Systematic review on model predictive control strategies applied to active thermal energy storage systems

Tarragona, Joan; Pisello, Anna Laura; Fernàndez, Cèsar; Gracia, Álvaro de; Cabeza, Luisa F.

doi:10.1016/j.rser.2021.111385

Cited by 54 publications

(15 citation statements)

References 82 publications

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“…In [112], a combined method of dynamic planning based on predictive control is presented. In [113], a predictive-based approach is proposed for the management of grid-connected photovoltaic power generation. In [114], a microgrid scheduling method based on MPC is proposed to increase the flexibility of distribution networks.…”

Section: Fault Tolerant Of Microgridsmentioning

confidence: 99%

A Literature Review of the Control Challenges of Distributed Energy Resources Based on Microgrids (MGs): Past, Present and Future

Razmi

2022

Energies

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Different types of distributed generation (DG) units based on renewable and non-renewable energy sources can create a local energy system in microgrids. The widespread penetration of distributed energy resources (DERs) has affected many power system issues, such as the control and operation of these networks. For the optimal operation of microgrids, optimal energy planning and management in the new space governing the distribution system requires extensive research and analysis. Getting acquainted with the latest research about the evaluation of the problems and challenges in the design of control systems plays an important role in providing a guidance map for researchers to find the recent challenges and propose new solutions. This paper tried to list the challenges of distributed generation sources for MG applications, opportunities, and solutions. These challenges are reported in hierarchical control strategies and power-sharing categories. Therefore, Model Predictive Control (MPC)-based approaches are reviewed for different recent control levels and power sharing strategies in a comprehensive and simple point of view. The performance comparison of MPC methods together and different allocated fitness functions and implementation algorithms are dedicated. Another hand, the potential of MPC methods to control inverters for increasing the reliability of the grid, which this feature could not be achieved by using conventional strategies, while has not been investigated by researchers widely, is introduced in a short review. Therefore, this paper shows an intersection guidance map for readers to facilitate future research works in these exciting and undiscovered fields.

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Section: Fault Tolerant Of Microgridsmentioning

confidence: 99%

A Literature Review of the Control Challenges of Distributed Energy Resources Based on Microgrids (MGs): Past, Present and Future

Razmi

2022

Energies

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“…Extensive research has been carried out previously in this domain, with work such as [4,5] presenting exhaustive reviews of different control algorithms like Rulebased control, Model Predictive Control, etc. MPC has emerged as an established control technique with works such as [10,11,12] presenting case studies for practical implementations in real-world buildings.…”

Section: Building Control and Modelingmentioning

confidence: 99%

“…Significant research has been carried out in the context of control algorithms for energy management in buildings, ranging from simple Rule-based Controllers to advanced controllers like Model Predictive Control (MPC) and data-driven techniques like Reinforcement Learning (RL) [4]. Amongst these, MPC has been identified as a powerful control algorithm and considerable research spans around different applications and algorithmic variants [5]. MPC is a sample efficient control paradigm that produces interpretable control decisions.…”

Section: Introductionmentioning

confidence: 99%

Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings

Gokhale,

Claessens,

Develder

2021

Preprint

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This paper presents a data-driven modeling approach for developing control-oriented thermal models of buildings. These models are developed with the objective of reducing energy consumption costs while controlling the indoor temperature of the building within required comfort limits. To combine the interpretability of white/gray box physics models and the expressive power of neural networks, we propose a physics informed neural network approach for this modeling task. Along with measured data and building parameters, we encode the neural networks with the underlying physics that governs the thermal behavior of these buildings. Thus, realizing a model that is guided by physics, aids in modeling the temporal evolution of room temperature and power consumption as well as the hidden state, i.e., the temperature of building thermal mass for subsequent time steps. The main research contributions of this work are:(1) we propose two variants of physics informed neural network architectures for the task of control-oriented thermal modeling of buildings, (2) we show that training these architectures is data-efficient, requiring less training data compared to conventional, non-physics informed neural networks, and (3) we show that these architectures achieve more accurate predictions than conventional neural networks for longer prediction horizons. We test the prediction performance of the proposed architectures using simulated and real-word data to demonstrate (2) and (3) and show that the proposed physics informed neural network architectures can be used for this control-oriented modeling problem.

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“…While TES have been interesting research topic with numerous publications, works dedicated to HWST are rather scarce [8]. More of the effort have been concentrated towards solar combined and systems for residential buildings, which leaves the applications for CHPP significantly limited.…”

Section: Introductionmentioning

confidence: 99%

“…More of the effort have been concentrated towards solar combined and systems for residential buildings, which leaves the applications for CHPP significantly limited. Overview of developed models for TES systems are shown in works [8] and [9]. The modeling approach used affects significantly the estimated energy content in the tank, and with it, the potential benefits for the system [9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic model for large scale hot water storage tank

Milan¹,

Zlatkovikj²,

Li³

et al. 2023

Preprint

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Due to the growing share of intermittent renewable energy sources (RES), the requirement for flexibility in the energy system is increasing to balance the generation and demand of electricity. It has been well recognized that Combined heat and power plants (CHPs) can contribute towards improved flexibility in the energy system. Thermal energy storage (TES), using hot water as working fluid, is a commonly integrated in CHPs, which allows for decoupling of heat and electricity generation. It has been verified that proper control of the operation of TES can improve the flexibility provided by CHP. The development of advanced control system relies on accurate dynamic modeling of TES. In this work, a onedimension (1D) dynamic model for large scale TES is developed in Dymola, based on mass and energy balances. It is validated against the operational data from a real CHP plant. Results show that the model can capture the dynamic variation in the operation of the TES energy content with maximum deviations of 6.5% from the maximum value.

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Systematic review on model predictive control strategies applied to active thermal energy storage systems

Cited by 54 publications

References 82 publications

A Literature Review of the Control Challenges of Distributed Energy Resources Based on Microgrids (MGs): Past, Present and Future

A Literature Review of the Control Challenges of Distributed Energy Resources Based on Microgrids (MGs): Past, Present and Future

Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings

Dynamic model for large scale hot water storage tank

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