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

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-

Stock Management in Hospital Pharmacy using Chance-Constrained Model Predictive Control

One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.Junta de Andalucía P12-TIC-240

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Multicriteria optimal operation of a microgrid considering risk analysis, renewable resources, and model predictive control

This paper proposes an optimal power dispatch by taking into account risk management and renewable resources. In particular, it examines how control engineering and risk management techniques can be applied in the field of power systems through their use in the design of risk-based model predictive controllers. To this end, this paper proposes a two-layer control scheme for microgrid management where both levels are based onmodel predictive control (MPC): the higher level is devoted to risk management while the lower layer is dedicated to power dispatching. In particular, the high-level controller is based on a risk-based approach where potential risks have been identified and evaluated. Mitigation actions are the decision variables to be optimized to reduce the consequences of risks and costs. The MPC-based algorithm decides the appropriate frequency of mitigation actions such as changes in references, constraints, and insurance contracting, by relying on a model that includes integer variables, identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. On the other hand, the low-level controller drives the plant to suitable values to satisfy demands. A series of simulations on a nonlinearmodel of a real laboratory-scale power plant located in the facilities of the University of Seville are conducted under varying conditions to demonstrate the effectiveness of the algorithm when risks are explicitly considered

On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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.Peer ReviewedPostprint (author's final draft

Stochastic Model Predictive Control Approaches applied to Drinking Water Networks

Control of drinking water networks is an arduous task given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances due to the stochastic water demand/consumption, and optimize operational costs, this paper proposes three stochastic model predictive control (MPC) approaches, namely: chance-constrained MPC, tree-based MPC, and multiple scenarios MPC. A comparative assessment of these approaches is performed when they are applied to real case studies, specifically, a sector and an aggregate version of the Barcelona drinking water network in Spain