This paper reviews the latest trends and challenges in battery management systems (BMS) using reinforcement learning (RL) techniques. RL has gained attention as an effective solution to address the volatility of renewable energy sources and the need for advanced power demand-supply balancing. Unlike traditional methods such as linear programming and model predictive control, RL demonstrates superior flexibility and adaptability in dynamic and uncertain environments. Key topics covered include the application of RL algorithms like Proximal Policy Optimization (PPO) in energy storage systems, highlighting their advantages in optimizing battery charge and discharge schedules. However, practical implementation faces challenges such as computational resource requirements and integration with real-world systems. Future directions include integrating RL-based BMS with electric vehicles and smart appliances to achieve more efficient energy management. This study aims to contribute to the development of sustainable and intelligent energy systems for future power grids.

As one of the measures to accept massive integration of renewable energy into Japanese electric power system, the Japanese version of Connect and Manage is under technical development and demonstration. By combining DC-SCUC and ACOPF models, we developed an operation planning model which considers transmission losses, active and reactive power flows, voltage control, and transformer taps. The verification calculation was performed on a future Tokyo bulk power system model in 2030. The optimal start/stop plan obtained by the combined model was modified from one by DC-SCUC, which indicates that it is important to consider the transmission losses and voltage control appropriately in Unit Commitment.

A load frequency control (LFC) becomes more difficult by the strong growth of renewable power generation. One of the promising measures to support LFC is a battery energy storage system (BESS). Frequency deviation (Δf) can be more effectively suppressed by using BESS for governor-free (GF) control (Case-G) than for Merit-Order based LFC (Case-L). However, kWh capacity of thermal power plants required for LFC in Case-G is larger than those in Case-L because the output of BESS is not taken into account in LFC. Therefore, this study proposes a LFC in consideration of BESS utilization for GF (Case-G_FB). As a result, kWh capacity of thermal power plants required for LFC is reduced in Case-G-FB compared to those in Case-G. In addition, the peak of Δf in Case-G_FB is a little smaller than those in Case-G because of adjusting the feedback value in consideration of Δf changing property.

This paper proposes connection phase estimation of pole mounted distribution transformers by majority voting of high-quality solutions using sampled stages. The essential challenge of the connection phase estimation of pole mounted distribution transformers problem is that the correct connection phase combination and the combination with the best objective function value do not match because of using inconsistent data. In order to solve the challenge, the proposed method utilizes the concept of majority voting of ensemble learning andtheproximate optimality principle. The proposed method is verified to estimate the connection phase accurately and specify pole mounted distribution transformers with insufficient estimation accuracy using a distribution system model.

It is desirable to reduce electricity charges of waterworks as much as possible because the electricity consumption of water pumps is very large. Although water utilities generally purchase electricity from retail electricity providers, the electricity charges could be reduced by trading the electricity at wholesale power exchanges in partnership with such providers. In this paper, a method to determine the day-ahead schedule of the water facilities based on the water distribution forecast is proposed assuming that the water utility participates in the spot market transaction. In addition, a method to operate the water facilities in the current day to prevent water shortages and overflows of the distribution reservoirs due to the forecast errors is proposed. The effectiveness of the proposed methods is evaluated by numerical simulations considering water transmission and distribution throughout one year.

Discussions are underway to make the reserve market to a day-ahead market starting in 2026. When it is realized, power producers need to make three types of plans for the next day: a unit commitment, bids for electricity market, and bids for reserve market. In a free competition, power producer needs to make these plans ensuring a stable earning. In this study, we present a practical method to plan a risk-averse unit commitment along with bids for electricity market, and reserve market. The numerical results show that a proposed method improves CVaR of a revenue from 1.40 to 5.14% within one hour of calculation time.

Considering optimization of the laying route for submarine power cables off the coast of Japan, there are many deep-water areas over depth of 1000m. In Japan, submarine power cables have been installed at a depth of about 300m as maximum, however, there is no track record beyond that level. The laying depth is deeper, the laying tension is larger. Therefore, as a result of discussion on the cable structure and material suitable for the laying in deep water, the aluminum conductor with keystone structure was applied for reducing the cable weight since the laying tension is proportional to the product of depth and weight. In this study, the prototype submarine cables of DC 500kV class (transmission capability of 1 GW) equipped with the double steel wire armour were manufactured, then mechanical and electrical properties were evaluated in accordance with CIGRE TB 496 and TB 623. As a result, the cables of our design, on which the tensile bending test had been carried out in advance, passed the high-voltage 30 days load cycle test followed by the lightning impulse test without any defect.

The mission of the Research and Development (R&D) Steering Committee of the Power and Energy Society (PES) of the Institute of Electrical Engineers of Japan (IEEJ) is to facilitate the research and development in the electric power energy technology. In this article, activities of the R&D Steering Committee in fiscal year 2024 are reported. Also, recent trend and future issues are discussed. Furthermore, efforts and actions to invigorate technical activities of the IEEJ PES and to improve service for members are described.