Distributed Residential Demand Response
ANU / NICTA / ECI
Paul Scott
Sylvie Thiebaux
Demand Response
Demand Response
- Reduce network peaks
- Balance renewable supply
- Provide network support
Home Automation
Home Automation
Retail Prices
Our Focus
A demand response mechanism that incentivises participation and coordinates activity whilst satisfying network constraints.
Considerations
- Ease of use ← Automated
- Privacy ← Aggregate consumption
- Incentives ← Market prices
- Quality of outcomes ← Near optimal
- Scalability ← Distributed computation
Optimisation Problem
Minimise total cost of serving power, whilst preserving network and participant constraints.
Distributed
- Real-time prices at every bus
- Participants predict needs
- Participants solve subproblems
- Results communicated
Algorithm Overview
- Participant determines best response
- Power profile communicated to bus
- Bus determines best response
- Prices updated
- Repeat 1-4 until prices converge
- Power can then be exchanged
Visualisation of Algorithm
Visualisation of Algorithm
Visualisation of Algorithm
Visualisation of Algorithm
Visualisation of Algorithm
Visualisation of Algorithm
Visualisation of Algorithm
Convergence
Results
Get within 1% of the global optimal. Works with AC power flows. Works with household discrete loads. Only a couple of minutes to converge.