Distributed control of deregulated electrical power networks

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 transmission-line 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 control-technical 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 day-ahead trading of electrical energy to second-based load-frequency 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 cost-based congestion redispatch are analyzed, followed by an assessment of their effects on grid operation. Next, we demonstrate that the current synchronous, energy-based 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 real-time, i.e., closed-loop, balancing or load-frequency 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 inter-controller communication is shown to be positively correlated with prediction accuracy and, thus, attainable closed-loop performance. Iterative, system-wide communication/coordination is usually not feasible for large networks, however, and consequently, Pareto-optimal performance and coupled-constraint handling are currently out of reach. This also hampers the application of standard cost-based stabilization schemes, in which closed-loop stability is attained via monotonic convergence of a single, optimal system-wide performance cost. Motivated by the observations regarding non-centralized 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 non-centralized, constraint-based stabilization scheme, in which the set of stabilizing control actions is specified via separable convergence conditions for a collection of a-priori synthesized structured max-control Lyapunov functions (max-CLFs). The method is shown to be non-conservative, in the sense that non-monotonic convergence of the structured functions along closed-loop 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 full-system CLF is relaxed while retaining the stability certificate. The conditions are embedded in an almost-decentralized Lyapunov-based MPC scheme, in which the local control laws rely on neighbor-to-neighbor communication only. Secondly, a generalized theorem and example system are provided to show that stabilization methods that rely on the off-line synthesis of fixed quadratic storage functions (SFs) fail for even the simplest of linear, time-invariant networks, if they contain one or more subsystems that are not stable under decoupled operation. This may also impede the application of max-CLF control. As key contribution of this thesis, to solve this issue, we endow the storage functions with a finite set of state-dependent parameters. Max-type 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 closed-loop trajectory, thus removing the impediment of centralized, off-line LF synthesis associated with fixed-parameter SFs. It is shown that parameterized-SF synthesis conditions can be efficiently exploited to obtain a scalable, trajectory-dependent control scheme that relies on non-iterative neighbor-to-neighbor communication only. For input-affine network dynamics and quadratic storage functions, the procedure can be implemented by solving a single semi-definite program per node and sampling instant, in a receding horizon fashion. Moreover, by interpolating a collection of so-obtained input trajectories, a low-complexity explicit control law for linear, time-invariant systems is obtained that extends the trajectory-specific convergence property to a much stronger guarantee of closed-loop asymptotic stability for a particular set of initial conditions. Finally, we consider the application of max-CLF and parameterized SFs for real-time balancing in multimachine electrical power networks. Given that generators are operated by competitive, profit-driven 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 Lyapunov-based predictive control and parameterized storage function algorithms is evaluated by simulating them in closed-loop with the 7-machine CIGRÉ benchmark system. The thesis concludes by summarizing the main contributions, followed by ideas for future research

Online Adversarial Stabilization of Unknown Networked Systems

We investigate the problem of stabilizing an unknown networked linear system under communication constraints and adversarial disturbances. We propose the first provably stabilizing algorithm for the problem. The algorithm uses a distributed version of nested convex body chasing to maintain a consistent estimate of the network dynamics and applies system level synthesis to determine a distributed controller based on this estimated model. Our approach avoids the need for system identification and accommodates a broad class of communication delay while being fully distributed and scaling favorably with the number of network subsystems

Control and identification in activated sludge processes = Regeling en identifikatie in aktief-slib processen

This thesis is about control and identification in activated sludge processes (ASP's). The chapters in this thesis are divided in two parts. Part I deals with the development of the best feasible, close-to-optimal adaptive receding horizon optimal controller (RHOC) for N-removal in a continuously mixed alternating activated sludge process reactor. Subsequently this controller and the most common existing controllers are mutually compared by means of simulations. In addition the application of the close-to-optimal RHOC controller to a system of two hydraulically connected alternating reactors is simulated for a range of plant designs within this class. In this way the combination of design and operation is optimized. Part II concerns identification on the basis of DO-measurements and respirometry. First the DO-dynamics in a continuously mixed ASP reactor are identified, including the non-linear relation between k L a and q air . Subsequenly the dynamics of a (DO-sensor based) continuous flow respirometer are identified by exciting its dynamics.In chapter 1 the principles of the N-removing ASP are shortly explained. The new problem of total-N removal is discussed. The general features of the ASP control problem are listed: disturbance attenuation, storm events, process uncertainty and variation, multiple time-scales. Special attention is paid to the potential of RHOC. The literature with respect to operational aspects of N-removal as well as the use of DO-sensors and respirometers in ASP operation is coarsely reviewed. It is argued that the anoxic periods approach for N-removal offers two principle advantages over the anoxic zones approach: excitation of dynamics and no need for internal recirculation. Some problems in the field are indicated. With respect to DO-sensors it is illustrated that the challenges today are in the field of extracting not only DO but also additional information from its readings. All experiments in this thesis have been carried out at a pilot scale ASP. A description of this pilot plant is given in chapter 1. The chapter ends with the formulation of research objectives and the thesis outline.Chapters 2 till 5 present the design procedure for the adaptive RHOC for control of NH 4 and NO x , though not exactly chronologically. The first step is presented in chapter 4, it concerns application of optimal control to the N-removal part of the generally accepted Activated Sludge Model no. 1. From this optimal control study it occurs that alternating nitrification/denitrification, as opposed to simultaneous nitrification/denitrification, may be optimal indeed. This, together with the risk of sludge bulking at limiting DO-values, justifies the limitation to alternating process operation. To implement an optimal control strategy on-line the receding horizon principle is needed, leading to RHOC. RHOC uses an internal process model for short term predictions. Hence a computationally efficient process model is required. Such a model is developed in chapter 2 by capturing the slower process dynamics in time-varying model parameters. It is taken into account that the model structure must be suited for recursive identification of the time-varying model parameters from the measurements.RHOC, like any model predictive controller, computes the current controls on the basis of model predictions upto horizon H . Hence the sum of squared 1, 2, .., H -step ahead prediction errors is a natural identification criterion. In chapter 2 this idea is postulated and applied to NH 4 /NO x measurements collected from the pilot scale ASP described in chapter 1. H appears to affect the parameter estimates significantly, supporting the idea that use of this new identification criterion will improve MPC performance in general.In chapter 4 RHOC with this simple model is applied to the pilot plant's alternating reactor. The controller successfully passed several tests, but it also appeared that the performance of this controller is suboptimal due to inaccurate model predictions. This was to be expected, as the simplicity of the N-removal model in chapter 2 has been achieved by capturing the slower process dynamics in the model parameters, while in this stage they are not recursively estimated.The results of chapter 4 illustrate that recursive identification of (some of the) model parameters is required to keep the model uptodate. Chapter 3 presents the algorithm for recursive identification of those model parameters. The Kalman filter is used, because it has the attractive feature that the filter gain accompanying non-identifiable parameters ( e.g. the nitrification rate during anoxic periods) increases linearly in time. It is proven that this increase of the filter gain will not cause instability during normal process operation. The method performs excellently on real data.Chapter 5 concerns adaptive RHOC of N-removal in alternating ASP reactors, being the combination of the recursively identified model in chapter 3 and the RHOC controller in chapter 4. Although stability of the nonlinear RHOC feedback controller has not been proven, not to mention its combination with recursive identification, only one source of instability was encountered in many experiments. This is the scenario in which NH 4 dominates the objective functional, its setpoint is zero and the estimated rate of nitrification has become negative for whatever reason. In that case the controller will keep aeration off to prevent the predicted production of NH 4 , as a consequence no new information to update the estimated nitrification rate is obtained and the deadlock is there. Obviously this scenario is easy to prevent and does not occur under normal operating conditions.In chapter 4 the unusual observation is done that the RHOC performance is nearly invariant to its prediction horizon. This triggered a study on the cause of this phenomena and an effort to generalize the results as far as possible, the results are presented in chapter 6. It has led to the derivation of an l 1 -norm optimal state feedback controller for 2-dimensional linear time invariant systems with decoupled dynamics and a single control input.In chapter 7 the close-to-optimal adaptive RHOC of chapter 5 and three existing control strategies (timers, NH 4 -bounds based and ORP, Oxidation Reduction Potential, based) for N-removal in continuously mixed alternating reactors are compared by means of simulation. The simulations are carried out in SIMBA TM, a commercially available application within the MATLAB/SIMULINK TMenvironment, based on the Activated Sludge Model no. 1. Drawback of simulations is that the dynamics of both the sensors and the process need to be modelled. And even the best model of the ASP is nothing but a poor resemblance of the real process. However, a fair experimental comparison of multiple controllers is impossible, not only for financial reasons. Simultaneous experimental testing would require the availability of multiple identical plants in parallel. Sequential testing on one plant would disrupt the results by changes in process conditions and influent, disabling a mutual comparison. Hence simulation is the best way to compare different control-schemes. It appears that three totally different controllers (timers, NH 4 -bounds based and adaptive RHOC) can achieve a more or less equal performance, if tuned optimally. Adaptive RHOC turns out to be superior in terms of sensitivity to suboptimal tunings. The timers approach is attractive for its simplicity, but very sensitive to suboptimal tuning.Chapter 8 describes a simulation study with the scope to optimise the plant design and operation strategy of alternating activated sludge processes for N-removal with two hydraulically connected reactors. The methodology is to simulate the application of RHOC to a range of different plant designs within this class of systems. The RHOC algorithm is obtained by reformulating the controller of chapter 4 for a 2-reactors system. It appears that in the optimal process design the two reactors are placed in series, the first reactor is about four times as large as the second one. A conceptually simple feedback controller straightforwardly implements the improved operation strategy. The results of this chapter strongly advocate the simulation of optimal control applied to complex process models. The results are unexpected and indicate a significant outperformance of the current operation strategy. This kind of simulation studies at least serves as an ideas generator.Chapter 9 presents a grey-box modelling approach for the identification of the nonlinear DO dynamics. Herein, singular value decomposition of the locally available Jacobian matrix, or equivalently eigenvalue decomposition of the parameter covariance matrix, as well as parameter transformation are essential techniques. The use of respiration rate measurements greatly simplifies the modelling procedure. The approach is amongst others capable of identifying the non-linear function k L a ( q air ), i.e. the relationship between k L a and the aeration input signal q air . This is especially valuable in experimental identification of the relationship between k L a ( q air ) and the design of (newly developed) aeration equipment, the use of specific carrier materials in aerated reactors, or the presence of certain detergents. After all a higher k L a at a given q air results in a higher efficiency of energy usage for aeration, and hence identification of k L a ( q air ) for newly developed equipment can yield important sales arguments.Chapters 10 and 11 both deal with excitation of the respiration chamber dynamics in a continuous flow respirometer with the objective to extract additional information from its dissolved oxygen (DO) sensor readings. Chapter 10 is an effort to improve the accuracy of the BOD st -estimation technique developed by Spanjers et al . (1994). Contrary to expectation, the estimates still suffer from unacceptable inaccuracy due to large parameter correlation. However, a slight modification in the measurement strategy is proposed which is expected to enable more accurate estimation. The results of experiments with this modified measurement strategy are reported in chapter 11. The estimation results convincingly discourage further efforts to identify sludge kinetics and BOD st from this type of experiments.The two other objectives of chapter 11 are the identification of the DO-sensor dynamics and the dilution rate in a continuous flow respirometer by excitation of the respiration chamber dynamics. Two separate simple procedures are presented. Both procedures consist of on-purpose in-sensor experiments succeeded by an ordinary least squares estimation step. The feasibility of both procedures is verified in experiments with activated sludge, fed with municipal wastewater. Large experimental data sets are presented, which strongly advocate the on-line incorporation of both procedures in the everyday operation of the respirometer.In chapter 12 those conclusions drawn in the individual chapters which are of direct relevance to practitioners are summarized. Moreover some remaining ideas, which I believe are novel and likely to be succesfull, are shortly expounded in chapter 12 as well. The ideas concern: 1) Meeting N-total effluent standards by permitting elevated effluent NH 4 ; 2) Control explicitly aiming at meeting yearly averaged effluent standards; 3) The use of pH-measurements for continuous on-line tuning of timers in a timer-based operation strategy for alternating N-removal in a continuously mixed ASP reactor.</p

Robust Model Based Control of Constrained Systems.

This dissertation is concerned with control of systems subject to input and state constraints. Model Predictive Control (MPC) is one promising control technique that is capable of dealing with constraints. Its flexible formulation also provides mechanisms to tune the closed loop system for desired performance. However, due to computational complexity and its dependency on accurate models of the system, the MPC applications for systems with fast dynamics or with model uncertainties are not wide spread. The focus of this dissertation is to develop methodologies and tools that can enhance the computational efficiency and address robustness issues of constrained dynamic systems. The core contribution of this dissertation is that it provides a computational efficient MPC solver, referred to as InPA-SQP (Integrated Perturbation Analysis and Sequential Quadratic Programming). The main results include four major components. First, a neighboring extremal control method is proposed for discrete-time optimal control problems subject to a general class of inequality constraints. A closed form solution for the neighboring extremal (NE) control is provided and a sufficient condition for existence of the neighboring extremal solution is specified. Second, the NE method is integrated with sequential quadratic programming that leads to InPA-SQP. Third, a robust control method is introduced for linear discrete-time systems subject to mixed input-state constraints. Unlike conventional MPC, the method does not require repeatedly solving an optimization problem online while guarantees states convergence to a minimal invariant set. Fourth, it is shown that if the dynamics of disturbances are incorporated, the attractor set associated with the proposed constrained robust control methods can be considerably smaller, leading to a much less conservative design. Applications of the InPA-SQP and proposed constrained robust control constitute the other key element of the study. The InPA-SQP is employed in two experimental applications: one for voltage regulation of a DC/DC converter and another for path following of a model ship. Both applications show effectiveness of the method in terms of computation and constraints handling. These applications not only serve as validation platforms but also motivate new research topics for further investigation.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77854/1/ghaemi_1.pd

Secure Multi-Party Computation In Practice

Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC provides strong privacy guarantees, but practical adoption requires high-quality application design, software development, and resource management. This dissertation aims to identify and reduce barriers to practical deployment of MPC applications. First, the dissertation evaluates the design, capabilities, and usability of eleven state-of-the-art MPC software frameworks. These frameworks are essential for prototyping MPC applications, but their qualities vary widely; the survey provides insight into their current abilities and limitations. A comprehensive online repository augments the survey, including complete build environments, sample programs, and additional documentation for each framework. Second, the dissertation applies these lessons in two practical applications of MPC. The first addresses algorithms for assessing stability in financial networks, traditionally designed in a full-information model with a central regulator or data aggregator. This case study describes principles to transform two such algorithms into data-oblivious versions and benchmark their execution under MPC using three frameworks. The second aims to enable unlinkability of payments made with blockchain-based cryptocurrencies. This study uses MPC in conjunction with other privacy techniques to achieve unlinkability in payment channels. Together, these studies illuminate the limitations of existing software, develop guidelines for transforming non-private algorithms into versions suitable for execution under MPC, and illustrate the current practical feasibility of MPC as a solution to a wide variety of applications

Model Predictive Control Applications to Spacecraft Rendezvous and Small Bodies Exploration

The overarching goal of this thesis is the design of model predictive control algorithms for spacecraft proximity operations. These include, but it is not limited to, spacecraft rendezvous, hovering phases or orbiting in the vicinity of small bodies. The main motivation behind this research is the increasing demand of autonomy, understood as the spacecraft capability to compute its own control plan, in current and future space operations. This push for autonomy is fostered by the recent introduction of disruptive technologies changing the traditional concept of space exploration and exploitation. The development of miniaturized satellite platforms and the drastic cost reduction in orbital access have boosted space activity to record levels. In the near future, it is envisioned that numerous artificial objects will simultaneously operate across the Solar System. In that context, human operators will be overwhelmed in the task of tracking and commanding each spacecraft in real time. As a consequence, developing intelligent and robust autonomous systems has been identified by several space agencies as a cornerstone technology. Inspired by the previous facts, this work presents novel controllers to tackle several scenarios related to spacecraft proximity operations. Mastering proximity operations enables a wide variety of space missions such as active debris removal, astronauts transportation, flight-formation applications, space stations resupply and the in-situ exploration of small bodies. Future applications may also include satellite inspection and servicing. This thesis has focused on four scenarios: six-degrees of freedom spacecraft rendezvous; near-rectilinear halo orbits rendezvous; the hovering phase; orbit-attitude station-keeping in the vicinity of a small body. The first problem aims to demonstrate rendezvous capabilities for a lightweight satellite with few thrusters and a reaction wheels array. For near-rectilinear halo orbits rendezvous, the goal is to achieve higher levels of constraints satisfaction than with a stateof- the-art predictive controller. In the hovering phase, the objective is to augment the control accuracy and computational efficiency of a recent global stable controller. The small body exploration aims to demonstrate the positive impact of model-learning in the control accuracy. Although based on model predictive control, the specific approach for each scenario differs. In six-degrees of freedom rendezvous, the attitude flatness property and the transition matrix for Keplerian-based relative are used to obtain a non-linear program. Then, the control loop is closed by linearizing the system around the previous solution. For near-rectilinear halo orbits rendezvous, the constraints are assured to be satisfied in the probabilistic sense by a chance-constrained approach. The disturbances statistical properties are estimated on-line. For the hovering phase problem, an aperiodic event-based predictive controller is designed. It uses a set of trigger rules, defined using reachability concepts, deciding when to execute a single-impulse control. In the small body exploration scenario, a novel learning-based model predictive controller is developed. This works by integrating unscented Kalman filtering and model predictive control. By doing so, the initially unknown small body inhomogeneous gravity field is estimated over time which augments the model predictive control accuracy.El objeto de esta tesis es el dise˜no de algoritmos de control predictivo basado en modelo para operaciones de veh´ıculos espaciales en proximidad. Esto incluye, pero no se limita, a la maniobra de rendezvous, las fases de hovering u orbitar alrededor de cuerpos menores. Esta tesis est´a motivada por la creciente demanda en la autonom´ıa, entendida como la capacidad de un veh´ıculo para calcular su propio plan de control, de las actuales y futuras misiones espaciales. Este inter´es en incrementar la autonom´ıa est´a relacionado con las actuales tecnolog´ıas disruptivas que est´an cambiando el concepto tradicional de exploraci´on y explotaci´on espacial. Estas son el desarrollo de plataformas satelitales miniaturizadas y la dr´astica reducci´on de los costes de puesta en ´orbita. Dichas tecnolog´ıas han impulsado la actividad espacial a niveles de record. En un futuro cercano, se prev´e que un gran n´umero de objetos artificiales operen de manera simult´anea a lo largo del Sistema Solar. Bajo dicho escenario, los operadores terrestres se ver´an desbordados en la tarea de monitorizar y controlar cada sat´elite en tiempo real. Es por ello que el desarrollo de sistemas aut´onomos inteligentes y robustos es considerado una tecnolog´ıa fundamental por diversas agencias espaciales. Debido a lo anterior, este trabajo presenta nuevos resultados en el control de operaciones de veh´ıculos espaciales en proximidad. Dominar dichas operaciones permite llevar a cabo una gran variedad de misiones espaciales como la retirada de basura espacial, transferir astronautas, aplicaciones de vuelo en formaci´on, reabastecer estaciones espaciales y la exploraci ´on de cuerpos menores. Futuras aplicaciones podr´ıan incluir operaciones de inspecci´on y mantenimiento de sat´elites. Esta tesis se centra en cuatro escenarios: rendezvous de sat´elites con seis grados de libertad; rendezvous en ´orbitas halo cuasi-rectil´ıneas; la fase de hovering; el mantenimiento de ´orbita y actitud en las inmendiaciones de un cuerpo menor. El primer caso trata de proveer capacidades de rendezvous para un sat´elite ligero con pocos propulsores y un conjunto de ruedas de reacci´on. Para el rendezvous en ´orbitas halo cuasi-rectil´ıneas, se intenta aumentar el grado de cumplimiento de restricciones con respecto a un controlador predictivo actual. Para la fase de hovering, se mejora la precisi´on y eficiencia computacional de un controlador globalmente estable. En la exploraci´on de un cuerpo menor, se pretende demostrar el mayor grado de precisi´on que se obtiene al aprender el modelo. Siendo la base el control predictivo basado en modelo, el enfoque espec´ıfico difiere para cada escenario. En el rendezvous con seis grados de libertad, se obtiene un programa no-lineal con el uso de la propiedad flatness de la actitud y la matriz de transici´on del movimiento relativo Kepleriano. El bucle de control se cierra linealizando en torno a la soluci´on anterior. Para el rendezvous en ´orbitas halo cuasi-rectil´ıneas, el cumplimiento de restricciones se garantiza probabil´ısticamente mediante la t´ecnica chance-constrained. Las propiedades estad´ısticas de las perturbaciones son estimadas on-line. En la fase de hovering, se usa el control predictivo basado en eventos. Ello consiste en unas reglas de activaci´on, definidas con conceptos de accesibilidad, que deciden la ejecuci´on de un ´unico impulso de control. En la exploraci´on de cuerpos menores, se desarrolla un controlador predictivo basado en el aprendizaje del modelo. Funciona integrando un filtro de Kalman con control predictivo basado en modelo. Con ello, se consigue estimar las inomogeneidades del campo gravitario lo que repercute en una mayor precisi´on del controlador predictivo basado en modelo

Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand