Hybrid Modeling of a Solar Cooling System

Pasamontes, M.; Álvarez, José Domingo; Guzmán, José Luís; Berenguel, Manuel

doi:10.3182/20090916-3-es-3003.00006

Cited by 23 publications

(13 citation statements)

References 7 publications

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“…The system has two main parts, composed of a solar plant and a set of subsystems. The solar plant is part of CIESOL located at the University of Almería in the southeast of Spain . The set of subsystems are models that represent energy consuming systems, in this case, representing HVAC systems .…”

Section: Simulation Scenario and Analysismentioning

confidence: 99%

“…The second one includes the valves VS1–4, which divert part of the flow in the secondary circuit to each HVAC system, and the pump B3.1/2, which is the pump that regulates the flow of the primary circuit and will correspond to actual measurements from the real solar plant. Physical and operation details of this system can be found in .…”

Section: Simulation Scenario and Analysismentioning

confidence: 99%

“…In order to probe the effectiveness of the proposed control strategy, several experiments were conducted with a real solar plant that is part of Centro de Investigación de Energía Solar (CIESOL) located at the University of Almería (Spain) , which has been adapted in this work to analyze the main properties of the proposed DMPC decomposition. The tests were performed using the hardware‐in‐the‐loop methodology: the real solar plant was connected to a load simulator that represents the dynamics of the process, in this case composed of a set of n HVAC systems, each one responsible for the temperature control of a physically separate building.…”

Section: Introductionmentioning

confidence: 99%

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Distributed MPC for resource‐constrained control systems

Scherer

Camponogara

Normey‐Rico

et al. 2014

Optim Control Appl Methods

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This article presents a distributed model predictive control methodology to manage energy resources for a set of consumer subsystems. The objective of the controller is to optimally distribute the allowable energy to the subsystems. The proposed methodology yields a distributed solution that converges to the optimum that would be obtained by a centralized controller. This optimal performance is achieved by expressing the problem in terms of slack variables and the global coupling constraint as a set of local subsystem constraints, thereby favoring the application of distributed model predictive control. Hardware-in-the-loop experiments with an air-conditioning thermal solar plant are performed to show the good performance of the proposed distributed controller.This section presents the MPC formulation for resource-constrained systems, followed by a compact formulation and its reformulation in terms of slack variables that will be handy to develop a DMPC solution. Model predictive control formulationMPC encompasses the large class of control algorithms that make explicit use of a process model to obtain the control signal by minimizing an objective function over a given horizon [1]. Moreover, MPC strategies use a receding strategy whereby the horizon is displaced toward the future at each instant, but only the first control signal of the sequence calculated at each step is applied to the plant.

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Section: Simulation Scenario and Analysismentioning

confidence: 99%

Section: Simulation Scenario and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed MPC for resource‐constrained control systems

Scherer

Camponogara

Normey‐Rico

et al. 2014

Optim Control Appl Methods

View full text Add to dashboard Cite

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“…In the case of the photovoltaic parking area, this value is scaled to a tenth of its capacity to make the analyzed situations more realistic: the Spanish self-consumption regulations limit the power of photovoltaic facilities so this arrangement adapts the values of the energy produced to those typically demanded by the industrial cluster considered. Upper limits are calculated in the same way, for both solar collector systems (the one in CIESOL [45] and the other in the desalination plant [46]) but since their control system can regulate the inlet water flow, the lower limits are set to zero.…”

Section: Operation Limits and Conversion Factorsmentioning

confidence: 99%

“…Apart from this, both collector system coefficients are obtained through previously obtained models for each field [45,46].…”

Section: Operation Limits and Conversion Factorsmentioning

confidence: 99%

Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example

et al. 2018

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The energy hub concept and modeling methodology are widely employed tools for solving resource conversion and storage scheduling problems. For instance, industrial clusters might benefit from determining the suitable time to operate their facilities and to sell electricity to the public power grid, according to legal, economic or environmental factors. In this paper, novel elements are introduced in order to more accurately represent real plants and to reduce the amount of decision variables. The major innovation is to consider devices consuming a resource which is not related to the quantity of output produced, by attaching binary decision variables to certain energy hub outputs. Secondly, a path vector is defined to take into account the flows of resources within the system instead of employing a variable for each branch between the components. The third innovation consists of an additional vector to express the amount of output resources sold from the energy hub, including constraints for those resources which are exported and imported through the same medium. An extended energy hub model is first proposed and then applied to a real plant example, including multiple and heterogeneous resources and performing a comparison between days with different demands, weather conditions and electricity prices. The results obtained in the selected scenarios demonstrate a logical operation scheduling, and therefore validate the proposed approach.

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