Bulk Power System Dynamics and Control V IREP2001:Onomichi GLOBAL HYBRID CONTROL OF POWER SYSTEMS David J Hill City University of Hong Kong (on leave from Sydney University) Co-authors: Yi Guo, Mats Larsson, Youyi Wang

OUTLINE  Introduction  Global Control Ideas  Global Transient Stability and Voltage Regulation  Emergency Voltage Control  Conclusions

GLOBAL CONTROL IDEAS  Introduction  Hybrid Models  Control Elements  Bifurcations and Global Control  Optimal Coordination and Swarming  Issues for Practical Implementation

Trends  Environmental limits  Load growth  Deregulation All push the system harder

Mathematical Complexity  Stability margins reducing, ie more difficult dynamics (nonlinearity)  Interconnection, ie larger-scale  More uncertainty System structure changing No nominal operating point Less modelling data  Coordinated control with mixed signals, costs and actions (heterogeneity)

Specific Features of Complexity  Large-scale network structure  Controls embedded, some with scope for tuning; further design must allow for and enlist  Hierarchical control structure  Control actions largely determined and have diverse timing, cost and priority  Control goals are multi-objective with local and global requirements which vary with operating state  Control interacts with planning

Control Challenge In general, we need a high-level version of distributed adaptive control which “swarms” around a complex system attacking problems as they arise, but keeping a meta-view so that other problems are not ignored ie. “reconfigurability” built in

Hybrid Models dynamic state variables x algebraic state variables ω parameters/controls  u)

Control Elements Those existing controllers and their tunable parameters which are free to adjust for system-wide purposes

Bifurcations and Global Control  Power systems have benefited from bifurcation theory  Most nonlinear control methodology does not recognise bifurcations

Bifurcation Control  Avoiding the bifurcation  Eliminating the bifurcation  “Delaying” the bifurcation  Stabilisation through bifurcations Can we control across boundaries?

What Can Modern Control Do?  Robust control  Adaptive control  Nonlinear control  Fuzzy control  Neural control

A Strategy  Bifurcation boundaries define domains of operation where dynamical behaviour is qualitatively different  Design controllers for each region and switch between them

Optimal Coordination and Swarming Nonlinear, multiple controls Swarming via   Optimal coordination via  

Global Control  Global view of nonlinear system  State space segmentation into structurally stable regions  Identification of regional controllers local models various control objectives different regional controller design approaches  Combination and coordination of regional controllers, e.g. scheduling, switching, hierarchical, hybrid control

Control Algorithms  Local tunable controllers, eg robust, adaptive etc  Optimal control (hybrid systems)  Staged optimisation  Predictive control  Speed-gradient and passivity  Structure in HJ eqn, etc

GLOBAL TRANSIENT STABILITY AND VOLTAGE REGULATION  Introduction  Dynamical Model  Local Controllers  Global Controller Reference: Y Guo, DJHill and Y Wang, Global transient stability and voltage regulation for power systems, IEEE Trans Power Systems, to appear.

Introduction  Transient stability and voltage regulation are required at different stages of system operation  Deal with the two problems separately, or employ a switching strategy of two different controllers which causes a discontinuity of system behaviour  Aim to design global control law to co-ordinate the transient stabilizer and voltage regulator, using heterogeneous control strategy  The global control objective is achieved with smooth and robust responses with respect to different transient faults.

SMIB Power System Model

Local Controllers  Transient controller:  Voltage controller:

A Switching Controller (t 0 is the fault occuring time, t s is the switching time) Disadvantages: The switching time is fixed; Not robust with respect to different faults.

Global Controller Design  The fault sequence is NOT known beforehand  The control law in each region is specified to be the usual type developed from model-based (nonlinear) control techniques  The global control law is the above weighted sum of local controllers type, which achieves smooth transitions between the transient period and post-transient period  The controller is globally effective in the presence of different uncertain faults; also the controller is robust with respect to parameter uncertainties

Global Controller Design  Operating region membership function:

Global Controller Design  Global control law:  Advantages: Control action is determined by online measurement of power frequency and voltage, which makes it unnecessary to know the fault sequence beforehand The controller is globally effective in the presence of different uncertain faults The controller inherits the properties of local controllers, i.e., it is robust with respect to parameter uncertainties

Simulations uTemporary fault + permanent fault: Stage 1: The system is in a pre-fault steady state Stage 2: A fault occurs at t=t 0 Stage 3: The fault is removed by opening the breakers of the faulted line at t=t 1 Stage 4: The transmission lines are restored at t=t 3 Stage 5: Another fault occurs at t=t 4 Stage 6: The fault is removed by opening the breakers of the faulted line at t=t 5 Stage 7: The system is in a post-fault state In the simulations, t 0 =0.1s, t 1 =0.25s, t 3 =1.4s, t 4 =2.1s, t 5 =2.25s; =0.04.

EMERGENCY VOLTAGE CONTROL  Introduction  System Modelling  Control Problem Formulation  Tree Search Method  Simulation Results  Other Possibilities Reference: M Larsson, DJHill and G Olsson, Emergency voltage control using searching and predictive control, International J of Electrical Power and Energy Systems, to appear.

Coordinated Control Scheme (Popovic, Hill and Wu, presented in Santorini)  Provide voltage regulation  Provide security enhancement  Control actions reactive power compensation tap regulation load control FACTs  Traditionally, done one by one, trial and error

 Why coordination minimum overall effort / cost maximum control effect better voltage profile, ie. better quality of supply  Difficulty Combination of dissimilar controls

Optimal scheduling of control actions  Actual control sequence accounts for combination of dissimilar controls different response speeds different dynamic characteristics priority  Optimal scheduling by economic cost availability of controls  When, how to take actions at each step?

Problem formulation subject to: (i) controls capability constraints (ii) stability constraints

Optimal Scheduling (  =0.2)

Model Predictive Control approach  Widespread in process control  Multivariable, nonlinear allowed naturally  Constraint handling  Future behaviour predicted for many candidate input sequences  Optimal input sequence selected by (constrained) optimization

Optimization by search  All controls are switching actions  Combinatorial optimization problem  Organize control state space in tree structure  Search tree for optimum  Combinatorial explosion  Search heuristics  Similar problem as solved in chess computers !

uNumerical example

Simulation Example (Fig 17)

CONCLUSIONS  Complex System Features  Global Control  Possibilities for Power Systems

Complex System Features  Control over wide ranges of operating conditions  Nonlinearity, ie control “in the large”  High dimension, ie large-scale  Multiple steady-state solutions  Qualitatively different behavior under different operating conditions  Lack of complete explicitly analytical description  Indices flag proximity to problems, ie bifurcations  ‘Elements’ of control physically based  Accommodate different control objectives  Optimal coordination required

Global Control  Global view of nonlinear system  State space segmentation into structurally stable regions  Identification of regional controllers local models various control objectives  Optimal combination and coordination of regional controllers, e.g. scheduling, switching, hierarchical, hybrid control  Swarming type adaptive control

Possibilities for Power Systems  Power systems are increasing in complexity  Security limits have huge financial implications  Control-based expansion  Modelling, analysis, control might all need to be redone  Develop hybrid, global models and control  Develop swarming type optimal hierarchical control of all available devices  Multi-level swarming, ie devices to system levels, according to where problem is  Adaptively group up the influential and available controls of various types to attack a problem as and when it arises  Project in HK considers power electronic controls.