Short Course on Future Trends for Power Systems, The University of Sydney, 12 th October, 2009 Adaptive Power Grids: Responding to Generation Diversity David J Hill Research School of Information Sciences and Engineering The Australian National University 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 1
Outline Future grids Challenges New control ideas Example: Voltage control Conclusions 12 October 2009 David J Hill The Australian National University Adaptive Grids 3
Australian Transmission Network 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 4
Diverse Generation in Australia New Wind Solar Bioenergy Geothermal Nuclear Old Coal Hydro 12 October 2009 David J Hill The Australian National University Adaptive Grids 5
New Power Grids Diverse loads Diverse generation New Diverse storage New All Distributed Multi-level Multi-scale Multi-type Volatile
Ref: J.Fan and S.Borlase, IEEE Power & Energy Magazine, Special Issue on the Next- Generation Grid, Vol.7, No.2, 2009 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 7
Big Changes Old model variable load, adjust generation New models variable load and generation End-to-end control, i.e. generation, demand management, storage
Changes The existing grids typically do not have the right structure and capacities for large-scale renewables, e.g. will need wind and solar hubs quickly Generation is much more volatile, i.e. now on both sides of the generation = load equation Major new need is demand management New loads on horizon, e.g. plug-in (hybrid) electric vehicles (PHEV) MUCH MORE UNCERTAINTY FOR THE GRID Need ADAPTIVE end-to-end control 12 October 2009 David J Hill The Australian National University Adaptive Grids 9
Uncertainty for Wind Generation Dependence on nature gives unpredictability Companies do not want to disclose their data, controls (IP for market) Manufacturers can disappear but their turbines keep operating Manufacturer models are very detailed, but need simpler models for grid studies
Challenges of Complexity Planning vs control Decision and control (performance, security) Massive amounts of data Optimizing (planning, control) on such a scale Validation
What is Smart Grid? Concept emerged in Europe; named in USA Energy Act 2007, Obama stimulus package Now a buzzword which captures other ideas: Intelligent Grid, EPRI; igrid, Australia etc But Aus budget just gave A$100 million, US$4.6 billion in USA, so much anticipation 12 October 2009 David J Hill The Australian National University Adaptive Grids 12
Smart Grid Targets Meet environmental targets Accommodate greater emphasis on demand management Support new loads, e.g. PHEVs Support distributed generation and storage Maintain a level of availability, performance and security 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 13
More Monitoring, Computing and Control Ref: A.Ipakchi and F.Albuyeh, IEEE Power & Energy Magazine, Special Issue on the Next-Generation Grid, Vol.7, No.2, 2009 12 October 2009 David J Hill The Australian National University Adaptive Grids 14
Smart Grid as Control Engineering Large network of sensors Massive amounts of data, i.e. measurements, availability etc Distributed control operating at many levels, c.f. Internet
Thinking like the Internet Things just have to happen in time, e.g. the TV immediate, the toaster within 1 minute, but allow some scale A vision of a plug and play capability for the whole grid All controlled in (seven) layers Congestion handled by protocols, AQM, delays Major problems by re-routing
What looks useful Computer science Machine learning Planning and diagnosis, etc Automatic control Communications Mathematical algorithms All working together have the tools to make major advances.
Outline Future grids Challenges New control ideas Example: Voltage control Conclusions 12 October 2009 David J Hill The Australian National University Adaptive Grids 18
Big Questions Diverse generation makes planning, analysis and control all harder What level of renewables (or any given energy mix) can a given network support? How do we plan and control the power grid given all challenges? 12 October 2009 David J Hill The Australian National University Adaptive Grids 19
Many Challenges Protocols for access cf. Internet plug and play Affect on system dynamics, collapse Blackouts due to weak points Wide-area control architectures How to coordinate 1000 s of controls at multiple levels Lot moreuncertainty Sensing technology and architectures 12 October 2009 David J Hill The Australian National University Adaptive Grids 20
Voltage Collapse 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 21
Blackout 2003 USA-Canada 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 22
South Australia Wind Power Case* Wind Generation Scenarios 1200MW wind scenario
South Australia Wind Power Case* --- continued Long term voltage stability limits * NEMMCO Report: Assessment of Potential Security Risks due to High Levels of Wind Generation in South Australia
Locations of Wind Turbines G1 G3 W G2 Critical Clearing Time 0.20 0.25 0.30 0.35 0.40 DFIG at G3 Constant Speed at G3 DFIG at G1 Constant Speed at G1 G4 W Ref: Bennett, Hill and Zhang, in prep 0 20 40 60 80 Number of Turbines
Comments and Conjectures All stability types affected Locations of generation types important Structure of network important More flexible (adaptive) control must be used
ARC LP Project 2009-2012 1. Investigate proper, possibly generic, models for diverse generators and their local controls in different power networks and voltage levels; 2. Determine what kinds of stability or security issues could arise due to characteristics of renewable energy resources; 3. Check the limitations of available control mechanisms to guarantee power system quality of supply and stabilities; 4. Otherwise, design proper coordinated control schemes to maximize the stability margin of the power system; 5. Given the improved technology, implemented at some generic level, develop methods to assess what level of renewable generation could be supported at different sites; 6. Investigate whether available control in power system with diverse generation can guarantee levels of security and quality of supply for increasing levels of mandatory targets for certain technologies. 12 October 2009 David J Hill The Australian National University Adaptive Grids 27
Outline Future grids Challenges New control ideas Example: Voltage control Conclusions 12 October 2009 David J Hill The Australian National University Adaptive Grids 28
Control Challenge A multi-level version of distributed adaptive control Attends to local and system control needs Reconfigurability plus tuning, i.e. can attack problems as they arise in staged response Call it global control 12 October 2009 David J Hill The Australian National University Adaptive Grids 29
From Ian Hiskens, Cornell Uni 12 October 2009 David J Hill The Australian National University Adaptive Grids 30
We already do well but we can do more! Currently SCADA has real-time data every 2 secs, state estimation, optimal power flow, security analysis etc that s already smart But this is forty year old concept (following 1965 blackout etc) Also its confined to generation-transmission system level And tends to treat problems separately, e.g. angle stability, voltage stability We now have PMUs which can give data in millisecs And major advances in technology especially ICT, power electronics With whole ICT repertoire we can do control at all levels for distributed generation, load and storage And we can coordinate a lot better, e.g. use refined load control to help system stabilities in a cascading situation
Global Control Framework (Leung, Hill and Zhang, 2009) 512 June October 2009 2009 David J J Hill Hill The The Australian National University Massive Adaptive Networks Grids 32
Other Ideas Our view of control is autistic ; for massive systems get cognitive overload ; Maybe just viewing the problem as computation reduction is inadequate; Will need more than just using structure better; In global control used indicators and switching, c.f. economic control; Computer scientists have ad hoc techniques for planning in large systems; we have systematic techniques for simple systems? 12 October 2009 David J Hill The Australian National University Adaptive Grids 33
Comments by ANU Computer Scientist The machine learning area has learned a lot from the control area in the past We see adaptive control as a precise way to deal with simple systems Machine learning has a lot of tools and tricks, a bit ad hoc, but does deal with complex systems Maybe its time to see how machine learning can help control?
Hewitt: I've been training extremely hard, putting in a lot of hours on the court (BBC Sports) An example of Learning by doing Fast responses needed
Learning-based Control Improves its performance based on past experiences (Fu, 1969; Farrell and Baker, 1993) Effectively recall and reuse the learned knowledge Use stability robustness to handle mismatch Can be used to reduce space for optimization
Pattern-based Control - used in large systems, e.g. Lissajous recordings of faults power systems - not developed in control area Dynamic pattern recognition Switching/tuning control between different patterns patterns as local models stability issues Ref: Wang and Hill, Deterministic Learning Theory for Identification, Recognition and Control, CRC Press, 2009. Towards development of a human-like learning and control methodology
Ref: Yusheng Xue, PSCC 2005
Coordinated Voltage Control 2 1 G G 30 39 9 8 G 5 7 37 25 3 4 13 6 G 31 26 18 15 14 12 11 10 G 32 G 28 29 27 38 17 16 24 19 20 22 34 G 33 21 G 36 23 G G 35 Voltage control Aim: Maintain steady voltages at all buses. Control devices: Tap changers, capacitors, load shedding The New England 39-bus Power System
Coordinated Voltage Control Coordinated Voltage Control (CVC) CVC is a scheme relies upon the simulated performance of a power system, coordinated scheduling and switching voltage control devices CVC include three aspects of system design: sequencing: decide the order of control actions timing: decide the switching time of each control action tuning: decide the values of the adjustable parameters of each control action
On-line Multi-Objective CVC System Control Output MCVC System: Power System Off-line global search On-line flexible control On-line learning Some Possible Faults Global Search: Get non-dominated solutions Data Base: 1.faults, 2.order of effective controllers 3.objective values of non-dominated solutions Evaluation Objective functions: J J J vi = min = min act n c load = min i k t n v load it k v iref Learning Local Search: 1.Get available controllers, 2.Searching neighborhood Get Available Controllers Get from Database Multiple Criteria Decision Making Mid-term Short term On-line Learning Mid-term Short term Off-line Searching On-line Adaptive Control
Case Study Case1: Tripping Generator 32 2 1 G Generator32 tripped at 15s G 30 G 37 25 3 39 4 26 28 29 27 38 18 17 16 21 15 14 24 G 36 G Case1: Tripping Generator36 and Line2-3 9 8 5 7 G 6 13 31 12 11 10 G 32 G 19 20 22 34 G 33 23 G 35
Case Study Case1: Tripping Generator 32 Order of effective controllers: No. 1 2 3 4 5 6 7 8 9 Ctrl Ltc31 Ltc30 Ltc35 Ltc11 Ltc12 Ltc33 C13 C13 Ltc37 move +1 +1 +1 +1 +1 +1 +0.15 +0.30 +1 No. 10 11 12 13 14 15 16 17 18 Ctrl C7 C7 C8 C8 C4 C4 Ltc38 Ltc36 Ltc34 System performance: move +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +1 +1 +1 No. 19 20 21 22 23 24 25 26 27 Ctrl C15 C15 C3 C3 C18 C18 C16 C16 Ltc39 move +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +1 No. 28 29 30 31 32 33 34 35 36 Ctrl C24 C24 C27 C27 C21 C21 C26 C26 C25 move +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 No. 37 38 39 40 41 42 43 44 45 Ctrl C25 C23 C23 C28 C28 C29 C29 C20 C20 move +0.3 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 +0.15 +0.30 Control preferences: (1) the solution which can recover bus voltages very fast is the most desirable one. Totally 28 controllers, 39 movements of control are used. (2) a solution uses less control actions is the best one. Totally 26 controllers, 29 movements of control are used.
Case Study Case2: Tripping Generator 36 and Line 2-3 Control Scenario Time 30s 60s 180s 540s 660s 1140s Event Line3-2 tripping G36 tripping Line3-2 and G36 reconnection Line3-2 and G36 tripping together Line3-2 and G36 reconnection Line3-2 and G36 tripping together System performance:
Outline Future grids Challenges New control ideas Example: Voltage control Conclusions 12 October 2009 David J Hill The Australian National University Adaptive Grids 45
Future Work Combine Computer science for learning, planning, diagnosis, visualization, data structures etc Networks for structure Control for dynamics to give algorithms which scale Link to other levels: power electronics, economics