Accelerated optimal maintenance scheduling for generation units on a truthful platform

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.