On‐line decentralised economical dispatch for power system with highly penetrated uncertain renewables

With the increasing concern on environmental and energy conserving, the new advancements in distributed generators (DGs) have enabled them to be grid‐connected with high penetration in distribution network. The remarkable characteristic of distributed network is that various small‐scale DGs, such as wind generators, solar generators, fuel cell and gas generator, would widely scatter across power network, and there is a pressing need for revisiting some of the fundamental problems in electric grid, e.g. economical dispatch (ED), to figure out better solutions to suit these new features. Therefore, it is the aim of this study to develop an on‐line decentralised ED to effectively and flexibly allocate the fast DGs outputs to quickly match the frequently and fast fluctuating outputs of uncertain renewables in distribution network. Firstly, the dynamic model of fast DGs will be designed to simulate their good capability of reference tracking, which would be used in the on‐line ED approach later. Then wind power generation is analysed based on the probabilistic model to generate typical time‐series data for ED. Finally, an on‐line decentralised ED approach based on the alternating direction method with multiplier algorithm is proposed to optimally allocate the frequently changed load demand and fluctuating uncertain renewables among multiple DGs in real time. The proposed on‐line decentralised ED approach could solve large complex problems by minimising the control variables and the dual variables in sequential iterations, and it is a novel divide‐and‐conquer decentralised optimisation method for ED. The proposed approach will be thoroughly tested on a distribution network consisting of multiple DGs and uncertain wind power generations. Comparative results with the conventional centralised ED algorithm confirm the accuracy and validity of the on‐line decentralised ED approach for power system operating with highly penetrated uncertain wind power generations.