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A stochastic power network calculus for integrating renewable energy sources into the power grid

A stochastic power network calculus for integrating renewable energy sources into the power grid. Presenter: qinghua shen.

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A stochastic power network calculus for integrating renewable energy sources into the power grid

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  1. A stochastic power network calculus for integrating renewable energy sources into the power grid Presenter: qinghuashen Wang, Kai, et al. "A stochastic power network calculus for integrating renewable energy sources into the power grid." Selected Areas in Communications, IEEE Journal on 30.6 (2012): 1037-1048.

  2. content • Intro • Formulation • Power system modelling • Performance metrics • Case study • Conclusions

  3. 1. Intro • Motivation use environmentally friendly sources adopt storage to match uncertain supply and demand (island) improve reliability of the system

  4. 1. Intro • Motivation • Why network calculus The ability of the stochastic network calculus to model broad classes of queueing scenarios and capture statistical multiplexing gain • Why extend Decoupled arrival and service process Specific performance metrics: Fraction of Time that energy is not-served (FTNS) waste of power supply (WPS) (drop rate)

  5. 2. Formulation • Problem description Island: only renewable sources for supply Storage has limited capacity C Np: PV panels, Nw wind turbines Objectives: reliability provisioning in terms of FTNS

  6. 2. Formulation • Network Calculus • was designed to facilitate stochastic performance analysis (tail performance analysis with multiplexing) • Envelop process to characterize arrival process • Queue characterization:

  7. 3. Power system modelling • Energy storage: discrete • Charged: • Discharged: • Differences from queue • Departure is not a function of the arrival and current queue

  8. 3. Power system modelling • Energy demand and supply • Upper curve: • Lower curve: • Similar for supply • Upper curve: similar to queue • Lower curve: needed for energy storage • Tightness: tradeoff between shapes of curves and bounding function

  9. 4. Performance metrics • Transform to non-recursive • recursive • Non-recursive: • Compare to previous work • Finite buffer length

  10. 4. Performance metrics • How to present metrics recursive form Loss of power supply: Fraction Time of no service: Waste of power supply (WPS)

  11. 4. Performance metrics • How to present metrics non recursive form Loss of power supply: Fraction Time of no service: Waste of power supply (WPS):

  12. 4. Performance metrics • Bound expression Loss of power supply: Intuition: upper of demand - lower of supply Waste of power supply (WPS): Intuition: upper of supply – lower of demand

  13. 4. Performance metrics • More benefits to come! Multiplexing: For N source with Similar results for upper bound • Something is missing! (hao)

  14. 5. A case study • Santa Catalina Island

  15. 5. A case study • Model fitting • Linear function with rate equal to long term mean rate • exponential functions for the bounding functions

  16. 5. A case study • Model fitting • Linear function with rate equal to long term mean rate • exponential functions for the bounding functions

  17. 5. A case study • Model fitting • Linear function with rate equal to long term mean rate • exponential functions for the bounding functions

  18. 5. A case study • Numerical results • Impacts of PV panels, wind and season

  19. 6. Conclusion • Issues: demand and supply for an island • Only renewable energy • Storage aided • Reliability • Good point • New type of “queue” • Finite buffer analysis • Insufficient • Does this really matter (finite queue, decoupled?) • My view • Not average tail but instantaneous tail(New fitting) • How will renewable energy impacts the market?

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