Predictive Learning for Energy Storage Dinos Gonatas (978) Ryan Hanna Center for Renewable Resources and Integration Mechanical and Aerospace Engineering UCSD

UCSD Microgrid Overview: 42 MW Peak Load - 66% efficient 27 MW gas cogeneration 10,000 tons steam-chillers 7,800 tons electric chillers Energy Storage: 3MW/ 6 MWh -2MW/ 4MWh BYD system -ZBB -Sanyo -BMW 2 nd life battery

UCSD Battery/ BMW Project

UCSD Solar Forecasting

Why Forecasting?

Anticipating where ball is going to be

Cornerback Malcolm Butler acts on prediction to affect outcome (decisive play clinching Superbowl 49)

Use Cases Building load forecasting + batteries (and/or PV) Grid Storage/ non-transmission alternatives Ramp smoothing for PV generation

Architecture Predictive analytics collects and analyzes sensor data/ grid status Drives optimization engine, controlling battery state of charge, eg. with Modbus interface Inverters Energy Storage/ BMS Predictive Analytics System Optimization/ batery controls Sensor data Sensor data Grid status Grid status Weather / other data Microgrid

Building Load Forecasting 6 hour forecast: using previous time history + weather data Key for deciding when to charge/ discharge battery for demand management prediction actual

Economics of Bad Forecasts

Solar Ramp Problem 14

Implications -Large production fluctuations cause instability in weak electric grids such as Hawaii, Puerto Rico -Penalties imposed when output changes more than 10%/minute -Avoiding penalties using batteries for storing excess production is $$$

How to Smooth Out Steep Ramps? Ramp exceeding limits

Proposed Solution: Smart Ramp Smoothing by Predicting Impending Power Changes + Sky Imaging Camera/ Production Forecasting PV + Inv control PV + Forecast PV + Storage Inverter and Battery Controls

Smart Forecasting Algorithm Green: measured black: USI nowcast red: USI 15 min forecast issued at 1732 Power Generation (arbitrary units) Plant A Plant B Plant C Plant D Large altocumulus cloud field is about to shade the plants

Cloud Prediction Using Sensor Arrays Predictions from SMUD sensor array

Simulated 5% Ramp Control w/o Forecasting: Steep Output Ramps 5% ramp limit

Ramp Smoothing With Batteries Without Forecasting 5% ramp limit

Ramp Smoothing With Forecasting Ramp smoothed out Battery cycle anticipates PV ramp (just as football player anticipates where ball is thrown)

Knowing Future Production Mitigates Ramps Ramp smoothed out

Knowing Future Production Mitigates Ramps Ramp smoothed out

Ramp Violations Eliminated With Right Combination of Forecast and Battery 10% Ramp limit 24kW PV array

For a battery of a given size, discharging it less extends cycle life (= cost reduction) DoD 80%  3000 cycles DoD 60%  5000 cycles DoD 40%  cycles

Conclusions: Implementing forecasting in energy storage can enhance performance 2x Reduce battery size 50%, or with fixed battery size, enhance performance or reduce cycling – Same as lowering battery cost