An energy dispatch strategy for smart microgrid with the participation of robotic energy storage

Robotic energy storage systems integrated within smart microgrids are instrumental in reducing the overall economic costs of the system while enabling effective peak-shaving and valley-filling strategies. The mobility and random volatility inherent in robotic energy storage systems pose significant challenges to the energy dispatch of smart microgrids. Therefore, in this paper, an energy dispatch strategy with robotic energy storage participation for the smart microgrid is proposed and solved by using the enhanced particle swarm optimization algorithm. In order to obtain the optimal solution to optimization problems and enhance the algorithm's global exploration capability, an adaptive inertia weight factor is introduced into the particle swarm algorithm. Simulation results demonstrate that the proposed strategy effectively dispatches the robot and leverages its energy storage system to play a pivotal role in the operation of the smart microgrid. The enhanced particle swarm optimization algorithm exhibits a maximum reduction in operation costs of 21.3% compared to other baseline algorithms, which verifies the economic and practicality of the proposed strategy.