Control of a Solar Energy Systems

8th IFAC Symposium on Advanced Control of Chemical ProcessesThe International Federation of Automatic Control Singapore, July 10-13This work deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems. While in other power generating processes, the main source of energy can be manipulated, in solar energy systems, the main source of power which is solar radiation cannot be manipulated and furthermore it changes in a seasonal and on a daily base acting as a disturbance when considering it from a control point of view. Solar plants have all the characteristics needed for using industrial electronics and advanced control strategies able to cope with changing dynamics, nonlinearities and uncertainties.Ministerio de Ciencia e Innovación PI2008-05818Ministerio de Ciencia e Innovación DPI2010-21589-C05-01/04Junta de Andalucía P07-TEP-0272

Control of Solar Power Systems: a survey

9th International Symposium on Dynamics and Controlof Process Systems (DYCOPS 2010)Leuven, Belgium, July 5-7, 20109This paper deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems.Ministerio de Ciencia y Tecnología DPI2008-05818Ministerio de Ciencia y Tecnología DPI2007-66718-C04-04Junta de Andalucía P07-TEP-0272

Application of Robust Model Predictive Control to a Renewable Hydrogen-based Microgrid

In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i. i. e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant-replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems

Incentivizing Truth-Telling in MPC-based Load Frequency Control

We present a mechanism for socially efficient implementation of model predictive control (MPC) algorithms for load frequency control (LFC) in the presence of self-interested power generators. Specifically, we consider a situation in which the system operator seeks to implement an MPC-based LFC for aggregated social cost minimization, but necessary information such as individual generators' cost functions is privately owned. Without appropriate monetary compensation mechanisms that incentivize truth-telling, self-interested market participants may be inclined to misreport their private parameters in an effort to maximize their own profits, which may result in a loss of social welfare. The main challenge in our framework arises from the fact that every participant's strategy at any time affects the future state of other participants; the consequences of such dynamic coupling has not been fully addressed in the literature on online mechanism design. We propose a class of real-time monetary compensation schemes that incentivize market participants to report their private parameters truthfully at every time step, which enables the system operator to implement MPC-based LFC in a socially optimal manner

Model predictive control for power system frequency control taking into account imbalance uncertainty

© IFAC.Model predictive control (MPC) is investigated as a control method for frequency control of power systems which are exposed to increasing wind power penetration. For such power systems, the unpredicted power imbalance can be assumed to be dominated by the fluctuations in produced wind power. An MPC is designed for controlling the frequency of wind-penetrated power systems, which uses the knowledge of the estimated worst-case power imbalance to make the MPC more robust. This is done by considering three different disturbances in the MPC: one towards the positive worst-case, one towards the negative worst-case, and one neutral in the middle. The robustified MPC is designed so that it finds an input which makes sure that the constraints of the system are fulfilled in case of all three disturbances. Through simulations on a network with concentrated wind power, it is shown that in certain cases where the state-of-the-art frequency control (PI control) and nominal MPC violate the system constraints, the robustified MPC fulfills them due to the inclusion of the worst-case estimates of the power imbalance

On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RMinisterio de Economía y Competitividad DPI2013-482443-C2-1-