When many wind power generators and photovoltaic power systems, which are affected by natural conditions, are introduced into the power system, it is necessary to consider the effects of voltage fluctuations. Therefore, in the operation of both power generation facilities, it is desirable to reduce uncertainties related to the power system and achieve both a stable supply of power and economic operation. As an effective solution, output power forecasting of wind power generators and photovoltaic power systems based on time series analysis and weather forecasting can be mentioned. If it becomes possible to predict the output power of natural energy power generation equipment by using weather forecast information, it will be possible to economically operate the power system in the system operation plan and reduce the cost of additional equipment. Our laboratory proposes a distributed power generation operation method for the forecast target period using the output power forecast data of wind power generators and photovoltaic power systems.

■Implementation image of model predictive control (MPC) based on prediction information.

As shown in the above figure, our laboratory is engaged in a recent research on smart grids and smart houses using MPC（Model Predictive Control） with the aim of expanding the introduction of power generation using renewable energy. It is desirable to develop methods for utilizing IoT (Internet of Things), deep learning, and AI (Artificial Intelligence). Since these research fields will become more and more important in the future, we believe that it is necessary to actively promote research. Based on the above, our laboratory will continue to work on research that can contribute to society by making use of the research results so far.