Accelerated Optimal Maintenance Scheduling for Generation Units on a Truthful Platform

Reference

J. Fu, A. Núñez, and B. De Schutter, "Accelerated optimal maintenance scheduling for generation units on a truthful platform," Energy Reports, vol. 8, pp. 9777-9786, Nov. 2022.

Abstract

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.

Publisher page

Access full text

Downloads

PDF of corresponding technical report (1.74 MB)

BibTeX

@article{FuNun:22-009,
   author  = {Fu, Jianfeng and N{\'{u}}{\~{n}}ez, Alfredo and De Schutter,
              Bart},
   title   = {Accelerated Optimal Maintenance Scheduling for Generation Units
              on a Truthful Platform},
   journal = {Energy Reports},
   volume  = {8},
   pages   = {9777--9786},
   month   = nov,
   year    = {2022}
   }

Download BibTeX entry