Facilitating the shift to more collaborative microgrids by alleviating demand volatility using a precontracted order updates strategy

Microgrids (MGs) have emerged as viable alternatives for delivering electricity to remote rural regions in a secure and ecologically sustainable manner. However, utilizing microgrids in a more collaborative manner might greatly enhance the integration of renewable energy sources into the electricity network. The primary objective of this research is to improve the quality of information communicated from MGs to the utility grid to achieve production levelling. Multiple simulation scenarios have been developed to analyse the performance of a grid-connected MG when the grid order update rules are altered. The findings indicate that the suggested scenarios for collaborative MGs, especially the scenario that relies on forecasted demand for precontracted order updates (COU), are enhancing system performance by stabilizing order volatility to the grid (58% less unplanned volatility of orders in the collaborative MG), thereby reducing the carbon footprint of the MG by 67% and increasing storage utilization by 74%. The only limitation was the volume of exported electricity; however, the implementation of long- term storage capacity (seasonal storage) has effectively reduced the exported power to 0. A distinct trade-off exists between enhanced storage capacity at a higher cost and a significant volume of exported power. The optimal resolution for this trade-off is greatly affected by the initial investment in storage technology and the feed-in tariff rate for exported power. The main conclusion may be summarized as the transition towards more collaborative MGs may serve as the foundation for developing more decentralized electrical networks and integrating more renewable energy sources into the current electricity system. • A distinction between selfish and collaborative MGs. • A novel grid order update strategy. • Minimizing the unplanned volatility of demand. • Real time simulation scenarios.