Intelligent Agents for Control of Distributed Energy Resources

1. Intelligent Agents for Control of Distributed Energy Resources (and some Retrospective Thoughts on Complex Systems Engineering) Presented by: Geoff James Friday 12 August 2005

2. The BIG Issues for DG in Australia • What happens when Mr DG (times 5000) starts his unit in the morning and becomes a generator for the day? • Network control and generation despatch is currently carried out top down > 30 MW • How can local generation be used to the advantage of the consumer and the network? • Customer choice and network needs must be balanced • Outcomes: lower cost, improved reliability, reduced emissions • How can demand side management options be deployed and controlled to reduce peak demand and prices and minimise GHG emissions? • SCADA? Try autonomous smart distributed agents!

3. Distributed Energy Mgmt and Control Project Goals • A realistic solution to large-scale deployment of DE resources in the distribution network • To impact the Australian network in 3 – 8 years time • Adaptive, intelligent, distributed agents for various applications • Local end-use optimisation • Aggregation for network benefits • A communications infrastructure • A new set of features in the Australian NEM

4. Five Key Messages about DEMC • One ICT infrastructure can have many applications that benefit the energy network • Distributed agent technology gives consumer choice with cost effectiveness and scalability • Key technology: coordination of consumer loads and distributed generation • Key technology: scalable aggregation of distributed energy on a visionary scale • This is the CSS bit • We can begin now: demonstration and industry trials as avenues to early deployments

5. But how did we get here? • Ageless Aerospace Vehicles (2001-2005) • Fantastic collaboration involving sensors, signal processing, distributed intelligence, communications, machine learning, and biology • GREMLab (2002-2005) • Tried to do engineering design for multi-agent CS • Vision for self-assembly from macro to nano scales • Hosted DAMAN and EDCCS projects for CSS • Smart Spaces ESA (2002-2004) • A collaborative vehicle and GREMLab participant that aimed to create a variety of self-organising smart spaces • Attracted attention of Energy Transformed Flagship before being managed to death by oversight committee

6. Simulated tiled surface for an AAV

7. Concept demonstrator for NASA

8. Some absent friends

9. Inchwork – response to a diagnosis

10. Towards complex systems engineering • We’ve done self-organising diagnosis • Simulation and hardware • Response is on the way • Inchworm robot • Critical damage reporting • Prognosis is the big challenge now • Existing project with CIP / CMIT / Boeing • Fingers crossed for the Sentient Structures ESA • Will carry forward our ideas and collaboration

11. A Collaborative Model For Research In Complex Systems Design GREMLAB Leader: Geoff Poulton

12. Herding cats

13. Idealised self-assembling mesoblock + / -/ 0 STATE MACHINE + / -/ 0 + / -/ 0 + / -/ 0 Sense Change

14. Cool simulation of a sea of mesoblocks

15. An emergent behaviour

16. Another response mechanism for AAV

17. Two-layer hierarchy in nature Design Goal: Class of Proteins Folding (emergent) Enzymes Ribosomes mRNA messages Amino acids, tRNA, etc. Real evolution emergent behaviour Biochemical building-blocks

18. Two-layer hierarchy for self-assembly Desired meso- or nano-structures - sensors, actuators etc. Construction Constructor entities – eg. “Enzymes” Self-assembly emergent behaviour Meso- or nano- “agents”

19. Towards complex systems engineering • Two-level hierarchy as framework methodology • Avoids designing out the complexity • Success in simulated environments • DAMAN project for CSS • Good publication record • Yet to make a bridge with reality • EDCCS project for CSS • Fantastic model for collaboration • Participating projects contribute resources • Best times were when we had no budget!

20. SmartLands (CLW, CLI, CTIP, CMIT)

21. The electricity network NEMCO agent decisions Management & Control Pool price & trading Generation despatch Contingency & security NEMCO AGC , GD, CA TNSP SCADA, CA Communications- Networks Web networks, GSM, SMS, Broadband Wireless, Intranets C&I Co LNSP SCADA, DA Distributed intelligence C&I Co Broker Agents Groups of DE, Filter information Enterprise management C&I Co Agent Malls C&I / SME agent decisions Agent SME Agent Buildings Agent Domestic Dom Co Dom Co Dom Co Agent Newcastle miniGrid

22. Towards complex systems engineering • Smart Spaces ESA had an evolving purpose • Sharpened goal and reduced collaboration • Distributed Energy M&C grew out of it • Among other things • Demo focus means less room for complexity • Top-down coordination of loads and generators • But using optimisation tools from GREMLab • Large-scale aggregation is essentially complex • Collaboration with VUA is warming up nicely

23. 1. One Infrastructure, Many Applications

24. Example Application: DSM NEM Aggregated demand response can also be used to defer capital expenditure This quantity is tradable Retailer Aggregated response > 30 MW Rewards SME SME PDA agent Fleck agent SME SME PC agent SME Mote agent PDA agent 1000s of these

25. 2. The Agent Mindset Agents run on local devices and measure, make decisions, and act in the real world • Local control is good for: • Robustness • Scalability • Consumer acceptance • Contrast with SCADA: • Prohibitively expensive to extend to consumer level • Top-down control is not scalable and sometimes not desirable • Opportunity: agents can be a last-mile solution

26. A Multi-Agent System (Domestic Case)

27. Distributed Software Agents • Natural model for distributed energy management • Agents run on local hardware and represent consumer’s interests – keeping data local as much as possible • Agents are intelligent and can model the resources they’re responsible for – learning as they go • Agents can optimise locally andinteract to achieve system benefits • They can provide desirable properties • Cheap, no single point of failure, “safe fail” • Easy to add and remove agents and services • No additional infrastructure (agents chat on the internet) • Provision for intuitive consumer user interface

28. 3. Coordination of Loads and Generators • Coordinating a set of loads and generators to achieve both local and system goals • Expressing as an optimisation problem • Goals vary with application; typically local cost effectiveness and participation in an aggregated system response • Local modelling of capabilities and constraints of loads and generators • Machine learning to • Improve models based on measured performance • Predict generating capacity for wind and photovoltaics • Adapt price sensitivity for agent goal setting

29. 4. Aggregation on a Visionary Scale • Scalable and timely aggregation of distributed capacity across 104, 105, 106, … consumers • System response > 30 MW in order of minutes with communication delays in order of seconds • BREAKTHROUGH WE AIM AT: demonstrating emergent behaviour to a desired outcome • Complex systems techniques: decentralised clustering, dynamic hierarchies, scale-free networks, …

30. Two Levels of Aggregation Market Broker (NEM) Agent Cool room Photo-voltaic generator HVAC Room information Gas micro-turbine Wind power Cranes and forklifts (May use similar or different methods) Customer agent Customer agent Customer agent Aggregation WITHIN Customers Customer agent Customer agent Customer agent Customer agent Aggregation BETWEEN Customers Grid / Market Interaction

31. 5. Begin Now • Writing an agent-based software platform • Collaboration with Infotility (Boulder / San Francisco) • Alpha release under test from April • Creating a uniform agent environment and a reliable platform across a diverse set of devices • Developing multi-agent coordination algorithms • Focus: coordination in 04/05 and scalability in 05/06 • Demonstrating in hardware at Newcastle • Cooperating loads and generators by June • Embarking on a trial with an industry partner • We won’t do front-end deployment ourselves • Looking for commercial partners in 05/06

32. Recap: Five Key Messages for Today • One ICT infrastructure can have many applications that benefit the energy network • Distributed agent technology gives consumer choice with cost effectiveness and scalability • Key technology: coordination of consumer loads and distributed generation • Key technology: scalable aggregation of distributed energy on a visionary scale • We can begin now: demonstration and industry trials as avenues to early deployments

33. Energy Transformed Flagship The Energy Transformed Flagship is aimed halving greenhouse gas emissions and doubling the efficiency of the nation’s new energy generation, supply and end use, and to position Australia for a future hydrogen economy. Theme 4: Distributed Energy – this technology Theme directly targets a step change in energy efficiency and reduction in GHG emissions by accelerating the uptake of distributed energy systems that provide local power, heat and cooling to industrial and commercial sites. The Centre for Distributed Energy and Power CenDEP is an alliance of organisations, joining with CSIRO to help put distributed energy on the map in Australia.

34. Contacts For more information, seewww.csiro.auor contact: Thank you