Distributed predictive control of the 7-Machine CIGRÉ power system

Stable operation of the future electrical power system will require efficient techniques for supply-demand balancing, i.e., load-frequency control, due to liberalization of electrical energy production. Currently, there is a growing interest for asymptotically stabilizing the grid frequency via model predictive control (MPC). However, the centralized implementation of standard MPC is hampered by the scale and complexity of power networks. In this paper we therefore evaluate the suitability of a scalable, distributed Lyapunovbased MPC algorithm as an alternative to conventional balancing techniques. The approach is particularly suited for largescale power networks, as it employs only local information and limited communication between directly-coupled generator buses to provide a stabilizing control action. The effectiveness of the distributed control scheme is assessed by simulating it in closed-loop with the 7-machine CIGRE benchmark system

Model predictive control for load frequency control of an interconnected power system.

Masters Degree. University of KwaZulu-Natal, Durban.Reliable load frequency control (LFC) is of importance in modern power system generation, transmission and distribution, it has been the basis of research on advanced control theory and application in recent years. In LFC scheme, local load disturbance, inter-area ties power fluctuation, frequency deviation, generation rate constraints (GRC), and governor dead band (GDB) are the major nonlinear factors on the control scheme that affect the dynamic response of the system to a large extent. Over the years, many methods have been designed for LFC problem of which model predictive controller (MPC) stands out due to its advantages. MPC is a control approach that simulates the feature behaviour of a system it controls and based on the result of the simulation attempt to find a control output such that the simulated system behaves optimally. When applied to LFC it copes with the perturbation. In this dissertation, robust distributed model predictive control (RDMPC) is developed as a controller scheme for LFC and is compared with a proportional integral derivative (PID) controller using MATLAB/Simulink for two-area and three-area hydro-thermal interconnected power system. From the simulation result, RDMPC significantly shows robustness over PID when compared in frequency deviation and area control error. It is observed that RDMPC still lags, from system varying dynamics and uncertainty despite its robustness over PID, hence an adaptive model predictive control (AMPC) is developed to improve on the performance of RDMPC. In order to evaluate the efficacy of this proposed controller, robustness and comparative analysis is performed using MATLAB/Simulink between the conventional PID, RDMPC, and AMPC with respect to performance indices such as settling time, undershoot and peak overshoot when subjected to frequency deviation, tie-line active power deviation, and area control error. Also, the dynamic response of the hydrothermal systems is analysed and compared in the presence of nonlinear constraints such as generator rate constraint (GRC) and governor dead band (GDB). The result from the simulation tests shows that AMPC has a better dynamic response when compared with PID, and RDMPC with a significant improvement

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches

Limited-Communication Distributed Model Predictive Control for HVAC Systems

This dissertation proposes a Limited-Communication Distributed Model Predictive Control algorithm for networks with constrained discrete-time linear processes as local subsystems. The introduced algorithm has an iterative and cooperative framework with neighbor-to-neighbor communication structure. Convergence to a centralized solution is guaranteed by requiring coupled subsystems with local information to cooperate only. During an iteration, a local controller exchanges its predicted effects with local neighbors (which are treated as measured input disturbances in local dynamics) and receives the neighbor sensitivities for these effects at next iteration. Then the controller minimizes a local cost function that counts for the future effects to neighbors weighted by the received sensitivity information. Distributed observers are employed to estimate local states through local input-output signals. Closed-loop stability is proved for sufficiently long horizons. To reduce the computational loads associated with large horizons, local decisions are parametrized by Laguerre functions. A local agent can also reduce the communication burden by parametrizing the communicated data with Laguerre sequences. So far, convergence and closed-loop stability of the algorithm are proven under the assumptions of accessing all subsystem dynamics and cost functions information by a centralized monitor and sufficient number of iterations per sampling. However, these are not mild assumptions for many applications. To design a local convergence condition or a global condition that requires less information, tools from dissipativity theory are used. Although they are conservative conditions, the algorithm convergence can now be ensured either by requiring a distributed subsystem to show dissipativity in the local information dynamic inputs-outputs with gain less than unity or solving a global dissipative inequality with subsystem dissipativity gains and network topology only. Free variables are added to the local problems with the object of having freedom to design such convergence conditions. However, these new variables will result into a suboptimal algorithm that affects the proposed closed-loop stability. To ensure local MPC stability, therefore, a distributed synthesis, which considers the system interactions, of stabilizing terminal costs is introduced. Finally, to illustrate the aspects of the algorithm, coupled tank process and building HVAC system are used as application examples

Distributed Control of HVAC&R Networks

Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems are a major component of worldwide energy consumption, and frequently consist of complex networks of interconnected components. The ubiquitous nature of these systems suggests that improvements in their energy efficiency characteristics can have significant impact on global energy consumption. The complexity of the systems, however, means that decentralized control schemes will not always suffice to balance competing goals of energy efficiency and occupant comfort and safety. This dissertation proposes control solutions for three facets of this problem. The first is a cascaded control architecture for actuators, such as electronic expansion valves, that provides excellent disturbance rejection and setpoint tracking characteristics, as well as partial nonlinearity compensation without a compensation model. The second solution is a hierarchical control architecture for multiple-evaporator vapor compression systems that uses model predictive control (MPC) at both the supervisory and component levels. The controllers leverage the characteristics of MPC to balance energy efficiency with occupant comfort. Since the local controllers are decentralized, the architecture retains a degree of modularity—changing one component does not require changing all controllers. The final contribution is a new distributed optimization algorithm that is rooted in distributed MPC and is especially motivated by HVAC&R systems. This algorithm allows local level optimizers to iterate to a centralized solution. The optimizers have no knowledge of any plant other than the plant they are associated with, and only need to communicate with their immediate neighbors. The efficacy of the algorithm is displayed with two sets of examples. One example is simulation based, wherein a building is modeled in the EnergyPlus software suite. The other is an experimental example. In this example, the algorithm is applied to a multiple evaporator vapor compression system. In both cases the design method is discussed, and the ability of the algorithm to reduce energy consumption when properly applied is demonstrated

The role of population games in the design of optimization-based controllers: a large-scale insight

Cotutela Universitat Politècnica de Catalunya i Universidad de los AndesPremi CEA Springer Award 2017, a la millor tesi d'enginyeria de control a EspanyaEuropean PhD Award on Control for Complex and Heterogeneous Systems, atorgat pel European Embedded Control InstituteThis thesis is mainly devoted to the study of the role of evolutionary-game theory in the design of distributed optimization-based controllers. Game theoretical approaches have been used in several engineering fields, e.g., drainage wastewater systems, bandwidth allocation, wireless networks, cyber security, congestion games, wind turbines, temperature control, among others. On the other hand, a specific class of games, known as population games, have been mainly used in the design of controllers to manage a limited resource. This game approach is suitable for resource allocation problems since, under the framework of full-potential games, the population games can satisfy a unique coupled constraint while maximizing a potential function. First, this thesis discusses how the classical approach of the population games can contribute and complement the design of optimization-based controllers. Therefore, this dissertation assigns special interest on how the features of the population-game approach can be exploited extending their capabilities in the solution of distributed optimization problems. In addition, density games are studied in order to consider multiple coupled constraints and preserving the non-centralized information requirements. Furthermore, it is established a close relationship between the possible interactions among agents in a population with the constrained information sharing among different local controllers. On the other hand, coalitional games are discussed focusing on the Shapley power index. This power index has been used to assign an appropriate rewarding to players in function of their contributions to all possible coalitions. Even though this power index is quite useful in the engineering context, since it involves notions of fairness and/or relevance (how important players are), the main difficulty of the implementation of the Shapley value in engineering applications is related to the high computational burden. Therefore, this dissertation studies the Shapley value in order to propose an alternative manner to compute it reducing computational time, and a different way to find it by using distributed communication structures is presented. The studied game theoretical approaches are suitable for the modeling of rational agents involved in a strategic constrained interaction, following local rules and making local decisions in order to achieve a global objective. Making an analogy, distributed optimization-based controllers are composed of local controllers that compute optimal inputs based on local information (constrained interactions with other local controllers) in order to achieve a global control objective. In addition to this analogy, the features that relate the Nash equilibrium with the Karush-Kuhn-Tucker conditions for a constrained optimization problem are exploited for the design of optimization-based controllers, more specifically, for the design of model predictive controller. Moreover, the design of non-centralized controllers is directly related to the partitioning of a system, i.e., it is necessary to represent the whole system as the composition of multiple sub-systems. This task is not a trivial procedure since several considerations should be taken into account, e.g., availability of information, dynamical coupling in the system, regularity in the amount of variables for each sub-system, among others. Then, this doctoral dissertation also discusses the partitioning problem for large-scale systems and the role that this procedure plays in the design of distributed optimization-based controllers. Finally, dynamical partitioning strategies are presented with distributed population-games-based controllers. Some engineering applications are presented to illustrate and test the performance of all the proposed control strategies, e.g., the Barcelona water supply network, multiple continuous stirred tank reactors, system of multiple unmanned aerial vehicles.Esta tesis doctoral consiste principalmente en el estudio del rol que desempeña la teoría de juegos evolutiva en el diseño de controladores distribuidos basados en optimización. Diversos enfoques de la teoría de juegos han sido usados en múltiples campos de la ingeniera, por ejemplo, en sistemas de drenaje urbano, para la asignación de anchos de banda, en redes inalámbricas, en ciber-seguridad, en juegos de congestión, turbinas eólicas, control de temperatura, entre otros. Por otra parte, una clase especifica de juegos, conocidos como juegos poblacionales, se han usado principalmente en el diseño de controladores encargados de determinar la apropiada asignación de recursos. Esta clase de juegos es apropiada para problemas de distribución dinámica de recursos dado que, en el contexto de juegos poblacionales, los juegos poblacionales pueden ser usados para maximizar una función potencial mientras se satisface una restricción acoplada. Primero, esta tesis doctoral presenta como el enfoque clásico de los juegos poblacionales pueden contribuir y complementar en el diseño de controladores basados en optimización. Posteriormente, esta disertación concentra su atención en cómo las características de los juegos poblacionales pueden ser aprovechadas y extendidas para dar solución a problemas de optimización de forma distribuida. Adicionalmente, los juegos con dependencia de densidad son estudiados con el fin de considerar múltiples restricciones mientras se preservan las características no centralizadas de los requerimientos de información. Finalmente, se establece una estrecha relación entre las posibles interacciones de los agentes en una población y las restricciones de intercambio de información entre diversos controladores locales. También, se desarrolla una discusión sobre los juegos cooperativos y el índice de poder conocido como el valor de Shapley. Este índice de poder ha sido usado para la apropiada asignación de beneficios para un jugador en función de sus contribuciones a todas las posibles coaliciones que pueden formarse. Aunque este índice de poder es de gran utilidad en el contexto ingenieril, ya que involucra nociones de justicia y/o relevancia, la principal dificultad para implementar el valor de Shapley en aplicaciones de ingeniería está asociado a los altos costos computacionales para encontrarlo. En consecuencia, esta disertación doctoral estudia el valor de Shapley con el fin de ofrecer una alternativa para calcular este índice de poder reduciendo los costos computacionales e incluso contemplando estructuras distribuidas de comunicación. Los enfoques de la teoría de juegos estudiados son apropiados para el modelamiento de agentes racionales involucrados en una interacción estratégica con restricciones, siguiendo reglas locales y tomando decisiones locales para alcanzar un objetivo global. Realizando una analogía, los controladores distribuidos basados en optimización están compuestos por controladores locales que calculan acciones óptimas basados en información local (considerando interacciones restringidas con otros controladores locales) con el fin de alcanzar un objetivo global. Adicional a esta analogía, las características que relacionan el equilibrio de Nash con las condiciones de Karush-Kuhn-Tucker en un problema de optimizaciones con restricciones son aprovechadas para el diseño de controladores basados en optimización, más específicamente, para el diseño de controladores predictivos. Por otra parte, el diseño de controladores no centralizados está directamente relacionado con el particionado de un sistema, es decir, es necesario representar el sistema en su totalidad por medio del conjunto de varios sub-sistemas. Esta tarea no es un procedimiento trivial puesto que es necesario tener en cuenta varias consideraciones, por ejemplo, la disponibilidad de información, el acople dinámico en el sistema, la regularidad en cuanto a la cantidad de variables en cada sub-sistema, entre otras. Por lo tanto, esta disertación doctoral también desarrolla una discusión alrededor del problema de particionado para sistemas de gran escala y respecto al rol que este procedimiento de particionado juega en el diseño de controladores distribuidos basados en optimización. Finalmente, se presentan estrategias de particionado dinámico junto con controladores basados en juegos poblacionales. Algunas aplicaciones en ingeniería son usadas para ilustrar y probar los controladores diseñados por medio de las contribuciones novedosas basadas en teoría de juegos, estas son, la red de agua potable de Barcelona, múltiples reactores, sistema compuesto por varios vehículos aéreos no tripulados y un sistema de distribución de agua.Aquesta tesi doctoral consisteix principalment en l'estudi del paper que exerceix la teoria de jocs evolutiva en el disseny de controladors distribuïts basats en optimització. Diversos enfocaments de la teoria de jocs han estat usats en múltiples camps de l'enginyeria, per exemple, en sistemes de drenatge urbà, per a l’assignació d'amples de banda, en xarxes sense fils, a ciber-seguretat, en jocs de congestió, turbines eòliques, control de temperatura, entre altres. D'altra banda, una classe especifica de jocs, coneguts com jocs poblacionals, s'han fet servir principalment en el disseny de controladors encarregats de determinar l'apropiada assignació de recursos. Aquesta classe de jocs és apropiada per a problemes de distribució dinàmica de recursos atès que, en el context de jocs poblacionals, aquests poden ser usats per a maximitzar una funció potencial mentre es satisfà una restricció acoblada. Primer, aquesta tesi doctoral presenta com l'enfocament clàssic dels jocs poblacionals poden contribuir i complementar en el disseny de controladors basats en optimització. Posteriorment, aquesta dissertació concentra la seva atenció en com les característiques dels jocs poblacionals poden ser aprofitades i esteses per donar solució a problemes d’optimització de forma distribuïda. Addicionalment, els jocs amb dependència de densitat són estudiats amb la _finalitat de considerar múltiples restriccions mentre es preserven les característiques no centralitzades dels requeriments d’informació. Finalment, s'estableix una estreta relació entre les possibles interaccions dels agents en una població i les restriccions d'intercanvi d’informació entre diversos controladors locals. També, es desenvolupa una discussió sobre els jocs cooperatius i l’índex de poder conegut com el valor de Shapley. Aquest índex de poder ha estat usat per l'apropiada assignació de beneficis per a un jugador en funció de les seves contribucions a totes les possibles coalicions que poden formar-se. Encara que aquest índex de poder es de gran utilitat en el context de l'enginyeria, ja que involucra nocions de justícia i/o rellevància, la principal dificultat per implementar el valor de Shapley en aplicacions d'enginyeria està associat als alts costos computacionals per trobar-lo. En conseqüència, aquesta dissertació doctoral estudia el valor de Shapley per tal d'oferir una alternativa per calcular aquest índex de poder reduint els costos computacionals i fins i tot contemplant estructures distribuïdes de comunicació. Els enfocaments de la teoria de jocs estudiats són apropiats per al modelatge d'agents racionals involucrats en una interacció estratègica amb restriccions, seguint regles locals i prenent decisions locals per assolir un objectiu global. Realitzant una analogia, els controladors distribuïts basats en optimització estan compostos per controladors locals que calculen accions optimes basats en informació local (considerant interaccions restringides amb altres controladors locals) per tal d'assolir un objectiu global. Addicional a aquesta analogia, les característiques que relacionen l'equilibri de Nash amb les condicions de Karush-Kuhn-Tucker en un problema d’optimització amb restriccions són aprofitades per al disseny de controladors basats en optimització, més específicament, per al disseny de controladors predictius. D'altra banda, el disseny de controladors no centralitzats està directament relacionat amb la partició d'un sistema, és a dir, cal representar el sistema en la seva totalitat per mitjà del conjunt de diversos sub-sistemes. Aquesta tasca no és un procés trivial, ja que cal tenir en compte diverses consideracions, per exemple, la disponibilitat d’informació, l'acoblament dinàmic en el sistema, i la regularitat pel que fa a la quantitat de variables en cada sub-sistema, entre d'altres. Per tant, aquesta dissertació doctoral també desenvolupa una discussió al voltant del problema de partició per a sistemes de gran escala i respecte al paper que aquest procediment de partició juga en el disseny de controladors distribuïts basats en optimització. Finalment, es presenten estratègies de partició dinàmic juntament amb controladors basats en jocs poblacionals. Algunes aplicacions en enginyeria són usades per il·lustrar i provar els controladors dissenyats per mitjà de les contribucions noves basades en teoria de jocs, aquestes són: la xarxa d'aigua potable de Barcelona, múltiples reactors, sistema compost per diversos vehicles aeris no tripulats i un sistema de distribució d'aigua.Award-winningPostprint (published version

Robust Distributed Model Predictive Control Strategies of Chemical Processes

This work focuses on the robustness issues related to distributed model predictive control (DMPC) strategies in the presence of model uncertainty. The robustness of DMPC with respect to model uncertainty has been identified by researchers as a key factor in the successful application of DMPC. A first task towards the formulation of robust DMPC strategy was to propose a new systematic methodology for the selection of a control structure in the context of DMPC. The methodology is based on the trade-off between performance and simplicity of structure (e.g., a centralized versus decentralized structure) and is formulated as a multi-objective mixed-integer nonlinear program (MINLP). The multi-objective function is composed of the contribution of two indices: 1) closed-loop performance index computed as an upper bound on the variability of the closed-loop system due to the effect on the output error of either set-point or disturbance input, and 2) a connectivity index used as a measure of the simplicity of the control structure. The parametric uncertainty in the models of the process is also considered in the methodology and it is described by a polytopic representation whereby the actual process’s states are assumed to evolve within a polytope whose vertices are defined by linear models that can be obtained from either linearizing a nonlinear model or from their identification in the neighborhood of different operating conditions. The system’s closed-loop performance and stability are formulated as Linear Matrix Inequalities (LMI) problems so that efficient interior-point methods can be exploited. To solve the MINLP a multi-start approach is adopted in which many starting points are generated in an attempt to obtain global optima. The efficiency of the proposed methodology is shown through its application to benchmark simulation examples. The simulation results are consistent with the conclusions obtained from the analysis. The proposed methodology can be applied at the design stage to select the best control configuration in the presence of model errors. A second goal accomplished in this research was the development of a novel online algorithm for robust DMPC that explicitly accounts for parametric uncertainty in the model. This algorithm requires the decomposition of the entire system’s model into N subsystems and the solution of N convex corresponding optimization problems in parallel. The objective of this parallel optimizations is to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Model uncertainty is explicitly considered through the use of polytopic description of the model. The algorithm employs an LMI approach, in which the solutions are convex and obtained in polynomial time. An observer is designed and embedded within each controller to perform state estimations and the stability of the observer integrated with the controller is tested online via LMI conditions. An iterative design method is also proposed for computing the observer gain. This algorithm has many practical advantages, the first of which is the fact that it can be implemented in real-time control applications and thus has the benefit of enabling the use of a decentralized structure while maintaining overall stability and improving the performance of the system. It has been shown that the proposed algorithm can achieve the theoretical performance of centralized control. Furthermore, the proposed algorithm can be formulated using a variety of objectives, such as Nash equilibrium, involving interacting processing units with local objective functions or fully decentralized control in the case of communication failure. Such cases are commonly encountered in the process industry. Simulations examples are considered to illustrate the application of the proposed method. Finally, a third goal was the formulation of a new algorithm to improve the online computational efficiency of DMPC algorithms. The closed-loop dual-mode paradigm was employed in order to perform most of the heavy computations offline using convex optimization to enlarge invariant sets thus rendering the iterative online solution more efficient. The solution requires the satisfaction of only relatively simple constraints and the solution of problems each involving a small number of decision variables. The algorithm requires solving N convex LMI problems in parallel when cooperative scheme is implemented. The option of using Nash scheme formulation is also available for this algorithm. A relaxation method was incorporated with the algorithm to satisfy initial feasibility by introducing slack variables that converge to zero quickly after a small number of early iterations. Simulation case studies have illustrated the applicability of this approach and have demonstrated that significant improvement can be achieved with respect to computation times. Extensions of the current work in the future should address issues of communication loss, delays and actuator failure and their impact on the robustness of DMPC algorithms. In addition, integration of the proposed DMPC algorithms with other layers in automation hierarchy can be an interesting topic for future work