Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition
Advanced control techniques for modern distribution grids are becoming fundamental for the reduction of grid reinforcements while maintaining network performances. In particular, as shown in literature, distributed algorithms for voltage control have gained much interests in comparison with the typi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9765511/ |
| _version_ | 1811248743159169024 |
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| author | Edoardo De Din Martina Josevski Marco Pau Ferdinanda Ponci Antonello Monti |
| author_facet | Edoardo De Din Martina Josevski Marco Pau Ferdinanda Ponci Antonello Monti |
| author_sort | Edoardo De Din |
| collection | DOAJ |
| description | Advanced control techniques for modern distribution grids are becoming fundamental for the reduction of grid reinforcements while maintaining network performances. In particular, as shown in literature, distributed algorithms for voltage control have gained much interests in comparison with the typical centralized formulation for its feasibility. Distributed model predictive control (MPC) is shown to optimally manage the Distributed Generators (DGs) over time and it can be implemented locally at the controllable sources. For this purpose, this paper adopts a distributed algorithm recently presented in the literature for solving a constraint-coupled optimization problem for model predictive voltage control. A detailed reformulation of the original MPC problem for the specific application is presented. Besides, this paper provides a calculation of the convergence limit for the value of the iteration step size of the algorithm, supported by numerical results. The proposed distributed solution of model predictive voltage control is compared with a centralized formulation via numerical simulation in terms of the percentage of error with the centralized solution and number of iteration for the convergence. |
| first_indexed | 2024-04-12T15:34:33Z |
| format | Article |
| id | doaj.art-5f5181af5102448ea4a5d948c8b3cfde |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T15:34:33Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5f5181af5102448ea4a5d948c8b3cfde2022-12-22T03:27:00ZengIEEEIEEE Access2169-35362022-01-0110505085052210.1109/ACCESS.2022.31713459765511Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed DecompositionEdoardo De Din0https://orcid.org/0000-0003-2271-4808Martina Josevski1Marco Pau2https://orcid.org/0000-0002-4681-2317Ferdinanda Ponci3https://orcid.org/0000-0003-0431-9169Antonello Monti4https://orcid.org/0000-0003-1914-9801Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, GermanyInstitute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, GermanyDepartment of Grid Planning and Operation, Fraunhofer Institute for Energy Economics and Energy System Technology, Kassel, GermanyInstitute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, GermanyInstitute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, GermanyAdvanced control techniques for modern distribution grids are becoming fundamental for the reduction of grid reinforcements while maintaining network performances. In particular, as shown in literature, distributed algorithms for voltage control have gained much interests in comparison with the typical centralized formulation for its feasibility. Distributed model predictive control (MPC) is shown to optimally manage the Distributed Generators (DGs) over time and it can be implemented locally at the controllable sources. For this purpose, this paper adopts a distributed algorithm recently presented in the literature for solving a constraint-coupled optimization problem for model predictive voltage control. A detailed reformulation of the original MPC problem for the specific application is presented. Besides, this paper provides a calculation of the convergence limit for the value of the iteration step size of the algorithm, supported by numerical results. The proposed distributed solution of model predictive voltage control is compared with a centralized formulation via numerical simulation in terms of the percentage of error with the centralized solution and number of iteration for the convergence.https://ieeexplore.ieee.org/document/9765511/Voltage controldistributed optimizationsmart gridsnetworked control systems |
| spellingShingle | Edoardo De Din Martina Josevski Marco Pau Ferdinanda Ponci Antonello Monti Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition IEEE Access Voltage control distributed optimization smart grids networked control systems |
| title | Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition |
| title_full | Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition |
| title_fullStr | Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition |
| title_full_unstemmed | Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition |
| title_short | Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition |
| title_sort | distributed model predictive voltage control for distribution grid based on relaxation and successive distributed decomposition |
| topic | Voltage control distributed optimization smart grids networked control systems |
| url | https://ieeexplore.ieee.org/document/9765511/ |
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