Applying Robust Optimization to MISO Look-ahead Unit Commitment

Transcription

1 1 Applying Robust Optimization to MISO Look-ahead Unit Commitment Yonghong Chen, MISO Qianfan Wang, Xing Wang, Alstom Grid Yongpei Guan, University of Florida IEEE General Meeting July, 2014

2 2 Outline Overview Applying Robust Optimization to MISO LAC Preliminary Study Results Challenges and next Steps

3 MISO Facts 3 South region integration in December states 65,787 Miles of Transmission Network Model 43,962 network buses 1,390 generating units (market) 394 Market Participants who serve 42 million people $20.3 billion annual gross market charges (2013) 2,413 pricing nodes Generation Capacity: 175,436 MW (market) MISO wind capacity: 500MW in 2005 to 12,000MW in 2013

4 Managing uncertainties under MISO operations Operators need to manage uncertainties everyday Carrying operating reserves; multiple commitment stages; etc. Source of uncertainties Input data Study interval length not granular enough to reflect the rate of changes Unexpected events and behaviors Simplified mathematical model Result: deviation of the market clearing models from actual system conditions Challenging with more renewable integration 4

5 Level of Uncertainty Varies along the Processes Expected difference between the actual system condition and the market clearing models RAC: Reliability Assessment Commitment LAC: Look-ahead Unit Commitment RT-SCED: real time Security Constrained Economic Dispatch 5

6 Ways to Manage Uncertainties at MISO The same operating reserve requirements are applied in all market clearing processes Not sufficient to accommodate larger uncertainties in RAC and LAC May not be able to account for the uncertainty caused by study interval differences Additional capacity headroom and ramp requirements 7-DayAhead-RAC and DayAhead-RAC Certain percentage of capacity headroom based on the analysis of uncertainties from historical input data Committing slow start resources so that future actions (fast start resource commitment and economic dispatch) can satisfy additional changes beyond the deterministic input data LAC Additional capacity headroom requirement (~350MW) to prepare for the capacity and ramp uncertainty 6

7 Problem Setup for MISO LAC LAC is primarily used to commit fast start resources in real time Run every 15 minutes; Study window: [t+15min, t+3hr] Interval length:15-min to 30-min (~10 intervals) Relatively small problem size and narrow range of uncertainty Three scenarios for each run Headroom requirement of 350MW Prototype Robust Optimization LAC A range of variations on load forecast from each of the 28 Local Balancing Authorities Can be extended to other input data such as scheduled interchanges and wind forecast Using robust optimization to model the uncertainties currently addressed by headroom requirement in LAC 7

8 Robust Optimization LAC Setup Configure uncertainty range to a range of uncertainties for each of the three scenarios Operators continue picking the proper scenario based on latest information 8

9 Robust Optimization UC 9

10 Unified Stochastic/Robust Optimization UC 10

11 Preliminary Study Results Solution method: two-stage scenario/constraint generation approach[5,6,7] with bilinear heuristic algorithm to solve the sub problem Robust optimization [2][3] Unified stochastic/robust approach [4] Only consider one nominal scenario dispatch cost and include it in the master problem; no need to generate the stochastic cut Generating feasibility cuts from the robust optimization sub problem Select 96 LAC cases from one operation day in Jan With ~130 commitment actions taken on ~50 resources per LAC suggestion in production Including starting new CT or extending existing commitment A mild day with some relatively small violations 11

12 Preliminary Study Results (Cont.) Three approaches of commitment are studied Deterministic approach with headroom requirement (Deterministic) Robust optimization approach (Robust) Unified Stochastic/Robust optimization approach (Unified) For Robust and Unified approaches Most cases converge within two cuts/scenarios Robust (54 out of 96) Unified (71 out of 96) Master problem solution time increases tremendously with the third cut Robust: ~2h Unified: ~0.5h *Results from 64-bit desktop with Intel Core 3.4GHz and 16GB RAM. LAC is built on AIMMS with CPLEX

13 13 Observations and Performance Tuning Robust optimization is much slow than unified approach One example case: Robust: (root node relaxation 250s); Master3 timed out Unified: Root node relaxation for robust optimization takes long time CPLEX log indicates degeneracy with a lot of "perturbations" in the dual-simplex LP solver and the objective is stuck for similar values for a long time Change LP solver option from Automatic to Barrier during the root node relaxation for the robust model Robust: Master2@135s and Master3@169s

14 14 Observations and Performance Tuning (Cont.) Improve performance by addressing feasibility issues first Conflict input data can introduce large violation cost in the objective for the base case and cause performance issues One example: Maximum daily startup constraints violation due to input data Penalty cost of $10 9 in the objective. It took 2939 sec for the robust model Master 2 to solve at an objective value of $1,004,248,719 Freezing violation variables after Master 1 Master 2 can solve at 105 sec with Automatic setting and 70 sec with Barrier setting during the root node relaxation Solved objective of the clean model is $4,248,594 under both settings.

15 Observations from Preliminary Study Set maximum number of cuts/scenarios to be 2 Unified approach converges faster than Robust approach Average Optimization Solution Time (sec.) Total Master1 Sub1 Master2 Sub2 Deterministic Robust Unified Both Robust and Unified approaches can help reduce the violations Sum of SCED 1st intervals Violation in MWh Spin Violations Xmission Violations Deterministic Robust Unified * After each commitment run, fix all integer variables to run a SCED for comparison purpose 15

16 Observations from Preliminary Study Initial results in 2013 showed that both robust and unified approached caused slight increase in total cost With better convergence, robust approach actually result in reduced total cost Feasibility cuts from unified approach corresponds to zero offers, which may not be effective to drive sufficient commitment for future uncertainties (Robust-Deterministic)/ Deterministic% (Unified-Deterministic)/ Deterministic% Sum of SCED 1st Interval Costs Total Production Cost (Commitment + Dispatch) Commitment Dispatch -0.09% 1.13% -0.33% 0.05% 1.26% -0.19% Need more studies on different types of days to draw further conclusion 16

17 Next Steps With footprint expansion, the problem size increased with more performance challenges Much more challenging for RAC study Long way to go for production implementation of robust optimization based approach Improving the headroom based approach Statistics analysis of the historic uncertainty data Using headroom based approach to manage uncertain scenarios Incorporating contingency scenarios and reserve deployment actions on the co-optimization Ensure reserve deliverability under deployment scenarios 17

18 References 1. Y. Chen, Q. Wang, X. Wang, and Y. Guan, Applying Robust Optimization to MISO Look-ahead Commitment, IEEE PES General Meeting, D. Bertsimas, E. Litvinov, X. Sun, J. Zhao, and T. Zheng, Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem, IEEE Transactions on Power Systems, Volume:28, Issue:1, Feb R. Jiang, J. Wang, and Y. Guan, Robust Unit Commitment with Wind Power and Pumped Storage Hydro, IEEE Transactions on Power Systems, Volume:27, Issue:2, May C. Zhao and Y. Guan, Unified Stochastic and Robust Unit Commitment, IEEE Transactions on Power Systems, Volume:28, Issue:3, Aug F. Furini, M. Laguna, and M. Samorani, Minimax Robust Unit Commitment Problem with Demand and Market Price Uncertainty 2012, Tech. rep., available in Optimization-Online. 6. L. Zhao and B. Zeng, Robust Unit Commitment Problem with Demand Response and Wind energy, Proceedings of IEEE PES, July Q. Wang, J. Watson, and Y. Guan, Two-Stage Robust Optimization for N-k Contingency-Constrained Unit Commitment, IEEE Transactions on Power Systems, Volume:28, Issue:3, Aug