Refining Solar-power-generation Plans to Achieve Stable Power Supply by Predicting Total Solar Irradiance

1. Introduction

As social and economic systems have been developed, technologies and services that respond to the diverse needs of individuals have been created, making it possible for people to lead rich social lives. However, the problems of global warming and climate change have become more apparent, and carbon neutrality has become a major theme.

In September 2021, NTT Group formulated a new environment and energy vision, called “NTT Green Innovation toward 2040,” which aims to simultaneously achieve zero environmental impact and economic growth by “reduction of environmental impact through business activities” and “creation of breakthrough innovation.”

Our major efforts to achieve carbon neutrality include “increased use of renewable energy” and “lower energy consumption with the Innovative Optical and Wireless Network (IOWN)” [1]. To expand the use of renewable energy, we will not only procure renewable-energy power sources from other companies but also develop and promote renewable energy such as photovoltaics (PV) within our own group and collaborate with partners and NTT Group companies [2].

To expand the use of renewable energy, it is essential to provide a stable supply of electricity. Renewable-energy sources are difficult to plan, that is, it is difficult to predict when, where, and how much power will be generated because the amount of power generated depends on natural conditions.

Electric-power producers had been able to generate stable income from the sale of electricity under the feed-in tariff (FIT) system^*1 without having to create a renewable-energy-generation plan, so they have been able to promote the development and use of renewable energy. However, with the introduction of the feed-in premium (FIP) system^*2, electric-power producers are now required to create a renewable-energy-generation plan and trade their power in the electricity market and other markets on the basis of that plan.

A variety of renewable-energy sources, such as hydropower, wind, and solar power, are available; however, in Japan, the use of PV is expanding, especially from the perspective of economic efficiency and environmental suitability (suitable locations). With PV, power generation (output) and total solar irradiance are strongly correlated; therefore, to draw up accurate power-generation plans, it is necessary to accurately measure and predict total solar irradiance.

We first broadly discuss stable power supply, which is fundamental in regard to expanding the use of renewable energy. We then discuss the measurement and correction technology for total solar irradiance we developed to improve PV-output forecasting.

2. The necessity for a stable supply of electricity

Simply put, to achieve a stable supply of electricity, the supply and demand of electricity must be perfectly matched to prevent power outages. If supply exceeds (or, conversely, subceeds) demand, various electrical products will malfunction and, in the worst case, power outages will occur. Accordingly, electricity utilities that support the electric-power system are obligated to make utmost efforts to ensure a stable supply of electric power. Japan has introduced a planned price coincidence system, under which power producers and retail electricity suppliers are obligated to match planned and actual electricity consumption in 30-minute increments per time block, and the system is operated so that the generation plan and demand plan coincide as a whole.

The electric-power system shown in Fig. 1 is composed of a power producer, retail electricity supplier, and general electricity transmission and distribution company. The obligation to match plans and actual results is imposed on the power producer and retail electricity supplier, and if a discrepancy (imbalance) between their power-generation and demand plans and actual results unavoidably occurs, the general electricity transmission and distribution company will use its regulatory power to eliminate the imbalance. In this case, however, depending on the amount of imbalance, settlement is required at a later date, and the cost tends to be higher than when supply and demand are adjusted in an electricity market such as the spot (one-day-ahead) market or the hourly (same-day) market. It is therefore essential that the power producer and retail electricity supplier formulate accurate plans to avoid imbalances to reduce business risks and maximize profits.

Fig. 1. Overview of an electric-power system.

The imbalance caused by the power producer is generally called the generation imbalance, while the imbalance caused by the retail electricity supplier is called the demand imbalance. As shown in Table 1, there are two types of imbalances, and the type depends on one of two imbalance situations. For example, if the actual power generated falls short of the generation plan, this situation is called a shortage imbalance, and the general electricity transmission and distribution company will make up the shortage and bill the customer for the amount of electricity generated at a later date. Conversely, if the actual power generated exceeds the generation plan, this situation is called a surplus imbalance, and the general electricity transmission and distribution company will purchase the surplus amount of electricity. In a surplus imbalance, the power producer appears to incur no loss; however, they do not know the purchase price until a later date and, in any case, they must pay compensation to the general electricity transmission and distribution company in accordance with the adjusted purchase amount. As mentioned above, it is thus essential for power producers and retail electricity suppliers to formulate accurate plans to avoid imbalances, reduce business risks, and maximize profits.

Table 1. Types of imbalance.

3. Challenges in predicting PV output

To draw up accurate power-generation plans, it is necessary to accurately predict the amount of power generated. Renewable-energy sources, also known as naturally variable power sources, are subject to large fluctuations in power output due to factors such as weather conditions, seasons, and time of day. As for one of those power sources, PV output varies greatly with the intensity of the total solar irradiance falling on the surface of the PV panels. Basically, PV output can be accurately predicted if, in addition to the intensity of total solar irradiance, the effects of shadows caused by topography and buildings, the characteristics of the PV panels and power-conditioning system (PCS)^*3, and any water droplets and dirt on the surface of the PV panels are known. However, current technologies and services for PV output forecasting have large errors, and each company is working to reduce these errors by incorporating various innovations in forecasting technology, establishing power-generation balancing groups, and reducing errors through renewable-energy aggregation. Efforts to control the generation imbalance by introducing grid storage batteries are also being made [3]. However, the smoothing effect of these measures is limited, and even if grid storage batteries are used, it is necessary to ensure that they have enough capacity to absorb the imbalance. From the standpoint of a power producer, it is a prerequisite that the generation imbalance be minimized to achieve cost benefits; therefore, it is essential to reduce errors in forecasted PV output, thus promote the introduction and expansion of renewable-energy sources.

Errors in forecasted PV output are mainly due to errors in measured and forecasted total solar irradiance, so it is important to reduce those errors. Total solar irradiance is generally measured using meteorological satellites, and as shown in Fig. 2(a), there is a significant error between ground-based and satellite measurements. If the ground and satellite measurements agreed perfectly, all the points would be plotted on the red line; however, in reality there is a considerable error. Since total solar irradiance measured at ground level is the true value, it is desirable to be able to use the ground-level measurements. However, only about 50 meteorological observatories across Japan are equipped to measure ground-level total solar irradiance, which makes it impossible to measure total solar irradiance uniformly over a wide area. Technologies and services that use sunshine-duration data obtained from AMeDAS (Automated Meteorological Data Acquisition System) of the Japan Meteorological Agency to pseudo-measure total solar irradiance are available; however, total solar irradiance and sunshine duration are different parameters, and there are only 690 AMeDAS stations nationwide [4] that can measure sunshine duration. It is thus difficult to obtain accurate total solar irradiance for any given location.

Two main models are used to predict total solar irradiance: numerical and statistical. Numerical models predict future total solar irradiance by calculating elements such as solar and atmospheric irradiation, cloud coverage, and aerosol concentration on the basis of physical laws. Statistical models predict future total solar irradiance by analyzing the relationship between total solar irradiance and other variables on the basis of historical measurement data and satellite images. Ensemble prediction models—which combine numerical and statistical models—and, more recently, deep-learning prediction models have been proposed [5]. However, total solar irradiance predicted by the currently widely used numerical forecasting models is generally calculated after predicting various meteorological factors such as cloud cover and atmospheric pressure, and as shown in Fig. 2(b), prediction errors in those factors lead to large errors in predicted total solar irradiance. The figure shows the relationship between the ground-measured total solar irradiance and the predicted (next day) values provided by weather companies. The errors shown here appear directly as errors in predicted PV output. To reduce errors in predicted PV output, we therefore constructed a high-quality PV-output prediction model by reducing measurement errors in total solar irradiance. The model reduces the prediction error in total solar irradiance and enables highly accurate prediction of PV output.

Fig. 2. Example of measurement errors and forecast errors of total solar irradiance.

4. Measurement and correction technology for total solar irradiance by using PV output

As shown in Fig. 3, this measurement and correction technology consists of two methods: estimation of measured total solar irradiance and correction of predicted total solar irradiance. These methods reduce the error between measured and predicted total solar irradiance.

Fig. 3. Overview of our measurement and correction technology for total solar irradiance.

5. Future developments

To make renewable energy a main power source, it is necessary to ensure a stable supply of electricity and reduce business risks. It is thus essential to reduce the power-generation imbalance that affects renewable energy. To reduce the error in predicted PV output, we proposed a method for estimating measured total solar irradiance and a method for correcting predicted total solar irradiance to reduce the error between the measured and predicted values of total solar irradiance, which is the main cause of error in predicted PV output. We confirmed the effectiveness of these methods in the field. However, we found that when the errors in predicted total solar-irradiance values provided by weather companies are large, correction with the proposed method was less effective in reducing the errors.

With that issue in mind, we plan to investigate new prediction technologies that do not rely on predicted total-solar-irradiance values provided by weather companies. As well as researching forecasting of power generation, we are also pursuing research and development on using digital twins to optimize the entire supply-demand matching process by taking into account the supply and demand of electricity to enable a stable supply of electricity in a decarbonized society. Thus, we are contributing to the creation of a sustainable society.

References

[1]	T. Yoshimatsu, Y. Yagi, K. Matsuo, and K. Asakura, “NTT Group’s New Environmental and Energy Vision ‘NTT Green Innovation toward 2040,’” NTT Technical Journal, Vol. 33, No. 12, pp. 44–47, 2021 (in Japanese).
[2]	Y. Sumida and T. Kirimoto, “NTT Anode Energy’s Smart Energy Business,” NTT Technical Journal, Vol. 33, No. 12, pp. 56–59, 2021 (in Japanese).
[3]	Agency for Natural Resources and Energy, “Energy White Paper 2021,” pp. 172–190, 2022 (in Japanese). https://www.enecho.meti.go.jp/about/whitepaper/2022/pdf/3_3.pdf
[4]	Ministry of Land, Infrastructure, Transport and Tourism, “The Role of AMeDAS” (in Japanese). https://www.mlit.go.jp/common/001043841.pdf
[5]	A. M. Assaf, H. Haron, H. N. A. Hamed, F. A. Ghaleb, S. N. Qasem, and A. Albarrak, “A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting,” MDPI, Appl. Sci., Vol. 13, No. 14, p. 8332, 2023. https://doi.org/10.3390/app13148332