Economic model predictive control for energy management of a microgrid connected to the main electrical grid

Alarcon, Martin A.; Alarcon, Rodrigo G.; González, Alejandro H.; Ferramosca, Antonio

doi:10.1016/j.jprocont.2022.07.004

Cited by 17 publications

(13 citation statements)

References 27 publications

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“…In this section, we first describe the microgrid model that we will later use to generate the simulation results and indicate the main characteristics of the EMS developed in 16 . Finally, we introduce the theoretical foundations of artificial neural networks, especially those of the recurrent type, the deep learning branch that we use to propose the prediction model for the energy demand to be included in the EMPCT problem.…”

Section: Preliminariesmentioning

confidence: 99%

“…As in any MPC control strategy, we need a mathematical model that can predict the dynamics for the variables of interest in the system. In this sense, as we indicated and used in 16 , a linear discrete-time state-space model proposed to manage the electrical resource available in the different components of a microgrid is:…”

Section: Microgrid Modelingmentioning

confidence: 99%

“…The main contribution of this work is to propose a prediction model for energy demand using deep learning and then include it directly in the EMS optimization problem presented in 16 . It is worth noting that this EMS formulation was carried out under the framework of an economic MPC with the ability to maintain the feasibility of the optimization problem before possible changes of the economic criterion to be optimized (EMPCT) 17 , which occurs due to the dynamics of the system itself or due to changes in objectives by the owner of the microgrid.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural networks for energy demand prediction in an economic MPC-based energy management system

ALARCÓN,

Alarcón,

González

et al. 2024

Preprint

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Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using an artificial neural network (ANN). First, we develop a framework to identify an ANN using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.

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Section: Preliminariesmentioning

confidence: 99%

Section: Microgrid Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial neural networks for energy demand prediction in an economic MPC-based energy management system

ALARCÓN,

Alarcón,

González

et al. 2024

Preprint

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“…In [8], EMPC is employed to minimize the cost of storage degradation and electricity purchase of a microgrid. Similarly, in [9], an EMPC strategy is proposed to minimize the cost of deviations from the users demand, energy purchase and storage degradation of a microgrid. In the context of ancillary services provision, EMPC is employed in [10] for electrical vehicles participation in frequency regulation markets.…”

Section: Introductionmentioning

confidence: 99%

Economic model predictive control for the energy management problem of a virtual power plant including resources at different voltage levels

Santosuosso,

Camal,

di Giorgio

et al. 2023

IET Conf. Proc.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…This paper examined the MG voltage and frequency stability from a power electronics perspective. Economic model predictive control (EPMC) offered economic dispatch for residential microgrids in 9 . Authors in this paper addressed applying prediction error in RESs output in their model with stochastic MPC in the future attempt.…”

Section: Introductionmentioning

confidence: 99%

A robust energy management system for Korean green islands project

Tightiz

Yoo

2022

Sci Rep

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Penetration enhancement of renewable energy sources is a core component of Korean green-island microgrid projects. This approach calls for a robust energy management system to control the stochastic behavior of renewable energy sources. Therefore, in this paper, we put forward a novel reinforcement learning-driven optimization solution for the convex problem arrangement of the Gasa island microgrid energy management as one of the prominent pilots of the Korean green islands project. We manage the convergence speed of the alternating direction method of multipliers solution for this convex problem by accurately estimating the penalty parameter with the soft actor-critic technique. However, in this arrangement, the soft actor-critic faces sparse reward hindrance, which we address here with the normalizing flow policy. Furthermore, we study the effect of demand response implementation in the Gasa island microgrid to reduce the diesel generator dependency of the microgrid and provide benefits, such as peak-shaving and gas emission reduction.

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Economic model predictive control for energy management of a microgrid connected to the main electrical grid

Cited by 17 publications

References 27 publications

Artificial neural networks for energy demand prediction in an economic MPC-based energy management system

Artificial neural networks for energy demand prediction in an economic MPC-based energy management system

Economic model predictive control for the energy management problem of a virtual power plant including resources at different voltage levels

A robust energy management system for Korean green islands project

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