Stochastic distributed optimization of shapeable energy resources in low voltage distribution networks under limited communications

Due to the rising renewable penetration rate, modern low voltage distribution network (LVDN) calls for active control with tractable computation and limited communication. To tackle this, the paper proposes a novel stochastic distributed optimization approach. The computation of optimum is completely decentralized, with a global broadcast signal is used as public reference in order to guarantee the consistency among individual shapeable energy resources. The proposed approach employs Bernoulli trials to imitate the searching process in classical gradient descent approach, and player compatible relationship is employed to play the role of gradients to indicate the direction of the search. Working in a model-free manner without relying on iterations, the proposed approach offers an approximate optimization to minimize the accumulated compensation of reshaping/deferring the shapeable energy resources in a given LVDN while respecting the system constraints. A 103 nodes test network based on a realistic Belgian semi-urban distribution network is used for validation. With two different profiles and a special case of communication failure, the proposed approach is validated and benchmarked with a classical AC optimal power flow algorithm. The results prove that the proposed approach is able to deliver a good approximation to the theoretical optimum with reasonable gap.

Automated summary

This paper proposes a novel stochastic distributed optimization approach to tackle the active control in modern LVDN with rising renewable penetration rate. By using global broadcast signal as public reference, the proposed approach achieves decentralized computation and consistency among individual shapeable energy resources. The method employs Bernoulli trials and player compatible relationship to imitate classical gradient descent approach for optimum computation.