A fully decentralized dual consensus method for carbon trading power dispatch with wind power

Abstract

The global-based and partition-based dynamic power dispatch problems with wind power integrated into the carbon emission trading system are established and investigated. To meet this challenge, a distributed dual consensus algorithm based the alternating direction method of multipliers is implemented by sharing Lagrangian multipliers associated with coupling constraints between partitioned subproblems rather than phase angles on adjacent buses that are usually shared, thus protecting the key private information of each subsystem. Furthermore, a fully decentralized algorithm is proposed by adopting the finite-time average consensus algorithm, which enables each partition to iteratively approach a consensus of its shared information in a finite number of steps. For comparison purposes, a global-based centralized optimization is implemented at first, adopting the effect of carbon price on the operation of a modified IEEE-30 bus system, followed by tests of the proposed algorithms with three different partitioning methods of power systems. Results illustrate that a higher carbon price can be regarded as an incentive to decrease the wind curtailment rates and spur the increased use of clean fuel. Compared with the results of the centralized optimization, both the algorithms can achieve satisfactory convergence accuracies, although the fully decentralized algorithm requires slightly longer time for computation.