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

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Presentation on theme: "Demand response algorithms for Home Area Networks (HAN) Fabiano Pallonetto Supervised by Dr. Donal Finn and Dr. Simeon Oxizidis 17 May 2013."— Presentation transcript:

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 - Present a short paper for the U21 International Network Universities conference on Energy will be held in Dublin from 19th to 21th of June Paper work in progress for next E-NOVA conference November 2013 on Sustainable buildings -

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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