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

机译：一种充分分散的碳交易电力调度双共识方法

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摘要

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.
机译：建立并调查了基于全球和基于分区的动态功率调度问题，并进行了集成在碳排放交易系统中的风电。为了满足这一挑战，通过共享乘法器的交替方向方法的分布式双共识方法通过共享与通常共享的相邻总线上的分区子问题而不是相位角之间的耦合约束相关联的拉长乘法器来实现。从而保护每个的密钥私有信息子系统。此外，通过采用有限时间平均共识算法提出了一种完全分散的算法，这使得每个分区能够以有限数量的步骤迭代地接近其共享信息的共识。为了比较目的，首先实现基于全球的集中优化，采用碳价格对改进的IEEE-30总线系统的操作的影响，然后用三种不同的电力系统的分区方法测试了所提出的算法。结果表明，碳价格较高，可以将碳价格视为减少风削率的激励，并促使使用清洁燃料的使用增加。与集中优化的结果相比，算法都可以实现令人满意的会聚精度，尽管完全分散的算法需要稍长的计算时间稍长。

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