MIP Heuristic

1. MIP Heuristic Greedy Algorithm The greedy algorithm is a fast real time heuristic where the primary component is a sensitivity study 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 • Based on a sensitivity study • Sensitivity reflects marginal improvement by switching lines • Quick and dirty method – The solution obtained from greedy algorithm 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 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 • 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*