Student project

Development of the dynamic power system security and reliability assessment tools

The overall vulnerability of the electrical grid appears to be increasing. This is because of different factors including the liberalization of the electrical market, the growth of demand and the appearance of new energy sources with a random production (solar and wind for instance). Today, engineers are undergoing research to try and enforce the reliability of the grid and propose various topological and technological solutions... One problem they have is that it’s difficult to see if a configuration is better than another. The goal of this project is to develop a tool that can evaluate the security and reliability of various power systems and its topologies. Using this tool, should make it possible to evaluate if a given power system topology of is better than another. Actually, tools that are able to do this task already exist [1]. However, they work statically by doing load flow iterations. The improvement of this program would be to do the simulation dynamically. The criterion used to define if a configuration is secure and stable will be the loss of load probability(LLP). In order to evaluate this LLP, the first thing to do is to define all possible scenarios. Then, there are two ways to calculate: 1. The simulations of all scenarios are done. Then, the average loss of load is calculated while taking into account the scenario probability. 2. Only a part of the possible scenarios are chosen according to Monte Carlo Simulation (MCS) [2] [1]. The simulation is applied to the selected scenario. Then, the average loss of load can be calculated. The first method is the most reliable to evaluate the loss of load, but it’s not applicable to large grids because of the required time for simulation. Thus, in this work, the second method shall be applied . This report will focus on some major points of the tools. First it will show how the program uses the MCS method and how scenarios are chosen. It shall be assumed that the number of events in a scenario follow a Poisson distribution. If few-event scenarios appear more often, they shall have smaller effects. On the contrary, several-event scenarios are rarer, but have a more important impact. Then, this document will focus on how the simulation is done, particularly on Eurostag and the API used. The report will show the rules applied to disconnect lines, machines and loads during simulation, if the limits of the system are not respected. After this, the report will show how to average the loss of load and how to calculate the probability density function (PDF). In order to classify the result, the PDF of the simulation can be approximated by a sum of Gaussian PDF. This is called the Gaussian mixture method (GMM) [2] [1]. ] In the end, simulation results will be exposed. Because of the numerous hypothesis’ done, the tools will not be able to give the exact loss of load probability (LLP). The main function of the program will be to compare two different topologies and decide which one is best.

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