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

2. PhD Overview Focus on residential dwellings Aim to implement a feasible, economic and powerful DSM residential 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 Peak ClippingReduction of load during peak demand periods Valley-FillingImprovement of system load factor by off-peak load building ConservationReduction of utility loads by efficiency measures Flexible Load ShapePrograms aimed at altering customer consumption by interruptible/curtailable agreements Load BuildingIncrease of utility loads Load ShiftingReduction of peak demand load, while increasing off-peak load [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 o Evaluate the flexibility of demand response strategies in all-electric residential building using building simulation analysis o Develop demand response algorithms for implementation on Home Area Network systems o Test and optimise demand response algorithms on a low energy all-electric test residential dwelling

9. Resources available – Test Bed House SystemConventional (Baseline) DwellingAll-Electric Dwelling Space Heating(17 kW oil) + (5 kW wood)(12 kW GSHP ) + (5 kW wood) DHWSolar Thermal + Immersion (2 kW) DHW Tank0.2 m 3 Thermal StorageNone2.2m 3 Water Tank Heat RecoveryNoneHeat Recovery Ventilation Micro-generationNonePV System (6 kWp) CarPetrol (1998 cc)Nissan Leaf EV (24 kWh) Test House Energy Model Test House

10. Methodology

11. Preliminary results – Economic performances

12. Preliminary Results CO 2 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 13 th International Conference of the International Building Performance Simulation Association. 25th - 30th August 2013, FRANCE - http://www.ibpsa.org/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 June http://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