Abstract
Language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Award date  20 Nov 2012 
Place of Publication  Eindhoven 
Publisher  
Print ISBNs  9789038632759 
DOIs  
State  Published  2012 
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Distributed control of deregulated electrical power networks. / Hermans, R.M.
Eindhoven : Technische Universiteit Eindhoven, 2012. 182 p.Research output: Thesis › Phd Thesis 1 (Research TU/e / Graduation TU/e) › Academic
TY  THES
T1  Distributed control of deregulated electrical power networks
AU  Hermans,R.M.
PY  2012
Y1  2012
N2  A prerequisite for reliable operation of electrical power networks is that supply and demand are balanced at all time, as efficient ways for storing large amounts of electrical energy are scarce. Balancing is challenging, however, due to the power system's dimensions and complexity, the low controllability and predictability of demand, and due to strict physical and security limitations, such as finitely fast generator dynamics and finite transmissionline capacities. The need for efficient and secure balancing arrangements is growing stronger with the increasing integration of distributed generation (DG), the ongoing deregulation of production and consumption of electrical energy, and thus, also the provision of many of the ancillary services that are essential for network stability. DG is mostly based on renewable, intermittent sources such as wind and sun, and consequently, it is associated with a much larger uncertainty in supply than conventional, centralized generation. Moreover, with the emergence of deregulated energy markets as core operational mechanism, the prime goal of power system operation is shifted from centralized minimization of costs to the maximization of individual profit by a large number of competing, autonomous market agents. The main objective of this thesis is to investigate the controltechnical possibilities for ensuring efficient, reliable and stable operation of deregulated and badly predictable electrical power networks. Its contributions cover aspects of power system operation on a time scale ranging from dayahead trading of electrical energy to secondbased loadfrequency control. As a first contribution, we identify the maximization of security of supply and market efficiency as the two main, yet conflicting objectives of power system operation. Special attention is paid to congestion management, which is an aspect of power system operation where the tension between reliability and efficiency is particularly apparent. More specifically, the differences between locational pricing and costbased congestion redispatch are analyzed, followed by an assessment of their effects on grid operation. Next, we demonstrate that the current synchronous, energybased market and incentive system does not necessarily motivate producers to exchange power profiles with the electricity grid that contribute to network stability and security of supply. The thesis provides an alternative production scheduling concept as a means to overcome this issue, which relies on standard market arrangements, but settles energy transactions in an asynchronous way. Theoretical analysis and simulation results illustrate that by adopting this method, scheduling efficiency is improved and the strain on balancing reserves can be reduced considerably. A major part of this thesis is dedicated to realtime, i.e., closedloop, balancing or loadfrequency control. With the increasing share of badly predictable DG, there is a growing need for efficient balancing mechanisms that can account for generator and transmission constraints during the operational day. A promising candidate solution is model predictive control (MPC). Because the large dimensions and complexity of electrical power networks hamper a standard, centralized implementation of MPC, we evaluate a number of scalable alternatives, in which the overall control action is computed by a set of local predictive control laws, instead. The extent of intercontroller communication is shown to be positively correlated with prediction accuracy and, thus, attainable closedloop performance. Iterative, systemwide communication/coordination is usually not feasible for large networks, however, and consequently, Paretooptimal performance and coupledconstraint handling are currently out of reach. This also hampers the application of standard costbased stabilization schemes, in which closedloop stability is attained via monotonic convergence of a single, optimal systemwide performance cost. Motivated by the observations regarding noncentralized MPC, the focus is then shifted to distributed control methods for networks of interconnected dynamical systems, with power systems as particular field of application, that can ensure stability based on local model and state information only. First, we propose a noncentralized, constraintbased stabilization scheme, in which the set of stabilizing control actions is specified via separable convergence conditions for a collection of apriori synthesized structured maxcontrol Lyapunov functions (maxCLFs). The method is shown to be nonconservative, in the sense that nonmonotonic convergence of the structured functions along closedloop trajectories is allowed, whereas their construction establishes the existence of a control Lyapunov function, and thus, stability, for the full, interconnected dynamics. Then, an alternative method is provided in which also the demand for a monotonically converging fullsystem CLF is relaxed while retaining the stability certificate. The conditions are embedded in an almostdecentralized Lyapunovbased MPC scheme, in which the local control laws rely on neighbortoneighbor communication only. Secondly, a generalized theorem and example system are provided to show that stabilization methods that rely on the offline synthesis of fixed quadratic storage functions (SFs) fail for even the simplest of linear, timeinvariant networks, if they contain one or more subsystems that are not stable under decoupled operation. This may also impede the application of maxCLF control. As key contribution of this thesis, to solve this issue, we endow the storage functions with a finite set of statedependent parameters. Maxtype convergence conditions are employed to construct a Lyapunov function for the full network, whereas monotonic convergence of the individual SFs is not required. The merit of the provided approach is that the storage functions can be constructed during operation, i.e., along a closedloop trajectory, thus removing the impediment of centralized, offline LF synthesis associated with fixedparameter SFs. It is shown that parameterizedSF synthesis conditions can be efficiently exploited to obtain a scalable, trajectorydependent control scheme that relies on noniterative neighbortoneighbor communication only. For inputaffine network dynamics and quadratic storage functions, the procedure can be implemented by solving a single semidefinite program per node and sampling instant, in a receding horizon fashion. Moreover, by interpolating a collection of soobtained input trajectories, a lowcomplexity explicit control law for linear, timeinvariant systems is obtained that extends the trajectoryspecific convergence property to a much stronger guarantee of closedloop asymptotic stability for a particular set of initial conditions. Finally, we consider the application of maxCLF and parameterized SFs for realtime balancing in multimachine electrical power networks. Given that generators are operated by competitive, profitdriven market agents, the stabilization scheme is extended with the competitive optimization of a set of arbitrarily chosen, local performance cost functions over a finite, receding prediction horizon. The suitability of the distributed Lyapunovbased predictive control and parameterized storage function algorithms is evaluated by simulating them in closedloop with the 7machine CIGRÉ benchmark system. The thesis concludes by summarizing the main contributions, followed by ideas for future research.
AB  A prerequisite for reliable operation of electrical power networks is that supply and demand are balanced at all time, as efficient ways for storing large amounts of electrical energy are scarce. Balancing is challenging, however, due to the power system's dimensions and complexity, the low controllability and predictability of demand, and due to strict physical and security limitations, such as finitely fast generator dynamics and finite transmissionline capacities. The need for efficient and secure balancing arrangements is growing stronger with the increasing integration of distributed generation (DG), the ongoing deregulation of production and consumption of electrical energy, and thus, also the provision of many of the ancillary services that are essential for network stability. DG is mostly based on renewable, intermittent sources such as wind and sun, and consequently, it is associated with a much larger uncertainty in supply than conventional, centralized generation. Moreover, with the emergence of deregulated energy markets as core operational mechanism, the prime goal of power system operation is shifted from centralized minimization of costs to the maximization of individual profit by a large number of competing, autonomous market agents. The main objective of this thesis is to investigate the controltechnical possibilities for ensuring efficient, reliable and stable operation of deregulated and badly predictable electrical power networks. Its contributions cover aspects of power system operation on a time scale ranging from dayahead trading of electrical energy to secondbased loadfrequency control. As a first contribution, we identify the maximization of security of supply and market efficiency as the two main, yet conflicting objectives of power system operation. Special attention is paid to congestion management, which is an aspect of power system operation where the tension between reliability and efficiency is particularly apparent. More specifically, the differences between locational pricing and costbased congestion redispatch are analyzed, followed by an assessment of their effects on grid operation. Next, we demonstrate that the current synchronous, energybased market and incentive system does not necessarily motivate producers to exchange power profiles with the electricity grid that contribute to network stability and security of supply. The thesis provides an alternative production scheduling concept as a means to overcome this issue, which relies on standard market arrangements, but settles energy transactions in an asynchronous way. Theoretical analysis and simulation results illustrate that by adopting this method, scheduling efficiency is improved and the strain on balancing reserves can be reduced considerably. A major part of this thesis is dedicated to realtime, i.e., closedloop, balancing or loadfrequency control. With the increasing share of badly predictable DG, there is a growing need for efficient balancing mechanisms that can account for generator and transmission constraints during the operational day. A promising candidate solution is model predictive control (MPC). Because the large dimensions and complexity of electrical power networks hamper a standard, centralized implementation of MPC, we evaluate a number of scalable alternatives, in which the overall control action is computed by a set of local predictive control laws, instead. The extent of intercontroller communication is shown to be positively correlated with prediction accuracy and, thus, attainable closedloop performance. Iterative, systemwide communication/coordination is usually not feasible for large networks, however, and consequently, Paretooptimal performance and coupledconstraint handling are currently out of reach. This also hampers the application of standard costbased stabilization schemes, in which closedloop stability is attained via monotonic convergence of a single, optimal systemwide performance cost. Motivated by the observations regarding noncentralized MPC, the focus is then shifted to distributed control methods for networks of interconnected dynamical systems, with power systems as particular field of application, that can ensure stability based on local model and state information only. First, we propose a noncentralized, constraintbased stabilization scheme, in which the set of stabilizing control actions is specified via separable convergence conditions for a collection of apriori synthesized structured maxcontrol Lyapunov functions (maxCLFs). The method is shown to be nonconservative, in the sense that nonmonotonic convergence of the structured functions along closedloop trajectories is allowed, whereas their construction establishes the existence of a control Lyapunov function, and thus, stability, for the full, interconnected dynamics. Then, an alternative method is provided in which also the demand for a monotonically converging fullsystem CLF is relaxed while retaining the stability certificate. The conditions are embedded in an almostdecentralized Lyapunovbased MPC scheme, in which the local control laws rely on neighbortoneighbor communication only. Secondly, a generalized theorem and example system are provided to show that stabilization methods that rely on the offline synthesis of fixed quadratic storage functions (SFs) fail for even the simplest of linear, timeinvariant networks, if they contain one or more subsystems that are not stable under decoupled operation. This may also impede the application of maxCLF control. As key contribution of this thesis, to solve this issue, we endow the storage functions with a finite set of statedependent parameters. Maxtype convergence conditions are employed to construct a Lyapunov function for the full network, whereas monotonic convergence of the individual SFs is not required. The merit of the provided approach is that the storage functions can be constructed during operation, i.e., along a closedloop trajectory, thus removing the impediment of centralized, offline LF synthesis associated with fixedparameter SFs. It is shown that parameterizedSF synthesis conditions can be efficiently exploited to obtain a scalable, trajectorydependent control scheme that relies on noniterative neighbortoneighbor communication only. For inputaffine network dynamics and quadratic storage functions, the procedure can be implemented by solving a single semidefinite program per node and sampling instant, in a receding horizon fashion. Moreover, by interpolating a collection of soobtained input trajectories, a lowcomplexity explicit control law for linear, timeinvariant systems is obtained that extends the trajectoryspecific convergence property to a much stronger guarantee of closedloop asymptotic stability for a particular set of initial conditions. Finally, we consider the application of maxCLF and parameterized SFs for realtime balancing in multimachine electrical power networks. Given that generators are operated by competitive, profitdriven market agents, the stabilization scheme is extended with the competitive optimization of a set of arbitrarily chosen, local performance cost functions over a finite, receding prediction horizon. The suitability of the distributed Lyapunovbased predictive control and parameterized storage function algorithms is evaluated by simulating them in closedloop with the 7machine CIGRÉ benchmark system. The thesis concludes by summarizing the main contributions, followed by ideas for future research.
U2  10.6100/IR739207
DO  10.6100/IR739207
M3  Phd Thesis 1 (Research TU/e / Graduation TU/e)
SN  9789038632759
PB  Technische Universiteit Eindhoven
CY  Eindhoven
ER 