Optimal Generation Capacity Allocation and Droop Control Design for Current Sharing in DC Microgrids

Considering the increasing amounts of renewable energy generation and energy storage required to meet ambitious environmental goals, it is clear that the next generation of power grids will be dominated by converter-connected devices. In addition, the increasing share of loads connected via power electronics and the general transition to non-synchronous grids with distributed generators make dc microgrids an attractive future alternative. However, achieving optimal utilization of distributed generators in such cases is a complex task, as the performance depends on both the grid and control design. In this paper, we consider such a case where the optimal utilization of distributed generators is achieved by optimal power sharing while taking into account the grid topology, the available generators, and the way they are controlled. For the latter, we consider a droop-based decentralized control scheme whose primary objective is to achieve voltage regulation in the allowable operating range. A novel mixed-integer optimization approach is proposed to identify the optimal converter size and location in the network so that the microgrid can operate safely and with optimal use of the available resources. Time-domain simulations are used to validate the proposed approach and demonstrate its robustness to uncertainty in generator availability.

Automated summary

With the increasing penetration of renewable energy and energy storage, converter-connected devices will dominate the next generation of power grids. DC microgrids are considered an attractive alternative due to the transition to non-synchronous grids and loads connected via power electronics. This paper proposes an optimal power sharing scheme using a droop-based decentralized control to achieve voltage regulation, and an integer optimization approach to determine the optimal converter size and location for safe and optimal operation. Time-domain simulations validate the approach's robustness to generator availability uncertainty.