Management and control optimization based on deep learning model

Abstract

Microgrid technology is a key solution to improve distributed power consumption, complementary utilization of multiple energy sources, and power supply reliability. To guarantee the reliability of the microgrid system, a realistic strategy must be created. This work takes the microgrid as an object and uses simulation technology to construct a microgrid system. Then, using this simulation system and the double deep Q-learning, the goal is to minimize the 24-hour electricity consumption cost from the external power grid to meet the requirements of voltage deviation. Power balancing and energy storage loads for microgrid systems. Under the constraints of the electrical state and other constraints, the control variable is the energy storage's capacity for charging and discharging, and the optimization strategy of energy storage control is obtained through training. The results demonstrate that the DDQN algorithm will save 26.95% of the electricity purchase cost, which is significantly more than the MPPT algorithm's 12.43% savings. As a result, this work examines the efficacy of the charging and releasing approach for energy storage and confirms the potential of the suggested approach to reduce the cost of purchasing electricity.