Japanese word “KEIDENKI”, which means a electrical relay in English, has used from Meiji era. In French and English dictionary at 1729, a “relais” in French means “a fresh horse” in English. In “Electricity and Magnetism” writing by Fleeming at 1873, a electrical relay means “an instrument which retransmits the original signal from a fresh battery” on the Morse's telegraph. The etymology of the “KEIDENKI” is like as a electric relay, interestingly.

In Japan, Protection Relay is called “RIRE” instead of “KEIDENKI” in recent years. Now, the survey for the word “KEIDENKI” is very important issue for review of Electrotechnical Vocabulary of the Japanese Electrotechnical Commitee.

Rapid installation of variable renewable power amid a worldwide transition to low-carbon energy systems would impact the stability of the power system due to an anticipated reduction in the synchronous inertia. Converting excess electricity into hydrogen and its use for power generation using gas turbines are gaining increasing attention since the process may help ensure the power system stability. By using an energy system optimization model, the role of the energy storage technology as well as large-scale batteries in decarbonizing Japan's energy system is investigated. The developed model incorporates the power sector with hourly time resolution to deal with variability of the variable renewables and considers a system non-synchronous penetration (SNSP) constraint. Simulated results reveal that around 730GWh of batteries and 50GWh of hydrogen storage are deployed to satisfy an increasing electricity demand toward 2050 with reducing the carbon intensity of electricity to almost zero. The installation of hydrogen storage may increase when the availability of imported hydrogen is limited. Raising SNSP limit could alleviate the associated cost of energy system decarbonization, and development of advanced technologies on emulated inertia would be crucial.

With the spread of solar power generation (PV) and electric vehicles (EV) in general households, there is concern about fluctuations in the distribution system voltage. In this paper, voltage control is performed by utilizing the battery energy storage system (BESS) owned by the energy consumers. In previous research, household batteries system had been converted to smart inverters, and charging/discharging control based on SoC adjustment had been performed. However, charging/discharging active power for the purpose of distribution system contribution may disturb economical optimal operation in demand side. This paper focus on battery PCS free capacity and propose multi-objective usage of the battery system, for demand side use, e.g. self-consumption of non-FIT PV surplus power, and distribution system voltage control with Volt-Var control. The potential of PCS free capacity under operation of the battery system for each household over than 500 was evaluated as well as the effect of voltage control with real parameter based on distribution system simulator. Then with the quantitative evaluation, significant voltage deviation reduction was confirmed.

Considering the long-term goal of the “Paris Agreement”, drastically reducing the CO2 emission of the energy sector as a whole is essential. Existing study showed an example of the energy portfolio in 2050 achieving the greenhouse gas emission reduction target (80% reduction compared to FY2013) by the transition of primary energy consumption to electricity by increased electrification due to reduction of cost and high energy efficiency of electricity. The study also pointed out that further study is required since it did not consider the cost of network developments due to the geographical distance between areas with large demand and where suitable for renewable integration.

This paper explains the mathematical optimization model to derive the cost optimal deployment of renewables and the corresponding required development of the interconnection, and, by showing a preliminary calculation result, verified that it should be effective to use this model in the study to seek the future of the energy sector.

In recent, smart inverter is focused on as a solution for the problems on voltage and frequency stability caused by mass introduction of PV. Smart inverter has many functions to keep voltage and frequency within the proper range. Its effectiveness significantly depends on which function is activated but it has not been clarified sufficiently. This paper investigates influence on voltage and frequency of combined use of voltage regulation function and frequency regulation function of smart inverter. In the analysis, smart inverters connected to transmission network and ones connected to distribution network are modeled separately, and appropriate function activation in each network is investigated. Results of simulation study shows that precedence setting in the combined use of multiple function sometimes makes interference among them and deteriorates performance of smart inverter. The dependencies on the performance of introduction in transmission and distribution network by the analysis, and importance of selection of activating function considering introduction amount in transmission and distribution network is recognized.

Forecasting the electricity spot market price is important in planning efficient power generation and demand schedules. It has become common for electric power utilities to use several commercial products of market price forecasts in their daily operations. Therefore, this study proposes a method of combining forecasts to use these products efficiently and improve forecasting accuracy. The method aims to capture the features of the component models' results considering situations by using open data as explanatory variables. The result estimated as the less prediction error in some situations obtains high confidence during combining forecasts in same situation. To evaluate the effectiveness of the method of combining forecasts, numerical experiments were conducted on all kinds of area price in the Japan Electric Power Exchange. To the best of our knowledge, it is the first paper that conducted forecasts for all nine area prices and reported the forecasting accuracy of the proposed forecasting method. The results indicate that the method of combining forecasts is a promising way to improve the accuracy of each component forecasts.

The voltage regulation in distribution systems becomes more important these days due to the more complicated power flow profile caused by the integration of variable renewable energy sources such as photovoltaic (PV). Step voltage regulator (SVR) and load ratio controlled transformer (LRT) have been mainly used for the voltage regulation conventionally based on decentralized control methods using line drop compensation (LDC) logic for estimation of voltage drop at secondary side of the SVR. Here, it has been required for distribution system operator to determine LDC parameters properly to estimate the voltage drop based on locally measured information. Hence, in this paper, we proposed an advanced LDC logic in which the parameters can be flexibly switched based on the power flow conditions. The effectiveness of the proposed method was verified using 41-node distribution system model with PV.

We used deep neural network technology to estimate the demand and supply curves of the JEPX day-ahead market. We studied relatively moderate market situations to focus on fundamental market characteristics. Weather forecast data are used as inputs and the disclosed clearing price, cleared quantity, and total volume of demand and supply bids were used as training data. No information about the actual curve shapes was disclosed, but the shapes of the estimated curves showed the responsiveness to the disclosed data sets. We evaluated the minimum distance between the estimated curves to the actual clearing price and cleared quantity. The distribution of this indicator showed that almost 90% of test cases were in the 10% error range. This was almost the same level of performance as regression of the disclosed clearing price determined using a support vector and convolutional neural network. The proposed method is inferior in terms of estimation of the clearing price, cleared quantity, and total volume of demand and supply bids, but we believe it can add intuitive understanding about the market situation to support bidding operations using this method with some other regression-based method.

We conduct an axisymmetric, two-dimensional (r-z) magnetohydrodynamic numerical analysis of a large-scale, nonequilibrium disk-shaped MHD generator with a thermal input of 1 GW to examine the influence of a total pressure loss between the hot duct inlet and the supersonic nozzle outlet of the MHD generator on its isentropic efficiency. Numerical results show that the total pressure loss there substantially affects the isentropic efficiency of the MHD generator. Most of the total pressure loss there, which is mainly caused by a self-excited Joule heating, occurs in a localized area from the vicinity of the hot duct outlet to the radial position of the upstream edge of anode. The numerical results also indicate that the isentropic efficiency is improved by widening an anode width toward the upstream side of the supersonic nozzle. This is because the total pressure loss due to the self-excited Joule heating in the supersonic nozzle decreases by widening it.

Wind turbines in the coastal area of the Sea of Japan are frequently damaged by lightning especially in winter. To investigate features of lightning hitting wind turbines in this area, direct observation of lightning current at 27 wind turbines in Japan was carried out during 2008-2012 in the NEDO's project, placing stress on those in the coastal area of the Sea of Japan. 687 lightning current waveforms were recorded, and 99.4% turned out to be upward lightning. 98.7% were recorded during October to April.