Power Management for Energy Systems

The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences

Model Predictive Control for Enhancing Wind Farms Participation in Ancillary Services

The increasing penetration of Renewable Energy (RE) systems into the electric grid is creating new challenges into the power system. The unpredictable and variable nature of renewable power generation is increasing the imbalances between generation and demand. For this reason, wind farms, which are the main source of RE in Europe, are required nowadays to support the grid, providing services of voltage and frequency regulation. To be able to increase their power production during a frequency event, Wind Power Plants (WPPs) need to work below their maximum generation capacity, keeping an additional amount of power, called power reserve, that can be injected into the grid when required. The power reserve of a wind farm strongly depends on the interaction among the wind turbines. The wake effect produced by the upstreams turbines affects the wind condition that each turbine faces and reduces their maximum available power. This study aims to present the effects of different distribution of the Wind turbines (WTs) individual power contribution on the power reserve. Three control strategies, based on Model Predictive Control (MPC), are tested on a fifteen turbines wind farm under different wind conditions. Simulation results show that, in almost all cases, prioritizing the power contribution of the most downstream turbines and deloading the upstream ones, leads to a maximization of the wind farm power reserve. Furthermore, an additional MPC strategy aiming to combine active and reactive power control, for providing both frequency and voltage regulation at the Point of Common Coupling (PCC), is presented. The advantage of a combined active and reactive power control is the possibility of improve the voltage support capability of the WPPs, by controlling the active power set-points. The MPC is also tested on a fifteen turbines wind farm, in order to validate the performances of the controller while solving the multi-objective problem. The ability of the controller to handle simultaneously the different requirements is proven

Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines

Wind turbine (WT) controllers are often geared towards maximum power extraction, while suitable operating constraints should be guaranteed such that WT components are protected from failures. Control strategies can be also devised to reduce the generated power, for instance to track a power reference provided by the grid operator. They are called down-regulation strategies and allow to balance power generation and grid loads, as well as to provide ancillary grid services, such as frequency regulation. Although this balance is limited by the wind availability and grid demand, the quality of wind energy can be improved by introducing down-regulation strategies that make use of the kinetic energy of the turbine dynamics. This paper shows how the kinetic energy in the rotating components of turbines can be used as an additional degree-of-freedom by different down-regulation strategies. In particular we explore the power tracking problem based on convex model predictive control (MPC) at a single wind turbine. The use of MPC allows us to introduce a further constraint that guarantees flow stability and avoids stall conditions. Simulation results are used to illustrate the performance of the developed down-regulation strategies. Notably, by maximizing rotor speeds, and thus kinetic energy, the turbine can still temporarily guarantee tracking of a given power reference even when occasional saturation of the available wind power occurs. In the study case we proved that our approach can guarantee power tracking in saturated conditions for 10 times longer than with traditional down-regulation strategies.Comment: 6 pages, 2 figures, 61st IEEE Conference on Decision and Control 202

Health-aware model predictive control of wind turbines using fatigue prognosis

This is the peer reviewed version of the following article: Sánchez, H. E., Escobet, T., Puig, V., Fogh, P. Health-aware model predictive control of wind turbines using fatigue prognosis. "International journal of adaptive control and signal processing", 1 Abril 2018, vol. 32, núm. 4, p. 614-627, which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/acs.2784. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived VersionsWind turbine components are subject to considerable fatigue because of extreme environmental conditions to which they are exposed, especially those located offshore. Wind turbine blades are under significant gravitational, inertial, and aerodynamic loads, which cause their fatigue and degradation during the wind turbine operational life. A fatigue problem is often present at the blade root because of the considerable bending moments applied to this zone. Interest in the integration of control with fatigue load minimization has increased in recent years. This paper investigates the fatigue assessment using a rainflow counting algorithm and the blade root moment information coming from the sensor available in a high-fidelity simulator of a utility-scale wind turbine. Then, the integration of the fatigue-based system health management module with control is proposed. This provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components' life and energy production. In particular, this paper explores the integration of model predictive control with the fatigue-based prognosis approach to minimize the damage of wind turbine components (the blades). A control-oriented model of the fatigue based on the rainflow counting algorithm is proposed to obtain online information of the blades' accumulated damage that can be integrated with model predictive control. Then, the controller objective function is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested in a well-known wind turbine benchmark.Peer Reviewe

ADV preview based nonlinear predictive control for maximizing power generation of a tidal turbine with hydrostatic transmission

As the development of tidal turbines attracts more and more attention in recent years, reliable design and efficient control of tidal turbines are becoming increasingly important. However, the majority of existing tidal turbines still utilize traditional fixed ratio geared transmissions and the associated control designs focus on simple feedback controllers that use measurements or possibly estimates of the turbine itself or current local tidal profile. Therefore, the measurement and control are inevitably affected by the inherent delay with respect to the current tidal speeds. This paper proposes a novel tidal turbine with continuously variable speed hydrostatic transmissions and a nonlinear predictive controller that uses short-term predictions of the approaching tidal speed field to enhance the maximum tidal power generations when the tidal speed is below the rated value. The controller is designed based on an offline finite-horizon continuous time minimization of a cost function, and an integral action is incorporated into the control loop to increase the robustness against parameter variations and uncertainties. A smooth second order sliding mode observer is also designed for parameter estimations in the control loop. A 150 kW tidal turbine with hydrostatic transmission is designed and implemented. The results demonstrate that the averaged generator power increases by 6.76% with this preview based nonlinear predictive controller compared with a classical non-predictive controller