## Thermal Energy Storage

## Projects.DynamicEnergyStorage History

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[2] Powell, K. M., Hedengren, J. D., and Edgar, T. F., Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference (ACC), Washington, DC, 2013. [[Attach:powell_acc2013.pdf|Preprint]]

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[3] K.M. Powell, Dynamic Optimization of Energy Systems with Thermal Energy

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# Powell, K. M., Hedengren, J. D., and Edgar, T. F., Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference (ACC), Washington, DC, 2013. [[Attach:powell_acc2013.pdf|Preprint]]

[3] K.M. Powell, Dynamic Optimization of Energy Systems with Thermal Energy Storage, Dissertation, The University of Texas at Austin, June 2013. [[Attach:kody_powell_dissertation_2013.pdf | Download PDF]]

[3] K.M. Powell, Dynamic Optimization of Energy Systems with Thermal Energy Storage, Dissertation, The University of Texas at Austin, June 2013. [[Attach:kody_powell_dissertation_2013.pdf | Download PDF]]

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[1] K.M. Powell and T.F. Edgar, Modeling and Control of a Solar Thermal Plant with Thermal Energy Storage, Chemical Engineering Science, ~~to be published~~.

[2] K.M. Powell, J.D. Hedengren, and T.F. Edgar, Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference,~~Montreal~~, ~~Canada~~, June ~~2012, submitted~~.

[2] K.M. Powell, J.D. Hedengren, and T.F. Edgar, Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference,

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[1] K.M. Powell and T.F. Edgar, Modeling and Control of a Solar Thermal Plant with Thermal Energy Storage, Chemical Engineering Science, 2012.

[2] K.M. Powell, J.D. Hedengren, and T.F. Edgar, Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference, Washington, DC, June 2013.

[3] K.M. Powell, Dynamic Optimization of Energy Systems with Thermal Energy Storage, Dissertation, Austin, TX, June 2013. [[Attach:kody_powell_dissertation_2013.pdf | PDF]]

[2] K.M. Powell, J.D. Hedengren, and T.F. Edgar, Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference, Washington, DC, June 2013.

[3] K.M. Powell, Dynamic Optimization of Energy Systems with Thermal Energy Storage, Dissertation, Austin, TX, June 2013. [[Attach:kody_powell_dissertation_2013.pdf | PDF]]

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<iframe width="~~480~~" height="~~360~~" src="//www.youtube.com/embed/56UfuGSo83g?rel=0" frameborder="0" allowfullscreen></iframe>

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<iframe width="560" height="315" src="//www.youtube.com/embed/56UfuGSo83g?rel=0" frameborder="0" allowfullscreen></iframe>

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(:html:)

<iframe width="480" height="360" src="//www.youtube.com/embed/56UfuGSo83g?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

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(:description ~~Optimize Thermal Energy Storage with APMonitor:~~)

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(:description Process Research and Intelligent Systems Modeling (PRISM) Group at Brigham Young University:)

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A solar thermal power plant is investigated as a case study for thermal energy storage. This process uses solar energy as the heat source, but the results are applicable to any system with an intermittent energy supply. Results from the study were generated by creating a realistic solar plant model~~ with %blue%A%red%P%black%Monitor driven with a MATLAB script~~. The results show that thermal energy storage allows more constant energy delivery.

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A solar thermal power plant is investigated as a case study for thermal energy storage. This process uses solar energy as the heat source, but the results are applicable to any system with an intermittent energy supply. Results from the study were generated by creating a realistic solar plant model. The results show that thermal energy storage allows more constant energy delivery.

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Information on latest research

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(:title Thermal Energy Storage:)

(:keywords thermal energy storage, solar collector, optimization, dynamic, nonlinear model:)

(:description Optimize Thermal Energy Storage with APMonitor:)

!! Thermal Energy Storage

Concerns over global greenhouse gas emissions and limited fossil fuel supplies have led researchers and industry to pursue measures to increase energy efficiency and utilize renewable power sources. One of the major drawbacks to solar or wind energy is the intermittent nature of the supply. Energy storage allows an intermittent source of energy (such as wind or solar) to be harvested and re-distributed in accordance with some demand schedule. Energy storage has also proven to be effective in enhancing traditional (fossil fuel) power sources by allowing these systems to shift times of production and consumption, giving them an increased ability to use their power generation capacity more effectively. As a result, a reduction in overall base-load power generation capacity can be achieved [1].

Attach:solar_availability.jpg

!! Case Study: Solar Collector

A solar thermal power plant is investigated as a case study for thermal energy storage. This process uses solar energy as the heat source, but the results are applicable to any system with an intermittent energy supply. Results from the study were generated by creating a realistic solar plant model with %blue%A%red%P%black%Monitor driven with a MATLAB script. The results show that thermal energy storage allows more constant energy delivery.

Attach:tes_system.png

By solving a dynamic optimization problem over one day, the system harvests a higher percentage of solar energy. Most of the most benefit occurs on mostly cloudy days as shown below.

Attach:tes_results.png

On mostly cloudy days, the solar energy captured increases from 4.75 MWh to 7.80 MWh. That is a 64% increase in the total energy captured by using weather forecasts and smarter operations [2].

----

!!! References

[1] K.M. Powell and T.F. Edgar, Modeling and Control of a Solar Thermal Plant with Thermal Energy Storage, Chemical Engineering Science, to be published.

[2] K.M. Powell, J.D. Hedengren, and T.F. Edgar, Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference, Montreal, Canada, June 2012, submitted.

[[Attach:tes_twccc.pdf | Presentation (PDF)]]

(:keywords thermal energy storage, solar collector, optimization, dynamic, nonlinear model:)

(:description Optimize Thermal Energy Storage with APMonitor:)

!! Thermal Energy Storage

Concerns over global greenhouse gas emissions and limited fossil fuel supplies have led researchers and industry to pursue measures to increase energy efficiency and utilize renewable power sources. One of the major drawbacks to solar or wind energy is the intermittent nature of the supply. Energy storage allows an intermittent source of energy (such as wind or solar) to be harvested and re-distributed in accordance with some demand schedule. Energy storage has also proven to be effective in enhancing traditional (fossil fuel) power sources by allowing these systems to shift times of production and consumption, giving them an increased ability to use their power generation capacity more effectively. As a result, a reduction in overall base-load power generation capacity can be achieved [1].

Attach:solar_availability.jpg

!! Case Study: Solar Collector

A solar thermal power plant is investigated as a case study for thermal energy storage. This process uses solar energy as the heat source, but the results are applicable to any system with an intermittent energy supply. Results from the study were generated by creating a realistic solar plant model with %blue%A%red%P%black%Monitor driven with a MATLAB script. The results show that thermal energy storage allows more constant energy delivery.

Attach:tes_system.png

By solving a dynamic optimization problem over one day, the system harvests a higher percentage of solar energy. Most of the most benefit occurs on mostly cloudy days as shown below.

Attach:tes_results.png

On mostly cloudy days, the solar energy captured increases from 4.75 MWh to 7.80 MWh. That is a 64% increase in the total energy captured by using weather forecasts and smarter operations [2].

----

!!! References

[1] K.M. Powell and T.F. Edgar, Modeling and Control of a Solar Thermal Plant with Thermal Energy Storage, Chemical Engineering Science, to be published.

[2] K.M. Powell, J.D. Hedengren, and T.F. Edgar, Dynamic Optimization of a Solar Thermal Energy Storage System over a 24 Hour Period using Weather Forecasts, American Control Conference, Montreal, Canada, June 2012, submitted.

[[Attach:tes_twccc.pdf | Presentation (PDF)]]

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!! Dynamic Energy Storage

Information on latest research

Information on latest research