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Demand response algorithms for Home Area Networks (HAN)

Demand response algorithms for Home Area Networks (HAN) . Fabiano Pallonetto Supervised by Dr. Donal Finn and Dr. Simeon Oxizidis 17 May 2013. PhD Overview . Focus on residential dwellings Aim to implement a feasible, economic and powerful DSM residential system. What is DSM and DR?.

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Demand response algorithms for Home Area Networks (HAN)

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  1. Demand response algorithms for Home Area Networks (HAN) FabianoPallonetto Supervised by Dr. Donal Finn and Dr. Simeon Oxizidis 17 May 2013

  2. PhD Overview • Focus on residentialdwellings • Aim to implement afeasible, economicand powerful DSMresidential system

  3. What is DSM and DR? • Demand side management (DSM) can be described as the concept of altering the pattern of a customer's electricity use "behind-the-meter” . • Similarly, demand response (DR) is often described as the change in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments .

  4. DSM - Measures to balance the supply/demand [Gellings C.W 1985] Concept of demand-side management for electric utilities. Proc. IEEE.

  5. Context and Motivation Grid supply and demand mismatches Balancing large-scale generation against variable system demand profile Increased contribution from wind generation On-going developments include: Communications technology Building energy management systems Rollout of smart metering Home area networks Time of day / real-time electricity pricing Past assumptions of largely uncontrollable load likely to change Increased renewables penetration  system flexibility challenges

  6. Example

  7. Research Question: Can DR algorithms be effectively used in residential buildings ?

  8. Objectives of the PhD • Evaluate the flexibility of demand response strategies in all-electric residential building using building simulation analysis • Develop demand response algorithms for implementation on Home Area Network systems • Test and optimise demand response algorithms on a low energy all-electric test residential dwelling

  9. Resources available – Test Bed House Test House Energy Model Test House

  10. Methodology

  11. Preliminary results – Economic performances

  12. Preliminary ResultsCO2 emission: days with different wind penetration • CO2 emissions for two days with different wind penetration: • Low wind at 4% • High wind at 20%

  13. Preliminary Results – Load Shifting from SMP peak

  14. Achievements • Paper Accepted for the 13th International Conference of the International Building Performance Simulation Association. 25th - 30th August 2013, FRANCE - http://www.ibpsa.org/ • Present a short paper for the U21 International Network Universities conference on Energy will be held in Dublin from 19th to 21th of Junehttp://www.universitas21.com/ • Paper work in progress for next E-NOVA conference November 2013 on Sustainable buildings - http://www.fh-burgenland.at/forschung/e-nova-2013-english/

  15. Future Work • Develop control algorithms for demand response management of residential energy systems. • Evaluate and optimise demand response algorithms in the test bed house • Assess performance (i.e., energy use, energy cost, thermal comfort, occupant response, system flexibility, etc.).

  16. The Vision!

  17. Thank you

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