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Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow. It is very difficult to predict spot prices in Japan, where solar power generation has entered the market. Herein, It is attempted to predict the timing of sudden price changes by using price sensitivity, which will begin to be made public in 2021. The impact of price sensitivity on forecasting will be examined by making other variables general.

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.

Inertia reduction due to inverter-based resource (IBR) penetration deteriorates power system stability, which can be addressed using virtual inertia (VI) control. There are two types of implementation methods for VI control: grid-following (GFL) and grid-forming (GFM). There is an apparent difference among them for the voltage regulation capability, because the GFM controls IBR to act as a voltage source and GFL controls it to act as a current source. The difference affects the performance of the VI control function, because stable voltage conditions help the inertial response to contribute to system stability. However, GFL can provide the voltage control function with reactive power controllability, and it can be activated simultaneously with the VI control function. This study analyzes the performance of GFL-type VI control with a voltage control function for frequency stability improvement. The results show that the voltage control function decreases the voltage variation caused by the fault, improving the responsivity of the VI function. In addition, it is found that the voltage control is effective in suppressing the power swing among synchronous generators. The clarification of the contribution of the voltage control function to the performance of the VI control is novelty of this paper.

The increase in inverter-based resources associated with the increased installation of PV sources is a concern because it reduces the inertia of the power system during peak PV generation periods. As a countermeasure to reduce grid inertia, the addition of pseudo-inertia using virtual synchronous machines can be selected, and PV generation can cooperatively contribute to the stable operation of the power system by using the suppressed output as reserve power. However, few studies have analyzed VSMs that do not use batteries and use PV as a resource (PV-VSM) in simulations, including grid interconnection and solar radiation fluctuations, and it is necessary to clarify the issues and discuss countermeasures. In this study, electromagnetic transient response analysis was applied to a VSM connected to a two-generator system, simulations were performed, and the following findings were reported and countermeasure methods for the problem were proposed. When the PV capacity is insufficient for the output required by the VSM inverter, the PV-VSM control system may become unstable. This is caused by a drop in the capacitor voltage of the DC/DC converter due to insufficient PV output. The limiter control system is designed to address this problem by combining the headroom estimation system with the current limiting algorithm. The proposed limiter control system is validated on solar radiation ramp fluctuations as a test case and found that the system was effective in supressing PV-VSM instability. In our simulation case, the PV-VSM with our limiter control can continue to operate stably even if the PV available power is 0.03 [p.u.] short of the inverter’s reference power by the solar power ramp fluctuation, as long as the inverter installation rate is less than 50%.

Virtual inertia control is a methodology to make inverter-based resources (IBR) behave like a synchronous machine. However, an IBR cannot fully emulate the response of synchronous machine because of its low-current capacity. When the inertial response of an IBR is affected by the current limitation, the synchronization of the synchronous machine simulated virtually inside the IBR controller with the other synchronous generators in the grid is affected, which may cause step-out of the simulated generator. We propose a methodology which can keep the synchronization by modifying internal induced voltage of the simulated generator to follow the system voltage change. The proposal is validated by the simulation using a nine-bus transmission system model including two synchronous generators and a large-scale IBR. The result of the generator trip simulation shows that the proposed method suppresses the phase angle variation while the current is limited, and avoids the instability regarding the synchronism. Furthermore, the impact of the current limitation on frequency stability is also evaluated through the simulation study and it is found that as the amount of output suppression increases, the frequency nadir falls, but the rate-of-change of frequency is hardly affected.

A large number of photovoltaic (PV) power systems have been adopted in Japan after a feed‐in tariff was introduced in 2012. However, PV power generation data from residential rooftop and/or ground‐mounted PV systems, and larger MW‐size PV plants have not been measured accurately in real‐time. This is because PV power monitoring instruments (eg, smart meters) have not collected a sufficient amount of power generation data. In order to realize adequate safety control of electric power systems under high PV‐penetration conditions, it is important to fully understand the temporal and spatial variations associated with PV power generation. In this study, we estimated the PV power generation for a regional area (ie, prefecture or municipality) in terms of PV power installation capacity and satellite‐estimated solar irradiance using a Japanese geostationary satellite, Himawari‐8. The satellite‐derived regionally integrated PV power estimations were validated with reference data provided by electric power companies. The validation results showed that these estimations were comparable to the reference data, provided by the Kyushu Electric Power Company Inc. (Kyushu) and the Tokyo Electric Power Company Inc. (TEPCO). However, the results also identified slight overestimations of PV power in the spring and summer seasons. An advantage of the proposed method is that it does not require land‐based monitoring instruments, which can lead to increased operational cost savings for PV power systems. Furthermore, in consideration of future PV power penetration scenarios, it is suggested that PV power in excess of regional power demands could be generated under the same weather conditions. Regional PV power generation (or prefecture and/or municipality regions) are estimated based on PV system installation capacity and satellite‐estimated solar irradiance by using a geostationary satellite.

To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future.

This study examines the performance of radiation processes (shortwave and longwave radiations) using numerical weather prediction models (NWPs). NWP were calculated using four different horizontal resolutions (5, 2 and 1 km, and 500 m). Validation results on solar irradiance simulations with a horizontal resolution of 500 m indicated positive biases for direct normal irradiance dominate for the period from 09 JST (Japan Standard Time) to 15 JST. On the other hand, after 15 JST, negative biases were found. For diffused irradiance, weak negative biases were found. Validation results on upward longwave radiation found systematic negative biases of surface temperature (corresponding to approximately −2 K for summer and approximately −1 K for winter). Downward longwave radiation tended to be weak negative biases during both summer and winter. Frequency of solar irradiance suggested that the frequency of rapid variations of solar irradiance (ramp rates) from the NWP were less than those observed. Generally, GHI distributions between the four different horizontal resolutions resembled each other, although horizontal resolutions also became finer.

Photovoltaic (PV) power generation is directly effected by global horizontal irradiance (GHI) and has also large variations in spatial and/or temporal scales. For a safety control of an energy management system (EMS), a day-ahead forecast or several hour forecast of solar irradiance by a numerical weather prediction model (NWP) becomes important for a control of reserve capacity (thermal power generation, etc.). In particular, a large forecast error of PV power and/or GHI forecasts has to be prevented in the EMS. The Japan Meteorological Agency (JMA) developed two NWPs with different horizontal resolutions. First one is a mesoscale model with horizontal grid spacing of 5 km and second one is a local forecast model with that of 2 km. The two NWPs have been used as an operational model in JMA. In this study, GHI forecasts obtained from the two models are validated and conducted a case study for large forecast error (outlier events) case of GHI.