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

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) p grid P’ grid time power ∫ = E time ≡ wind, solar, etc.

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 1 st order predictor Future = f(previous output) 2 nd order predictor Future = f(two previous outputs) 3-dimensional predictor Future = f(previous output, 1 st derivative, 2 nd 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)-T a (k)) - βu(k) + γd(k) Goal: min{C(k)*u(k)} over some horizon e.g. 24 hrs where C(k)=f(Power grid, Power renewables ) Expected temp. Previous temp. Leakage to the ambient environment Control effort (to be optimized) Perturbations/ disturbances to the system

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)