Power Management for Hospital Combined Distributed Power with Load Prediction Using Deep Learning in Islanded Operation Mode

Abstract

The purpose of this paper is to propose a power management method in a hospital for a combination of emergency generators (EGs) with photovoltaic power generation (PV). The power balance of the grid not only influences the droop control for the generator but also the output fluctuations of the PV. Frequency control and a load control by a load prediction are necessary for the system grid combined with EGs and PV in an islanded operation mode. When the PV system is installed in the grid, the EG system should distribute power to small generators, the reason is because when the EG is too large, the power balance cannot be maintained to stabilize the system frequency in all power ranges. Since the distributed generation system needs the demand for each generator, it is important to predict the load. This paper proposes a new method for power energy management for stabilization with the islanded operation mode in a hospital power grid with load prediction using deep learning. The proposed method can realize operation by using a power emulator with the hospital power grid model. The verification of results show that the power emulator is effective in the energy management strategies.