Collaboration protocols for sustainable wind energy distribution networks

Wind energy has attracted more attentions in recent decades as a green and sustainable electricity generation resource with little or no pollution. Fluctuations in output of wind turbines and their dependency on environmental conditions, however, limit their penetration into power grids. This research proposes a collaboration platform to overcome these challenges by collaboration among communities in the energy distribution network. First, a second order trigonometric regression model is applied to forecast energy demands, and a multiple linear regression model is used to predict energy output of a wind farm. Based on this information, communities in the network can initiate the collaboration process through the proposed platform, which is controlled by two collaboration protocols: Demand and Capacity Sharing Protocol (DCSP) and Best Matching Protocol (BMP). The protocols are applied to optimize communities' profit through matching the communities with excessive capacity with the ones with energy shortage so that they can create a sustainable distribution network. A simulation of three communities with three wind farms is conducted to measure the impact of the platform. The results show the sustainability of the energy network can be achieved by reduced demand loss and holding cost and improved collaborative capacity. •We propose a collaboration platform & protocols for sustainable wind energy networks.•Two protocols are designed: demand and capacity sharing and best matching protocols.•Regression analysis is conducted to estimate energy demand and capacity.•Simulation is conducted to assess impact of protocols on collaborative performance.•The collaboration protocols improve network sustainability by handling variability.