An economic D-RTO for thermal solar plant: analysis and simulations based on a feedback linearization control case

Pataro, Igor M. L.; Costa, Marcus V. Americano da; Roca, Lídia; Sanches, José L. Guzmán; Berenguel, Manuel

doi:10.48011/asba.v2i1.1294

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…These trajectories are updated regularly with a new dynamic optimization run based on updated forecasts and current measurements of state variables and disturbances. This has been tested for a concentrating solar thermal field, without considering the storage tank, in [8]. Another work focused on the daily storage management of a solar district heating system [9], by optimizing the flow rate between a long term and a short term storage tanks.…”

Section: Solar Thermal Plants Optimizationmentioning

confidence: 99%

Rolling Horizon Dynamic Real-Time Optimization of a Solar Thermal Plant with a Planning Phase

Untrau

Sochard

Marias

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Solar thermal plants operate in a highly variable environment, with variations in both the energy source and the heat demand. Moreover, weather and load forecasts contain uncertainty. Thermal energy storage helps to decouple the heat production from the heat supply and gives the solar thermal plant more flexibility while complexifying its operation. In this work, a Dynamic Real-Time Optimization (DRTO) methodology is presented. Firstly, a planning phase determines the best storage management policy, given the estimated weather and load forecasts. An economic DRTO algorithm is then used to update the optimal trajectories to minimize the operating costs of the plant while respecting the storage management policy determined at the planning level, despite disturbances in the weather conditions. This stage uses updated forecasts and real-time information to update the optimal trajectories. This methodology is tested on a "virtual solar plant" (a detailed dynamic model of an existing plant) in a case study, with real data for the weather forecasts and measurements and a variable heat demand. Results obtained without and with DRTO adjustment are compared. In the first case, the virtual plant is operated using trajectories computed with offline dynamic optimization (DO) at the planning phase and undergoing the real-time weather and load conditions, while in the second case, real-time modification of the operating trajectories is performed. We observe an improvement in the solar fraction used to satisfy the heat demand and a reduction in the operating costs with DRTO compared to DO, without degrading the storage management significantly. The results are promising for an application to an existing plant.

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Section: Solar Thermal Plants Optimizationmentioning

confidence: 99%

Rolling Horizon Dynamic Real-Time Optimization of a Solar Thermal Plant with a Planning Phase

Untrau

Sochard

Marias

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

View full text Add to dashboard Cite

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Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production

Untrau,

Sochard,

Marias

et al. 2024

Applied Energy

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A Real-Time Identification Algorithm and Epidemic Model Associated with Social Distancing Index for Control Applications: COVID-19 Analysis and Simulations in the United States

Costaa

2021

BJSTR

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The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is already the most worrying health problem worldwide. The virus spread rapidly across the countries, making many researchers looking for answers to mitigate the disease's effects. While vaccines are not available on a large scale, mathematical modeling has allowed the scientific community to perform forecasts for decision-making and social distancing policies to decrease the velocity of the COVID-19 transmission. However, dynamic models must represent the pandemic's reality considering validation criteria and an appropriate procedure that ensures realistic simulations. These principles are the only way to make an epidemiological model useful for studying and analyzing practical effects. From a control theory point of view, representative models ensure optimal solutions and allow a more reliable and robust control strategy. Therefore, this paper proposes a parameter identification algorithm for a dynamic pandemic disease model spread, including a new time-varying parameter for control applications. The developed framework uses an epidemiological model, denoted here as , capable of associating the real pandemic dynamics to its biological parameters and the population social mobility in real-time, which can be led by an appropriate optimal control strategy. Simulations and forecasts are performed comparing with official data of the epidemic in the United States.

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An economic D-RTO for thermal solar plant: analysis and simulations based on a feedback linearization control case

Cited by 3 publications

References 19 publications

Rolling Horizon Dynamic Real-Time Optimization of a Solar Thermal Plant with a Planning Phase

Rolling Horizon Dynamic Real-Time Optimization of a Solar Thermal Plant with a Planning Phase

Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production

A Real-Time Identification Algorithm and Epidemic Model Associated with Social Distancing Index for Control Applications: COVID-19 Analysis and Simulations in the United States

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