demand response algorithms for home area networks (han) fabiano pallonetto supervised by dr. donal...
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Demand response algorithms for Home Area Networks (HAN)
Fabiano PallonettoSupervised 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?
• 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 .
DSM - Measures to balance the supply/demand
Peak Clipping Reduction of load during peak demand periodsValley-Filling Improvement of system load factor by off-peak load buildingConservation Reduction of utility loads by efficiency measures
Flexible Load Shape Programs aimed at altering customer consumption by interruptible/curtailable agreements
Load Building Increase of utility loads Load Shifting Reduction of peak demand load, while increasing off-peak load
[Gellings C.W 1985] Concept of demand-side management for electric utilities. Proc. IEEE.
Context and Motivation
Grid supply and demand mismatches Balancing large-scale generation against variable system demand profileIncreased contribution from wind generation
On-going developments include:Communications technology Building energy management systemsRollout 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
Objectives of the PhD
oEvaluate the flexibility of demand response
strategies in all-electric residential building
using building simulation analysis
oDevelop demand response algorithms for
implementation on Home Area Network
systems
oTest and optimise demand response algorithms
on a low energy all-electric test residential
dwelling
Resources available – Test Bed House
System Conventional (Baseline) Dwelling All-Electric Dwelling
Space Heating (17 kW oil) + (5 kW wood) (12 kW GSHP ) + (5 kW wood) DHW Solar Thermal + Immersion (2 kW) Solar Thermal + Immersion (2
kW) DHW Tank 0.2 m3 0.2 m3
Thermal Storage None 2.2m3 Water Tank Heat Recovery None Heat Recovery Ventilation
Micro-generation None PV System (6 kWp) Car Petrol (1998 cc) Nissan Leaf EV (24 kWh)
Test HouseEnergy Model
Test House
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%
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 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/
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.).
The Vision!
2014• System
developed and tested
2015• End of PhD
2025• Every
residential house will have an energy management system EMS based on HAN
2030• Aggregators
use the EMS to balance supply demand of energy
• RES penetration more than 50%