The introduction of smart electric meters in Japan began in 2014, with installation at all consumers scheduled to be completed by 2024. During this period, the power grid and its environment has changed significantly. Next-generation smart electric meters, specifications for which were established in 2022, are scheduled to be gradually introduced from 2025. The potential use of smart electric meters will expand greatly, and it is expected that new value-added services will be realized. This article introduces the functions of next-generation smart electric meters, current use cases at power companies, and research trends in the use of smart electric meter data in academic field both in Japan and overseas.

This paper proposes a multi-objective particle swarm optimization (MOPSO) algorithm for optimizing the placement of energy storage systems in active distribution networks to accommodate the integration of renewable energy sources such as solar and wind. Simulation analysis will demonstrate the algorithm's effectiveness in optimizing power distribution, reducing losses, and maintaining voltage stability in a dynamic energy environment, focusing on improving grid stability and efficiency. The algorithm considers the intermittent nature of renewable energy sources and strategically places energy storage systems to mitigate these fluctuations. By optimizing the placement of energy storage batteries, the algorithm not only improves the reliability and resilience of the grid but also makes more efficient use of renewable energy sources. Simulation results validate the algorithm's performance, with significant improvements in critical targets compared to previous approaches. In conclusion, this study provides a comprehensive solution for integrating renewable energy sources into distribution networks to ensure stability and reliability.

The use of HVDC systems has expanded with the increasing interconnection of power systems. However, this progress has also made systems more complex. Various simulation methods are employed to simulate and understand these complex interactions in research, development, and testing. Because simulation and analytical methods for HVDC systems and grid converters have diversified, selecting the appropriate method based on understanding their advantages and limitations is crucial for efficient research and testing. This paper reviews model-based development and various simulation techniques that bridge the gap between computer simulations and experiments, clarifying their differences, advantages, and disadvantages.

Previously, in temperature distribution analysis inside a transformer, there is an overall analysis that simulates the core and tank. However, insulators are not taken into consideration because the analysis cost increases. Therefore, the actual oil flow differs from the analysis results, and there is a possibility that the temperature may be evaluated higher than the actual oil flow, or the location of the hot spot may differ. In this study, we first focused on the temperature change around the core and took into the insulator that greatly contributes to the oil flow. Next, the insulation in the gap between the cores is represented by thermal resistance. This has made it possible to analyze changes in oil flow and temperature distribution inside a transformer, taking insulators into account.