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 residentialdwellings

• Aim to implement afeasible, economicand powerful DSMresidential 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  

Example

Research Question:

Can DR algorithms be effectively used in residential buildings ?

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 HouseSystem 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

Methodology

Preliminary results – Economic performances

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%

Preliminary Results – Load Shifting from SMP peak

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/

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%

Thank you