In this project, conducted in collaboration with Japan’s Ministry of Economy, Trade and Industry (METI) and the Agency for Natural Resources and Energy, we explore the system-control mathematics behind a “Japanese Connect & Manage” framework. Our goal is to propose a desirable future vision for a smart grid from the standpoint of mathematical control theory.

As Japan’s 6th Basic Energy Plan (approved by Cabinet in October 2021) sets the goal of achieving carbon neutrality by 2050, it emphasizes the simultaneous realization of S + 3E: Safety, Energy Security, Economic Efficiency, and Environment. However, existing grid operation systems are designed under assumptions of conventional generation (e.g. thermal, nuclear), and are not well adapted to the characteristics of renewable energy, which vary unpredictably with weather.

Our laboratory addresses this gap by applying system control mathematics to develop market-driven control methods that balance grid stability and economic efficiency under large-scale integration of renewables. Specific research tasks include:

• Acceleration via machine learning of large-scale energy storage planning under renewable uncertainty

• Analysis and design of real-time market mechanisms evaluating renewable + storage together with conventional generation

• Market-driven control of synchronized generators and grid-forming inverters based on dissipativity principles

• Extension of GUILDA (Grid & Utility Infrastructure Linkage Dynamics Analyzer) to integrate planning and operation in simulation

These research and development themes require an interdisciplinary fusion of distributed cooperative control, mathematical optimization, time-series forecasting, mechanism design, machine learning, and model-predictive control.

We also coordinate with the Agency for Natural Resources and Energy of the Ministry of Economy, Trade and Industry (METI) in discussions on institutional design for a Japan-style “Connect & Manage” framework. At the same time, through this research, we aim to contribute to future smart grid systems by proposing mathematically grounded visions for power system infrastructures.