Simira Papadopoulou

Constrained switching stabilization of a dc-dc boost converter using piecewise-linear Lyapunov functions

This paper describes a new methodology for designing robust and efficient control laws for power converters. In addition to guaranteed closed-loop robust stability, the proposed design is capable of addressing a number of further key issues, i.e. low complexity of the implementation, accurate nonlinear dynamics incorporation, nonconservative handling of hard state and control constraints, and robustness to supply voltage variations and setpoint changes. The control design is of a set-theoretic nature. The iterative algorithms used for controller generation are based on the ray-gridding approach, that generates piecewise-linear Lyapunov functions and corresponding controlled invariant polytopes, induced by systematic conic decompositions of the state-space and state-dependent switching control actions. The proposed technique is evaluated on a boost converter case study. Simulation results in the MATLAB/SIMULINK™ environment are reported. The bifurcation behaviour of the system is further studied by numerical and analytical nonlinear stability analysis.

A power grand composite curves approach for analysis and adaptive operation of renewable energy smart grids

This work proposes the use of the power grand composite curves (PGCC) method to identify energy recovery targets in renewable energy smart grids and to adaptively adjust their operation in short-term energy requirements through appropriately selected power management strategies (PMS). A PMS is the sequence of decisions offering efficient utilization of resources and equipment to meet specific targets. The aim is to identify the appropriate PMS within recurrent subsequent time intervals that efficiently serves the desired operating goals in view of operating variability. This is approached by predicting the PGCC for a time horizon extending into the future. Subsequently, the PGCC is appropriately shifted to set a target for the minimum energy inventory needed by the end of the current interval for which decisions about the system operation are sought in order to satisfy the system operating goals. The target energy inventory is guaranteed in the current interval by selecting the PMS that best matches the identified target. A formal mathematical framework is presented, associating Pinch analysis with PMS within a generic model considering numerous structural and temporal grid interactions. The proposed method is implemented on an actual hybrid smart grid considering multiple RES-based energy generation and storage options.

Modeling and Experimental Validation of a PEM Fuel Cell System

Abstract This work presents a detailed dynamic model and a model validation study using real data from a Hydrogen Fuel Cell Testing Unit (HFCTU). A parameter estimation technique is employed for the determination of key model parameters and the validation of the overall system behavior is carried out by comparing experimental and simulation results. Data illustrate the transient response of the system during load changes. The model is oriented towards process optimization and control and relies on mass balances and electrochemical equations implemented in the gPROMS™ software environment.

Robust constrained stabilization of a boost DC-DC converter with Lyapunov-based control and piecewise-linear Lyapunov functions

In this paper we describe a new methodology for designing robust and efficient state-feedback control laws for a switched-mode boost DC-DC power converter. The proposed design is adopting the so-called Lyapunov-based control law and attempts to solve the robust constrained stabilization problem under large parameter variations. With the methodology proposed static or switching state-feedback control laws are generated so that a number of further key issues are addressed, i.e. low complexity of the implementation, accurate nonlinear dynamics incorporation, nonconservative handling of hard state and control constraints, robustness to supply voltage variations, set-point and load changes. The control design procedure is based on the generation of controlled invariant polytopes (safety domains) using piecewise-linear Lyapunov functions. The proposed technique is numerically evaluated using the exact switched model of the converter.

Multi-Parametric Model Predictive Control of an Automated Integrated Fuel Cell Testing Unit

Abstract The aim of this work is to present a framework for the design of a constrained multiparametric Model Predictive Control (mp-MPC) strategy for a fully automated integrated Hydrogen Fuel Cell Testing Unit (HFCTU) at CERTH. The underlying process is described by an experimentally validated dynamic model which provided the basis for the control framework. This model was used to derive reduced order state space models based on which an explicit/multi-parametric MPC controller was designed in order to satisfy the load demand, avoid starvation and maintain the temperature at a nominal point. The derived controller was tested offline on several operating conditions and showed an excellent transient and steady-state performance.

Energy management in a stand-alone power system for the production of electrical energy with long term hydrogen storage

This paper deals with the importance of an efficient energy (or power) management strategy (EMS) on an existing stand-alone power system that uses renewable energy sources for the production of electrical energy. Due to the intermittent nature of the renewables, part of this energy is used to split the water for the production of hydrogen, which is stored and used later for the production of energy in a PEM fuel cell, in case of high energy demands. The energy management algorithms aim at the reliable and effective control of the energy flow that is basically used to meet the load requirements of the autonomous system. The developed simulated algorithms were compared to each other in order to determine the most efficient strategy as far as hydrogen production and autonomy are of concern. Parametric sensitivity was also a major issue which was studied extensively. All the results and outcomes of such an analysis are considered as the basis for the optimization and control study of similar stand-alone power systems.

Design of an energy decision framework for an autonomous RES-enabled smart-grid network

The objective of this work is to develop a reliable, sustainable and adaptive architecture to implement the energy decisions in a complex networked ecosystem for power production by Renewable Energy Sources (RES). To achieve this objective, the modelling of the involved systems is discussed in order to identify the requirements for energy management in dynamic and distributed environments. Also this work investigates the impact of emerging Internet of Things (IoT) architecture on an energy production networked systems in a smart grid environment. Furthermore, the relation of the developed IoT enabled architecture to the energy management decisions is overviewed combined with a Machine2Machine (M2M) approach with implementation aspects relying on industrial-grade software. Preliminary results are presented that show the response of the proposed scheme to a smart grid system with RES and hydrogen production, usage and storage.

Optimal switching Lyapunov-based control of a boost DC-DC converter

A new methodology for designing robust and efficient state-feedback control laws for a switched-mode boost DC-DC power converter has been recently proposed. This approach has adopted the so-called stabilizing or Lyapunov-based control paradigm which is well-known in the area of energy-based control of DC-DC converters, whereby the control law takes a state-feedback form parameterized by a positive scalar γ. Extension to state-dependent switching state-feedback control laws has been proposed, where the switching surfaces are parameterized by a number of positive scalars γ.i. In this paper this methodology is revisited by considering the problem of designing optimal switching state-feedback control laws, i.e. finding the optimal control parameters γ.i corresponding to the optimal position of the switching surfaces. This permits minimization of the number of switchings required for achieving an optimal performance and hence reduced complexity of the control law. Systematic derivation of gradient information to apply gradient-descent algorithms is provided. The proposed technique is numerically evaluated using the exact switched model of the converter.

Development of a Nonlinear Model Predictive Control Framework for a PEM Fuel Cell System

Abstract The aim of this work is to demonstrate that nonlinear model based predictive control (NMPC) is a suitable approach for the efficient and optimal operation of a PEM fuel cell system. Thus, a NMPC-based framework is developed structured on a simultaneous full-discretization optimization method which involves the use of orthogonal collocation on finite elements and represents the optimal control problem as a nonlinear programming problem (NLP). The purpose of the framework is to control the system behaviour under a range of operating conditions. Also the performance of the overall scheme is fine-tuned so that the resulting controller can be deployed at an on-line industrial automation environment. Results indicate that the developed NMPC framework exhibits excellent performance characteristics in terms of both computational requirements and convergence rates between successive iterations. Furthermore it is illustrated that the system can follow successfully set point changes of the load demand and compensates errors occurring from sudden disturbances in operating conditions.

Optimal switching Lyapunov-based control of power electronic converters

This paper presents new ideas and insights towards a novel optimal control approach for power electronic converters. The so-called stabilizing or Lyapunov-based control paradigm is adopted, which is well known in the area of energy-based control of power electronic converters, in which the control law takes a nonlinear state-feedback form parameterized by a positive scalar λ. The first contribution is the extension to an optimal Lyapunov-based control paradigm involving the specification of the optimal value for the parameter λ in a typical optimal control setting. The second contribution is the extension to more flexible optimal switching-gain control laws, where the optimal switching surfaces are parameterized by a number of positive scalars λj. Systematic derivation of gradient information to apply gradient-descent algorithms is provided. The proposed techniques are numerically evaluated using the exact switched model of a DC-DC boost converter. Copyright