1 / 1

MIP Heuristic

MIP Heuristic. Greedy Algorithm. G-1, G-m, and N-m Applications. Objective . To develop the greedy and MIP heuristic algorithm for contingency management Identify the features of greedy and MIP heuristic algorithm

alban
Download Presentation

MIP Heuristic

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. MIP Heuristic Greedy Algorithm G-1, G-m, and N-m Applications Objective • To develop the greedy and MIP heuristic algorithm for contingency management • Identify the features of greedy and MIP heuristic algorithm • Implement and test the greedy and MIP heuristic algorithm for G-1, G-m and N-m scenarios • Iterative process • Fast solution time – requirement of real-time application • Given a contingency and resulting level of instantaneous load shed, the heuristics attempt to maximize the amount of shed demand that can be recovered by line switching. • Critical focus on contingencies for which ramp of available generators alone does not recover all of the load shed – nontrivial cases. • The heuristics make a line-switching decision at the beginning of 10-minute ramping intervals. • Iterative process • Ranks lines from highest to least likely to provide improvement • sensitivity study • Sensitivity reflects marginal improvement by line switching • Quick and dirty method • The solution does not have guaranteed feasibility • However, multiple solutions are generated in each iteration • Benefits: • Fast solution time – suitable for real-time application • With one iteration sensitivity of all the line can be determined • Adaptable to different types of scenarios MIP Heuristics • The MIP Heuristic switches one line at a time as long as a predefined level of improvement is reached. Its process diagram is similar to that of the greedy algorithm. • Flexible algorithm • User defined execution time • Easily parallelizable • Scalability for practical implementation N-2 MIP Heuristic • Test cases: • IEEE 73 bus (versions 1 and 2) • IEEE 118 bus (versions 1 and 2) • The heuristic was run for all G-1,G-2,T-1, and T-2 contingencies. • Demand levels of 100% and 103% tested. • IEEE 73 bus did not yield nontrivial cases (both versions). • IEEE 118 bus did not yield nontrivial cases for T-1 or T-2 contingencies (both versions). • The table below summarizes the results for IEEE 118 bus G-1 and G-2 contingencies. Cascading Event Results with Greedy Algorithm • IEEE 73 bus Test Case – Base Load 8500 MW • Scenario : Lines 1-5 have a permanent fault; Lines 6-21 have a temporary fault; Lines 22-120 have no faults • Maximum load served is 96.33 percent with transmission switching • Maximum load served is 88.28 percent without transmission switching • 1 Average Load Shed without system re-dispatch or topology control • R.S.D. – Recovered Shed Demand • Opt* is the best line-switching solution obtained in 1 hour. • The solution gap is between MIP Heuristic and Opt*

More Related