The purpose of this paper is to evaluate the swing option price written on the underlying asset of JEPX spot price. The swing option price is calculated by following three steps; (i) modeling JEPX spot price process taking into account the features of mean-reverting, seasonality and spikes, (ii) deriving risk-neutral measure from TOCOM electricity forward curve, and (iii) executing Least-squares Monte Carlo simulation. The calculation result is shown in several graphs which focus on the combination of swing type, number of swing rights and option exercise price. Moreover, this paper analyzes the sensitivity of swing option price to the spot price trend, volatility and spike frequency. Swing option has already been introduced in power purchase agreements between power generating companies and power retail companies in Japan. Therefore, it is considered important for these companies to find a suitable reference price for negotiation. This paper will show a theoretical way to calculate the reference price.
Expanding renewable energy is essential to realize decarbonization. Increase of capability to balance electricity demand and supply makes further diffusion of renewable energy possible. Part of photovoltaics comes to the end of purchase period of feed-in tariff from 2019 in Japan. It is an opportunity for retail electricity providers to widen supply source, but it carries a risk of imbalance settlement. The risk is expected to increase when imbalance system is revised in future. Therefore, it is significant to ensure measures to offset the imbalance caused by prediction error of photovoltaic generation. Some previous studies have analyzed imbalance reduction using CHP with renewable energies, but there are few studies focused on aggregation of home PV and fuel cell for imbalance control. Here we developed energy demand-supply model considering supply from aggregated home solid oxide fuel cell (SOFC) to evaluate its contribution to imbalance compensation. We found that imbalance can be reduced and balance of payments can be improved through output control of SOFC. The effect increases if incentive index of imbalance fee is risen. In addition, we found that SOFC can contribute to greenhouse gas emission reduction by both substitution of grid electricity and support of stable supply from PV. This result indicates value of SOFC from in terms of stabilization of energy system, economic efficiency of retail electricity providers and climate change mitigation.
Battery is a key technology in the transition to a low-carbon and resilient energy system. Recently in Japan, residential PV battery system is considered as one of the most promising applications; however, its impacts had not been fully assessed. This study evaluates the effects of residential PV battery system for end-users under various conditions on geological, technological and socio-economic factors. Regional diversity is considered by using data on residential power consumption and PV power generation that were collected from over 40 thousand all-electric houses in Japan. Scenario analysis and sensitivity analysis are performed to evaluate the effects of uncertain parameters on the system’s performances. Simulation results suggest that economic benefits of the system can be improved by changing battery operation mode at the end of purchase period for PV under Feed-in Tariff (FiT) scheme and the benefits should vary depending on the region. The results also indicate that PV self-consumption rate is over 50% when charging the battery with surplus power. Sensitivity analysis results suggest that the unit prices of grid electricity and the purchasing price of surplus power after FiT scheme should have large influences on the economic benefits of the system.
Methanation, or methane synthesis, attracts the energy sector in the context of sector integration and climate change mitigation.
This study presents a techno-economic assessment on synthetic methane in Japan employing an electricity and city gas supply
model. This model, formulated as a linear programming problem, explicitly considers a carbon recycling system, including
carbon capture, water electrolysis and Sabatier reaction processes. The electricity and city gas sectors in this model are
temporally disaggregated, balancing hourly consumption and supply for a year, to incorporate the intermittent output of solar
and wind power (Variable Renewable Energy = VRE) as well as hourly CO2 generation profiles of thermal power plants.
Simulation results imply that cost reductions of VRE as well as high carbon prices, such as 75% cost reduction of VRE (from
the level in 2014) and 1000 US$/tCO2, would be crucial to boost the deployment of methane synthesis in Japan. Significant
amount of VRE capacity would be necessary to decarbonize the both sectors. The results also suggest that water electrolysis
and methanation in VRE rich regions, combined with inter-regional methane transportation to demand centers, could be a costefficient
option to promote synthetic methane.
The purpose of this research is to prepare energy consumption data of houses as an evaluation standard for the analysis of the effect of introduction of regional home energy management technologies. For that purpose, we used the micro data of household CO2 statistics to create a energy consumption intensity for housing, and aggregated the primary energy consumption status of households in Tokyo by region. The intensity estimated for each region in Tokyo is 80–90% of the national average and it seems to be appropriate as a value based on the social attributes of the local residents. It was estimated that the total housing in Tokyo consumes 374,237 TJ. This is 12.3% of the national total energy consumption and 19.2% of the central region consumption in the comprehensive energy statistics, which is almost the same as the population ratio. By region, the consumption composition ratio was high in Setagaya Ward (6.8%), Nerima Ward (5.2%), and Hachioji City (4.7%).
In Japanese electricity market, Feed in tariff (FIT) contracts for residential solar Photovoltaic systems (PV systems) start to expire from 2019. For the retail electricity providers, those non-FIT PV systems are attractive sources to procure from the viewpoint of decarbonization and cost reduction. However, imbalance risk caused by the system output projection error is the barrier to be overcome. In this study, firstly, a simple projection method of surplus energy from those residential PV systems are developed. Secondly, imbalance management method using remotely controlled residential Heat pump water heater are proposed. Thirdly, the imbalance management system was developed and applied for actual 9 household in Kyusyu area for performance evaluation. Fourthly, based on the empirical research, the operational method for Heat pump water heater was modified so as to improve the performance. Finally, economic impact on retail electricity providers by applying the developed system are evaluated. The result showed that the system can reduce the overall procurement cost through better imbalance risk mitigation and appropriate demand control of the Heat pump water heater.
In general, demand response programs (DRs) can be classified into the price-based DR and the incentive-based one, and we can expect that the latter will be easily accepted by consumers as compared to the former. This is because the incentive-based DR brings benefits both power suppliers and consumers. However, the incentive-based DR has still plenty room for discussion on rebate level setting. This paper presents a design method of the incentive-based DR using a problem framework of social welfare maximization (SWM). In the proposed method, first, the electricity trade between the suppliers and the consumers is represented as the SWM problem. Next, expectable decrement of the consumers’ surplus, which is caused by cooperation to the DR request, is calculated as the minimum rebate level. In contrast, expectable increment of the suppliers’ surplus is calculated as the maximum rebate level. By these calculations, we can set appropriate range for the rebate level in the target DR. Afterwards, the upper limit of adjustable demand is estimated on the basis of decrement of the suppliers’ surplus by the incentive payment. Through numerical calculations and discussions on their results, usefulness of the authors’ proposal is verified.
We are facing a great trend of energy transition from fossil fuel to renewable energy in the world, and companies should play a key role in de-carbonization. In the Energy Transition period, it is important to examine what brings companies to encourage the transition from a financial and business perspective. This paper aims to clarify the characteristics of aggressive companies about energy transition by applying machine learning methods in the Tokyo Stock Exchange; both quantitative and qualitative analysis of the company’s financial/business data were examined. The k-means++, which is a method of clustering, was used for the quantitative analysis, and TF-IDF, which is a method of quantifying the importance of words in documents, was applied to the qualitative analysis. The results showed that companies who worked on energy transition positively (1) earned normal or slightly below financial scores, (2) had larger electricity indirect CO2 emission than direct CO2 emission, and (3) were particularly aware of “Strategies” and “Own brand names” in their business plans. It was also shown that newly introduced CO2 indexes successfully captured characteristics of companies from CO2 emission perspective.
The stock of heating, ventilating, air-conditioning (HVAC) system and water heating system and adopted energy saving measures have significant influences on energy demand of Japanese commercial building stock. This study estimated the composition of the Japanese commercial building stock in terms of the configuration of HVAC system and water heating system and the adoption of energy conservation measures in the year 2013 and 2030. The result implied that the proportion of centralized HVAC systems will decrease, whereas that of electricity-driven decentralized HVAC systems and electricity-driven water heating system and the number of adopted energy conservation measures will increase. Future change in stock of these building system will have a significant impact on energy demand of the Japanese commercial building stock.
Attention has been focused on efforts to promote energy saving actions by utilizing various data regarding energy use of households. When promoting energy-saving behavior, analysis and classification of households’ equipment ownership status will be able to be used for the personalization of energy-saving tips such as energy-saving measures and home appliance replacement in order to promote behavior change effectively. In this paper, we compared the accuracy of methods for classifying equipment ownership status using the national survey of carbon dioxide emissions from residential sector. We employed four classification methods for comparison: binary logistic regression, decision tree, random forest and XGBoosting. We also evaluated the importance of explanatory variables by using permutation test, which remove variables’ feature by randomizing the values of each variable. We find that machine learning methods such as Random Forest and XGBoosting generate relatively higher classification accuracy. Furthermore, these methods show less degradation of classification accuracy when important explanatory variables are permuted from the full model.
Researches have been done on systems that use heat pump water heaters and other appliances to compensate for PV imbalances. In such systems, pinpoint solar irradiance forecast is needed for each PV facility in order to carry out the planning of electricity supply and demand. However, there are still few studies that cover pinpoint solar irradiance forecast. Therefore, in this study, using the forecasted and measured solar irradiance data from 21 locations in Miyama City, the accuracy of pinpoint solar irradiance forecast has been verified and analyzed from several different perspectives. We confirmed that smoothing effect exists even within small areas. The relationship between season, lead time, data release time and forecast accuracy has been investigated. We also found out that Laplace distribution is better suited than normal distribution to represent the forecast error distribution.