if you use the mean of the input variables and run the OPF once for that input you will get the mean of the output. That is as if you run it for 5000 or 100000 iterations and then average over all; as you already proved it empirically. This is a fact of the model being linear and deterministic. About the other three moments (std, kurtosis, skewness) I believe they will be same as the input or otherwise just scaled. Would be interesting to prove it also empirically.

[@mamanahmadkhan9365]

That’s such a nice explanation. I am just wondering, can I use the same procedure to run load flow on distribution network like on IEEE 13 network in place of IEEE 14 bus you used which is a transmission network?? Regards..

[@luisgustavocorderobautista7959]

Yes, the procedure can be applied to distribution network.

[@drupadpatidar9241]

I am also working on the topic "probabilistic load flow for distribution system" can you please help me out. Contact- [e-mail]

[@chandandevnath4991]

Dear Luis Gustavo, Thank you so much to upload this valuable video. Can you help me? Actually I want to Find out the Probabilistic Power Flow of multiple wind speed by using Point Estimation method within Monte Carlo Simulation. I have just CSV file of wind speed. Please, suggest me, how can I do step by step...

[@veeraiahchatla5431]

hello can you share your emial i had doubts for running the code

[@canhochothueBinhDuong]

Hi guy, I really want to download the program of your video, can you help me

[@luisgustavocorderobautista7959]

hey brother! its quite simple, first thing, you should model generation and loading buses or other uncertainties probability distribution function. Lets say, there are 5000 possible scenarios. So, a deterministic power flow execute each scenario and then by statisitical inference you calculate mean, standard deviation. The more uncertain variables the more complex Monte Carlo Simulation becomes but it offers PDF and CDF.

[@MOKRABachour]

the code don't work,????

[@HosseinTajmiri]

hello. can i have ur code?

[@ronniemiller6020]

Thank you for this! It helped a lot. But I would like to ask, do you also have an example for a Probabilistic Optimal Power Flow? It can be a Power Flow using MCS, but also includes the optimal dispatching of generators. Thank you!

[@vincentari6483]

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[@jadielkyle6077]

@Vincent Ari Instablaster :)

[@vincentari6483]

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[@vincentari6483]

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[@jadielkyle6077]

@Vincent Ari No problem =)

[@muhammadrashed9810]

i tried to run the code but it's giving errors

[@luisgustavocorderobautista7959]

hello muhammad, let me give a look at the code. I will update github and videos coming weeks. thanks.