Exploiting the Inverse Capacity-Rate Relationship in a Stochastic Setting

1. Exploiting the Inverse Capacity-Rate Relationship in a Stochastic Setting Control Algorithm Development for Hybrid Energy Storage in Renewable Energy Applications Advisors: Prof. Craig Arnold, Prof. Warrant Powell Sami Yabroudi

2. In a Nutshell… • To make alternative energy viable in a closed system, need to make storage functional and efficient. • To store with varying supply and demand (i.e. in the real world), use multiple complimentary storage devices. • To decide where to allocate energy to and where to use it from at a given time, use Approximate Dynamic Programming.

3. The Big Idea With Storage Devices: Every storage device has its own power and capacity applications. Pick the one that matches your needs. INVERSE CAPACITY-RATE RELATIONSHIP!!!!!!!

4. But what if……energy supply and demand are stochastic? • What if you wanted to power a house using a standalone wind turbine system, and • What if the wind changes speed and direction, sometimes blowing a little, sometimes blowing a little more, sometimes blowing A LOT, and sometimes not blowing at all, and • What if the family inside the house has an energy demand that changes significantly over the course of the day, unpredictably. Translation to the vernacular: What if everything in the world behaves normally?

5. (Summarize) Battery Rate and Specific Capacity • Charge, discharge rate measured in power per unit mass or volume (or money), or C rate, which is a percentage of total capacity • Ex: A battery charging at .1 C would take 10 hours to charge • The more charge/discharge current you draw (or try to draw), the more ohmic and kinetic overpotential you have, as well as ohmic loss • Charge Voltage: • Discharge Voltage: • The higher the current on a battery, the more permanent (and bad) chemical changes you make to the battery. • “Gassing” • Ragone Plot: Most Batteries prefer to operate below 1-2 C, and reach their absolute limit below 10 C.

6. (Skip) Electrochemical (i.e. “Ultra”) Capacitors • Energy stored between porous electrode and electrolyte, and across separator • ~3-10 Wh/kg • ~200-2000 W/kg • @ 95% discharge efficiency • Same rate effects as batteries, but for much higher rates

7. Other STSES Devices • Compressed Air Energy Storage (CAES) • Flywheels (Skip) • Inverse Capacity-Rate Relationship both betweenclasses of devices and within each class • Superconducting Electromagnetic Energy Storage (SMES) 1 = cooler 2 = compressor 3 = air 4 = clutch 5 = generator/motor 6 = power supply 7 = turbine 8 = combustor 9 = fuel 10 = valve 11 = air storage cavity

8. So now we have the problem:The Inverse Capacity-Rate Relationship in a Stochastic Setting But Wait! More device behaviors than just capacity, rate. • Self Discharge • (Very, very) generally, higher rate devices have more self-discharge • Frequency, rise-time, fall-time effects • Irrelevant when using a 10 second time interval

9. Hope that I’m not out of time… • Hopefully, I have conveyed the motivation for the project • No time to get into the algorithm methodology development • Can outline the requirements though……….

10. Requirements of a Methodology for Storage Control Algorithms • Must not depend on specific transition functions • Must be dynamic with regards to the number of devices • Must satisfy objective • Long run objective: maximize storage and usage efficiency to minimize amount of storage needed • MCC objective: maximize the amount of energy stored before time T

11. The End