GLOBAL HYBRID CONTROL OF POWER SYSTEMS Professor Peter P GLOBAL HYBRID CONTROL OF POWER SYSTEMS Professor Peter P. Groumpos Graduate course Department of Electrical and Computer Engineering A SEMESTER 2016-17 We discuss a promising approach to do control for general complex systems. I will only describe it in general terms. There are lots of more theoretical aspects. The aim in this project is to further develop it for power systems. Co-authors are in USA, Sweden and Singapore respectively, but all work was done while they were in Sydney.

OUTLINE Introduction Global Control Ideas Global Transient Stability and Voltage Regulation Emergency Voltage Control Conclusions After general ideas, look at two specific cases which are more recent work. Say at outset that the term is just a simple way to capture the aims of: Control in the large, cf global stability Control over a large-scale system, cf global in a geographical sense

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 Global control is a response to increasing complexity.

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 These features apply to power systems and many others like chemical processes.

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 Complexity has led to the use of AI methods, often ignoring models; we are here advocating getting the best of both in a framework. One of the problems with applying modern control methods like adaptive, fuzzy or robust control to power systems is that they are established in a generic framework which is not sensitive to the structure of particular practical systems. Hence many practitioners get the impression that these methods, while sounding appealing at first, are really intellectual games. A more specific challenge is to blend these new ideas into the overall scheme of control which exists, ie excitation control, PSS, network regulators, eg tap-changers, security control. Typically, much of this is already trusted and we only need some new control modules and some overall co-ordination.

Hybrid Models dynamic state variables x algebraic state variables ω parameters/controls l = (q, u, u) Well-used to these models of DAD form. x includes generator fluxes, continuous control and load dynamics y includes network voltages, powers Nonlinear discrete acting controls, eg tap changers, FACTS, give discrete part Q represents tunable parameters n represents (structured) uncertainty u represents control variables which are yet to be designed

Control Elements Those existing controllers and their tunable parameters which are free to adjust for system-wide purposes Recall PSS where we design a dynamical feedback and keep some parameters tunable. Could be any simple or complex controller. Required to perform well inspite of uncertainty ni Use qi to implement coordination.

Bifurcations and Global Control Power systems have benefited from bifurcation theory Most nonlinear control methodology does not recognise bifurcations There is an enormous literature on nonlinear systems and control. Most nonlinear control has only considered small systems and without bifurcations (effectively via assumptions). Our control must work for large systems with lots of bifurcations!

Bifurcation Control Avoiding the bifurcation Eliminating the bifurcation “Delaying” the bifurcation Stabilisation through bifurcations Can we control across boundaries? This is normal idea in power systems. Avoidance is possible with PQ control, ie keep a margin from instability. Possible to eliminate or at least delay the bifurcation with nonlinear actions. Stabilisation of bifurcated solutions, ie as pass through a surface, involves damping any unstable or oscillatory solutions that arise. Stabilization of

What Can Modern Control Do? Robust control Adaptive control Nonlinear control Fuzzy control Neural control RC: For unstructured uncertainty only AC: Requires stability of frozen systems NC: In principle, but usual geometric methods have no sensitivity to structure in models. FC: Does’t want models at all! NC: Needs to be trained for all circumstances met; will not extrapolate to bifurcations

A Strategy Bifurcation boundaries define domains of operation where dynamical behaviour is qualitatively different Design controllers for each region and switch between them A possible control strategy, accepting the boundaries, is to design controllers for each structurally stable region and switch between them in some way in a two-level structure. Assuming the state-parameter space is partitioned into two domains, the control u takes this form: ue provides the new equilibrium manifold; μ1 and μ2 are functions of an indicator variable expressing the closeness to a particular region or control concern; and u1 and u2 are the local controllers. Has a weighted Sugeno type form used in fuzzy control. Allows the controller to adjust the individual contributions of the local controllers according to the particular problem being faced at a given time. For instance when an instability occurs in a certain domain or in a certain variable type, eg voltage instability, the indicators driving the weighting functions will automatically adjust the control to emphasise the appropriate actions.

Optimal Coordination and Swarming Nonlinear, multiple controls Swarming via mi Optimal coordination via qi No major problem that the controllers are complex; they will be expressed in software code. Also, at system-wide level, the controller complexity is related more to the number of parameters that need tuning. This may involve simply tuning of free parameters, as in the PSS, or a more complicated scheduling exercise which allows for any control redundancy to use scaling and timing according to control costs, dynamic properties and priority - quite complicated for dynamical systems as it requires the solution of the optimal control problem. When optimal coordination of many such controllers is considered, we can think of numerous controller elements in a complex system as swarming <cite>BON99</cite> to deal with problems as they arise.

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 There are enormous possibilities with this framework for constructing and analysing algorithms.

GLOBAL TRANSIENT STABILITY AND VOLTAGE REGULATION Introduction Dynamical Model Local Controllers Global Controller

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 (t0 is the fault occuring time, ts 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: Z variable is the key to getting excitation to attend to the problems as they arise

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 Temporary fault + permanent fault: Stage 1: The system is in a pre-fault steady state Stage 2: A fault occurs at t=t0 Stage 3: The fault is removed by opening the breakers of the faulted line at t=t1 Stage 4: The transmission lines are restored at t=t3 Stage 5: Another fault occurs at t=t4 Stage 6: The fault is removed by opening the breakers of the faulted line at t=t5 Stage 7: The system is in a post-fault state In the simulations, t0=0.1s, t1=0.25s, t3=1.4s, t4=2.1s, t5=2.25s; l=0.04.

We design smooth nonlinear feedback control law for the excitation system, such that the closed-loop power system is transiently stable when subjected to a severe disturbance, and restores the steady pre-fault voltage value after the disturbance.

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,

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 These are typically all switching type controls which must work with continuously acting ones.

Why coordination Difficulty 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) We have studied this approach for a large system, ie NSW grid (Wu et al, IEEE Trans to appear).

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 DDP emerged as an obvious way to deal with staging of the control. But it is just a way to do discrete optimal control. There are connections to other optimal control techniques like MPC, which have different tools. MPC has developed to handle considerable complexity.

In contrast to normal MPC, we now have a specific set of basic control actions. There are moves in this direction mathematically, but here the actions are physical. Also there are lots of combinations to check.

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!

Numerical example

Simulation Example (Fig 17) Response with ESP, LMPC, ESPLO approximate predictors

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 Of course optimal tuning is done in practice, eg all the PSS, but now asking for more, ie optimal staging of dissimilar controls. This leads to concept of Global Control. Use the term global here in both senses: Large-scale, geographically, ie the globe In the large or global stability, ie wrt to the state space All the usual predictive, adaptive, neural methods break down when system moves across bifurcation boundaries. Set up control actions for each region. A picture can be use of the control actions ‘swarming’ on problems as they arise as detected by indicators.

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.