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Distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain. [ABSTRACT FROM AUTHOR]

Temporal decomposition is a potential approach to relieve the computation cost of power system multi-interval scheduling problems, such as economic dispatch. In this form of decomposition, the considered scheduling horizon is partitioned into several subhorizons. A subproblem is formulated for each subhorizon, and a distributed optimization algorithm strategy is used to coordinate subproblems. The main existing challenge is decomposing the scheduling horizon to gain the most time saving from distributed computing. This paper serves as an extension to our previous work and presents a machine learning-aided temporal decomposition strategy to partition a scheduling horizon optimally. We have found that the load profile, known before solving economic dispatch, significantly affects the best number of subhorizons. We have used load profiles as inputs to a learner whose goal is to assign a temporal decomposition class to each load profile. Possible decomposition classes are divisors of the considered scheduling horizon. Thus, the proposed learning procedure is a multiclass classification. We have selected Extreme Gradient Boosting that is a tree-based classification learner. Simulation results using real-world load profiles show the promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]