Towards Supply-Following Loads: Lessons from Wind Prediction

1. Towards Supply-Following Loads:Lessons from Wind Prediction Mike He, Achintya Madduri, Yanpei Chen,Rean Griffith, Ken Lutz, Seth Sanders, Randy Katz University of California, Berkeley

2. Multi-scale Energy Network Wind Modeling Gen-to-Building Gen-to-Grid Facility-to-Building Building-to-Grid Building-to-Grid Facility-to-Building uGrid-to-Grid Facility-to-Building Grid Storage-to-Building Building MR-to-Building Demand Response Machine Room Load Following Temperature Maintenance Supply Following Plug Loads Web Server Instrumentation Models Grid OS Instrumentation Instrumentation Lighting Web App Logic CompressorScheduling Instrumentation Models Models Models DB/Storage Facilities Routing/Control Control Building OS Load Balancer/Scheduler Supply FollowingLoads Power-AwareCluster Manager Controls 2

3. Key Message • Not-so-good news: Wind-prediction is challenging • High variability = You are wrong a lot even using a “good” model • Good news: It’s ok to be wrong • Prediction horizon matters (short vs. long) • We can deal with the high variability in wind power outputs (control algorithms for load-sculpting)

4. Contributions • Identified additional quality metrics for wind predictors beyond Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) • Quantitative delineation between short-term and longer term prediction horizons • Integrated wind-predictors into controllers used in home appliances and home/building HVAC systems

5. Outline • Background • Big picture solution • Wind prediction (approaches & evaluations) • Putting predictors to work • Conclusions & Future work

6. Background • Big picture goal: integrating renewables into energy portfolios • Utility-side (Independent System Operator) • Customer-side (Office park, campus, home) power power wind, solar, etc. ≡ pgrid P’grid ∫ = E ∫ = E time time

7. Perfect prediction isn’t the silver bullet. Storage needed. Effective supply-following/load-sculpting Storage Supply prediction Implicit storage (thermal mass, deferrable work) Explicit storage (better batteries)

8. Predicting Wind Power: Can you spot the trend? • Dataset: 1 Hz traces from a “large wind power plant in the Midwest region or Texas” (100 – 150 MW) • Goal: Investigate the impact of prediction horizon on predictor quality • Challenges: High variability, no clear/consistent patterns, “dirty” data

9. Wind characteristics – inertia The future looks a lot like the present!

10. Exploiting inertia in Wind-modeling • 1st order predictor • Future = f(previous output) • 2nd order predictor • Future = f(two previous outputs) • 3-dimensional predictor • Future = f(previous output, 1st derivative, 2nd derivative)

11. Prediction horizon and error distribution matter

12. Prediction horizon and error distribution matter • Take away(s): • Simple predictors do well over short prediction horizons (~3hrs or less) • Metrics like RMSE and MAE mask the • variability of errors in longer term predictions

13. Outline • Background • Big picture solution • Wind prediction (approaches & evaluations) • Putting predictors to work • Conclusions & Future work

14. Controller case studies • Temperature regulation/control • Refrigerator • Building/home HVAC • General problem formulation • x(k+1) = x(k) - α(x(k)-Ta(k)) - βu(k) + γd(k) • Goal: min{C(k)*u(k)} over some horizon e.g. 24 hrs where C(k)=f(Powergrid, Powerrenewables) Control effort (to be optimized) Leakage to the ambient environment Previous temp. Perturbations/ disturbances to the system Expected temp.

15. HVAC Controller Simulations • Use home thermal model from jronsim (Java Residential Occupied Neighborhood Simulation) • Goal: Manage HVAC cooling cycles • Identify controller quality metrics • Cost • Minimize renewable power wasted • QoS-inspired metrics “spoilage/discomfort seconds”, “missed-objective penalties” • Compare against oracle using perfect wind predictor

16. Building/home HVAC controller simulations (LW)

17. Building/home HVAC controller simulations (HW)

18. Conclusion • Wind-prediction is challenging • High variability = You are wrong a lot even using a “good” model • It’s ok to be wrong • Prediction horizon matters (short vs. long) • We can deal with the high variability in wind power outputs (control algorithms for load-sculpting) • Combining prediction with implicit storage allows us to compensate for mispredictions

19. Future work • Generalize our analysis to collections of wind/solar farms – you can help by providing data • Build actual supply-following computational/electrical loads – comments and suggestions welcome! • Special thanks to: • The National Renewable Energy Laboratory (NREL) for providing wind data traces • Albert Goto (UC Berkeley)