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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. In a Nutshell…. - PowerPoint PPT Presentation
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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 PowellSami Yabroudi
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.
The Big IdeaWith StorageDevices:Every storage device has its own power and capacity applications. Pick the one that matches your needs.
INVERSE CAPACITY-RATE RELATIONSHIP!!!!!!!
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?
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.
(Summarize)
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
(Skip)
Other STSES Devices
• Compressed Air Energy Storage (CAES)
1 = cooler2 = compressor3 = air4 = clutch5 = generator/motor6 = power supply7 = turbine8 = combustor9 = fuel10 = valve11 = air storage cavity
• Flywheels
• Superconducting Electromagnetic Energy Storage (SMES)
Inverse Capacity-Rate Relationship both between classes of devices and within each class
(Skip)
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
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……….
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
The End
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