Dec Eunshin Byon

Transcription

1 On robust optimization of wind farm operation and maintenance (O&M) under uncertainty Dec Eunshin Byon

2 Motivation Wind as growing renewable energy source - Fastest growing source around the world - Still, low market share (Just over 1% of world-wide electricity use) Need to increase marketability by reducing the operation and maintenance costs up to 70 m - High cost for maintaining the wind turbines at remote areas - Heavy-duty equipment (ex, crane, vessel) involved for maintenance - Unique features of wind turbines: operated under non-stationary and stochastic (nondeterministic) conditions ground vehicle wind turbine component crew member On robust optimization of wind farm O & M under uncertainty 2

3 Background Wind turbine components (Failure modes) Condition-based maintenance normal alert alarm Low speed shafthigh speed shaft (30 rpm) (1500 rpm) failed - Gearbox (0.3 failures/year) and control are the most frequently failed components - By the stream of sensor output, we can estimate the turbine s state On robust optimization of wind farm O & M under uncertainty 3

4 Objectives Develop a optimum maintenance decision model - Minimization of the operating cost caused by the information uncertainties while keeping high availability - Adaptive maintenance scheduling and resource allocation cost aperiodic false alarm other cities Maintenance crew alarm On robust optimization of wind farm O & M under uncertainty 4

5 Two-stage stochastic programming(sp) optimization model The problem can be modeled in a rolling horizon fashion. Suppose that maintenance decision is made in the beginning of the horizon. Information Period 1(Stage 1) Period 2 (Stage 2) Known Turbines states at period 1 Weather condition at period 1 Turbines states at period 2 Weather condition at period 2 Unknown (uncertainties) Turbines states at next period Future weather condition Decision Maintenance action to be carried out in the current period Resource allocation for next period Adaptive maintenance scheduling Adjusting resource allocation On robust optimization of wind farm O & M under uncertainty 5

6 Model Formulation On robust optimization of wind farm O & M under uncertainty 6

7 Model Formulation (1) Adjusted labor cost for the current horizon + Reserved labor cost for the next horizon + Repairing cost for the current horizon + Expected cost for the next horizon (2) Constraint to adjust the labor hours to fix all of turbines under maintenance consideration. (3) Adjusted labor cost + the repairing cost for failed or alarmed turbines in the second stage. (4) Constraint to adjust the labor hours to fix all of alarmed or failed turbines in the second stage. On robust optimization of wind farm O & M under uncertainty 7

8 Illustrative examples: Epoch #1 Data Scenarios # turbines gearbox control alarmed 0 0 failed 0 0 unit repairing cost gearbox control alarmed failed repairing hour gearbox control alarmed 36 6 failed pre-assigned hour for 1st stage gearbox control 0 0 unit labor cost gearbox control unscheduled scheduled # turbines gearbox control alarmed failed prob alarmed failed prob *2=8 scenarios result 1 st stage variable Labor hour gear box control Adjusted hour for 1 st period 0 0 Pre-assigned hour for 2 nd period nd stage variable Adjusted labor hour for 2 nd period gear box control Scenario #1 0 0 Scenario # Scenario # Scenario # Scenario #5 0 0 Scenario # Scenario # Scenario # Total cost: $45,130 On robust optimization of wind farm O & M under uncertainty 8

9 Illustrative examples: Epoch #2 1 st stage variable Data Scenarios # turbines gearbox control alarmed 3 5 failed 4 7 pre-assigned hour for 1st stage gearbox control # turbines gearbox control alarmed failed prob alarmed failed prob *2=8 scenarios result Labor hour gear box control Adjusted hour for 1 st period Pre-assigned hour for 2 nd period nd stage variable Adjusted labor hour for 2 nd period gear box control Scenario #1 0 0 Scenario # Scenario #3 0 0 Scenario # Scenario # Scenario # Scenario # Scenario # Total cost: $259,698 On robust optimization of wind farm O & M under uncertainty 9

10 Illustrative examples: Epoch #1~#4 Decisions are made in a rolling horizon manner Labor hour Pre-assigned hour for current epoch Adjusted hour for current epoch Pre-assigned hour for next epoch Epoch #1 Gearbox control Epoch #2 Gearbox control Scenario #3 realized Scenario #6 realized Epoch #3 Gearbox control Epoch #4 Gearbox control Other scenario realized On robust optimization of wind farm O & M under uncertainty 10

11 Extended Model Policy: In the beginning of the horizon, we can decide whether to carry out maintenance action or not. If we decide to dispatch crew, all of the alarmed/failed turbines will be fixed during the horizon First-stage decision dependent scenario model Model formulation: Two-stage integer(binary) SP (5) Setup cost and revenue losses are added (6), (8) x 3, y 3 : binary variable indicating whether maintenance is carried or not (7),(9) Availability constraint : minimum # turbines that should be operated (generation requirement) (8) Increased repairing cost are added On robust optimization of wind farm O & M under uncertainty 11

12 Conclusion and further research This work provides a dynamic data-driven framework for wind farm operation and maintenance policy, which will enable a cost-effective generation of wind electricity. Progress in wind turbines will also benefit the power generation, automobile, aerospace, and engine industries. We need to extend the current SP optimization models by incorporating more sophisticated uncertainty factors and also to multi-period decision model The SP model will be Integrated with simulation whereby, the simulation generates the necessary data and validates the optimal policies from the optimization model. On robust optimization of wind farm O & M under uncertainty 12