Accelerated optimal maintenance scheduling for generation units on a truthful platform

Maintenance of generation units is a measure to ensure the reliability of power systems. In this paper, a novel blockchain-based truthful condition-based maintenance of generation units (T-CBMGU) platform is proposed to innovate and upgrade state-of-the-art CBMGU. In addition, two valid inequalities...

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Main Authors: Jianfeng Fu, Alfredo Núñez, Bart De Schutter
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:Energy Reports
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722014469
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author Jianfeng Fu
Alfredo Núñez
Bart De Schutter
author_facet Jianfeng Fu
Alfredo Núñez
Bart De Schutter
author_sort Jianfeng Fu
collection DOAJ
description Maintenance of generation units is a measure to ensure the reliability of power systems. In this paper, a novel blockchain-based truthful condition-based maintenance of generation units (T-CBMGU) platform is proposed to innovate and upgrade state-of-the-art CBMGU. In addition, two valid inequalities are proposed to accelerate the convergence speed of Benders decomposition in maintenance scheduling process. The proposed valid inequalities are formulated based on technical/physical analysis and greedy-based heuristic initialization. More specifically, for data acquisition and failure rate diagnosis/prognosis processes, T-CBMGU can ensure the immutability of the collected operational data. In this way, the influence of tampered data on the diagnosis/prognosis results in state-of-the-art CBMGU can be reduced. For maintenance scheduling and bidding to change scheduled time slot processes, in state-of-the-art CBMGU, the decision makers, i.e., independent system operators (ISOs), may not be trusted. However, in T-CBMGU, the scheduling and bidding processes are implemented automatically via smart contracts rather than by the ISOs; as such, incentives to manipulate data can be avoided. Finally, regarding performance of maintenance actions, in contrast to state-of-the-art CBMGU, the implementation process can be truthfully recorded by the T-CBMGU platform, which facilitates backtracking of responsibility. Then, the T-CBMGU platform and the valid inequalities are tested for the IEEE 300-bus power system. Furthermore, cases with tampered data and distrust caused by fairness manipulation are simulated to show the importance of using T-CBMGU. Finally, the Benders decomposition algorithm with valid inequalities is compared with other solvers to demonstrate its fast convergence speed.
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spelling doaj.art-901e071f4e354eab93d8c267b4efd3a22023-02-21T05:12:44ZengElsevierEnergy Reports2352-48472022-11-01897779786Accelerated optimal maintenance scheduling for generation units on a truthful platformJianfeng Fu0Alfredo Núñez1Bart De Schutter2Delft Center for Systems and Control, Delft University of Technology, Building 34, Mekelweg 2, Delft, The Netherlands; Corresponding author.Department of Engineering Structures, Delft University of Technology, Gebouw 23, Stevinweg 1, Delft, The NetherlandsDelft Center for Systems and Control, Delft University of Technology, Building 34, Mekelweg 2, Delft, The NetherlandsMaintenance of generation units is a measure to ensure the reliability of power systems. In this paper, a novel blockchain-based truthful condition-based maintenance of generation units (T-CBMGU) platform is proposed to innovate and upgrade state-of-the-art CBMGU. In addition, two valid inequalities are proposed to accelerate the convergence speed of Benders decomposition in maintenance scheduling process. The proposed valid inequalities are formulated based on technical/physical analysis and greedy-based heuristic initialization. More specifically, for data acquisition and failure rate diagnosis/prognosis processes, T-CBMGU can ensure the immutability of the collected operational data. In this way, the influence of tampered data on the diagnosis/prognosis results in state-of-the-art CBMGU can be reduced. For maintenance scheduling and bidding to change scheduled time slot processes, in state-of-the-art CBMGU, the decision makers, i.e., independent system operators (ISOs), may not be trusted. However, in T-CBMGU, the scheduling and bidding processes are implemented automatically via smart contracts rather than by the ISOs; as such, incentives to manipulate data can be avoided. Finally, regarding performance of maintenance actions, in contrast to state-of-the-art CBMGU, the implementation process can be truthfully recorded by the T-CBMGU platform, which facilitates backtracking of responsibility. Then, the T-CBMGU platform and the valid inequalities are tested for the IEEE 300-bus power system. Furthermore, cases with tampered data and distrust caused by fairness manipulation are simulated to show the importance of using T-CBMGU. Finally, the Benders decomposition algorithm with valid inequalities is compared with other solvers to demonstrate its fast convergence speed.http://www.sciencedirect.com/science/article/pii/S2352484722014469Maintenance of generation unitsBenders decompositionValid inequalityTruthful maintenance platform
spellingShingle Jianfeng Fu
Alfredo Núñez
Bart De Schutter
Accelerated optimal maintenance scheduling for generation units on a truthful platform
Energy Reports
Maintenance of generation units
Benders decomposition
Valid inequality
Truthful maintenance platform
title Accelerated optimal maintenance scheduling for generation units on a truthful platform
title_full Accelerated optimal maintenance scheduling for generation units on a truthful platform
title_fullStr Accelerated optimal maintenance scheduling for generation units on a truthful platform
title_full_unstemmed Accelerated optimal maintenance scheduling for generation units on a truthful platform
title_short Accelerated optimal maintenance scheduling for generation units on a truthful platform
title_sort accelerated optimal maintenance scheduling for generation units on a truthful platform
topic Maintenance of generation units
Benders decomposition
Valid inequality
Truthful maintenance platform
url http://www.sciencedirect.com/science/article/pii/S2352484722014469
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