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Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model's robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel "policy-versus-policy" strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF. [ABSTRACT FROM AUTHOR]

As the global energy landscape transitions towards a more sustainable future, hydrogen has emerged as a promising energy carrier due to its potential to decarbonize various sectors. However, the economic competitiveness of hydrogen production by water electrolysis strongly depends on renewable energy source (RES) availability. Thus, it is necessary to overcome the challenges related to the intermittent nature of RESs. This paper presents a comprehensive techno-economic analysis of complementing green hydrogen production with grid electricity. An evaluation model for the levelized cost of hydrogen (LCOH) is proposed, considering both CO2 emissions and the influence of RES fluctuations on electrolyzers. A minimum load restriction is required to avoid crossover gas. Moreover, a new operation strategy is developed for hydrogen production plants to determine optimal bidding in the grid electricity market to minimize the LCOH. We evaluate the feasibility of the proposed approach with a case study based on data from the Kyushu area in Japan. The results show that the proposed method can reduce the LCOH by 11% to 33%, and increase hydrogen productivity by 86% to 140%, without significantly increasing CO2 emission levels. [ABSTRACT FROM AUTHOR]

As an unconventional clean resource with huge reserves and a wide distribution, natural gas hydrates (NGHs) have good application prospects. However, due to limited understanding and available production technology for NGHs, there is still a large gap between current production tests and commercial exploitation. A breakthrough in reservoir stimulation technologies is key to realizing the industrialization of NGHs in the future. Through a comprehensive summary of hydrate production test cases in Japan and China, this paper highlights the difficulties in the transformation of weakly-consolidated reservoirs. By systematically reviewing the theory and technology of hydrate reservoir transformation and engineering applications, this paper elucidates in detail the technical principles and mechanisms of several available stimulation technologies for weakly-consolidated reservoirs, and assesses the feasibility of their application to increase the production of NGHs. Existing problems and challenges are summarized and future prospects are discussed. Finally, suggestions are put forward for research and development of transformation technology for weakly-consolidated NGHs reservoirs in the future. [ABSTRACT FROM AUTHOR]

This paper studies the distribution of emergency relief for electric vehicles (EVs), which considers energy saving, multi-depot, and vehicle routing problems with time windows, and the named energy saving-oriented multi-depot vehicle routing problem with time windows (ESMDVRPTW). Our aim is to find routes for EVs such that all the shelter demands are fulfilled during their time windows and the total cost traveled by the fleet is minimized. To this end, we formulate the ESMDVRPTW as a mixed-integer linear programming model. Since the post-disaster transportation network contains a large number of vertices and arcs composed of vertices, we propose a two-stage approach to solve the ESMDVRPTW. The first stage is to obtain the minimal travel cost between any two vertices in real-time on a post-disaster transportation network using the proposed Floyd algorithm combined with the neighboring list (Floyd-NL algorithm). In the second stage, we develop the genetic algorithm (GA) incorporating large neighborhood search (GA-LNS), which determines the delivery scheme of shelters. Simulation results of the MDVRPTW benchmark illustrate that the performance of the GA-LNS is better than GA, simulated annealing (SA) and tabu search (TS). Finally, case studies are constructed on two real cases acquired from the OpenStreetMap (OSM) generated by the Quantum Geographic Information System (QGIS) in Ichihara city, Japan, and the test results of case studies show the effectiveness of the proposed two-stage approach. [ABSTRACT FROM AUTHOR]

The Japan Electric Power Exchange (JEPX) provides a platform for the trading of electric energy in a manner similar to more traditional financial markets. As the number of market agents increase, there is an increasing need for effective price-forecasting models. Electricity price data are observed to exhibit periods of relatively stable, i.e., low-magnitude, low-variance prices interspersed with periods of higher prices accompanied by larger uncertainty. The price data time series therefore exhibits a temporal non-stationarity characteristic that is difficult to capture with typical time series modeling frameworks. In this paper, we implement models for the occurrence of price spike events where spikes are defined as observing prices above a predefined threshold set here at 25 JPY/kWh. This value corresponds to about the 90th percentile of observed prices during peak trading periods. The price spikes time series is observed to be rare events that occur in clusters. We therefore propose to model the data as a Hawkes process whereby the occurrence of a spike event increases the probability of observing more spikes in the period immediately following a price spike event. We test two variations of the classical Hawkes model: the first variation models the change in the magnitude of the underlying intensity as a function of the magnitude of the price spike while the second variation models the change in the decay rate of the underlying intensity as a function of the magnitude of the price spike. An analysis of the performance of the models based on the mean absolute error (MAE) of the spike occurrence probability, a weighted accuracy index, and the Matthews correlation coefficient (MCC) metrics shows the effectiveness of the variable magnitude variation of the Hawkes model in generating short-term forecasts of the occurrence of price spike events. The modified Hawkes model especially outperforms other candidate models as the length of the forecasting horizon increases. [ABSTRACT FROM AUTHOR]

After the Fukushima nuclear accident in Japan, whether the structural safety of the nuclear power plant (NPP) can be guaranteed under an earthquake has been of wide concern. Automatic seismic trip systems (ASTS) have been deployed in NPPs. There are generally two units or more double units on the one NPP site, and the vibration energy of the structure can certainly affect its adjacent structures through the soil, and there is energy transfer and conversion between adjacent structures. At present, the distance between two reactors of NPPs of different reactor types is generally 100–200 m (this distance is referring to the distance between the centers of two reactors, which is slightly different for different reactor types). In the past, seismic instruments were used in the ASTS and the setting of shutdown threshold, so the impact of only one unit was considered in the structural analysis of NPPs, and the interaction of two reactors through site conditions was not considered. In addition, as the site conditions of NPPs become more and more complex, it is necessary to consider the impact of one reactor structure on another reactor structure through the soil on the same site under an earthquake. In order to analyze the influence of structure–soil–structure interaction (SSSI), a three-dimensional refined finite element model of NPPs is established in this paper. The soil–structure interaction (SSI) is considered by using viscoelastic boundary. The seismic responses of different positions of the raft foundation and NPP structure, under the conditions of considering SSI effect (one reactor) and SSSI effect (two reactors), are compared. The influence of SSSI effect on the seismic responses of the raft foundation and NPP structure is revealed. It is proposed that SSSI effect should be reasonably considered according to the site conditions for the structural analysis of NPPs for the NPP ASTS. [ABSTRACT FROM AUTHOR]

Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement. [ABSTRACT FROM AUTHOR]

Managing energy-producing companies as well as managing the entire energy sector in the light of legal and environmental requirements requires a new vision, mission, and strategy. The paper analyses the strategies of energy enterprises. It is not enough now to produce energy and deliver it at appropriate, acceptable prices to consumers; it must be generated with the least negative impact on the environment. To achieve that plan, companies should cut the carbon intensity of their products by 20% by 2030, 45% by 2035, and 100% by 2050, using a baseline of 2016. To compared to 1990 levels, the greenhouse gas emission reduction target for 2030 should be increased to 55%. Bioenergy will represent 18% of total final energy consumption in 2050. Additionally, this requires the development of a long-term strategy that can force companies to completely reorganize their production or start a new operation and activities. A low-cost strategy or a competition strategy are insufficient, and it is necessary to look for new strategies that combine adaptation to the requirements of the external environment with the use of innovative solutions. The article analyzes the possibilities of implementing an innovative strategy based on biomass, especially bamboo biomass. The reduction in CO2 emissions of bamboo, taking into account life cycle emissions, can reach up to 85%. The novelty is to show the possibility of producing electricity by a large-scale power plant solely based on bamboo biomass on the example of a power plant located in the Tokushima prefecture, Japan. Another novelty is the fact that this article draws attention to the problem of burning bamboo in a power plant. The problem is that, as a result of burning bamboo, the clinker settles quite quickly. The study analyzes the selected ingredients for co-firing, which improve the combustion parameters of bamboo biomass (e.g., blended 20% bamboo with 80% pine or 30% bamboo with 70% tree bark). The importance of this research lies in the fact that it shows new innovative solutions in the energy sector that will help to achieve emission reductions. In addition, the article proposes to use eco-innovations and pay attention to eco-efficiency. Such solutions are an opportunity for ecological development through the use of bamboo as a fuel, which is classified as a renewable energy source by power plants. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading is gaining attention as a technology to effectively handle already existing distributed energy resources (DER). In order to manage a large number of DER, it is necessary to increase the number of P2P energy trading participants. For that, designing incentives for participants to engage in P2P energy trading is important. This paper describes a user-centric cooperative mechanism that enhances user participation in P2P energy trading. The key components of this incentive for participants to engage in P2P energy trading are described and evaluated in this study. The goal of the proposal is to make it possible to conduct economic transactions while reflecting the preferences of the traders in the ordering process, making it possible to conduct transactions with minimal effort. As a case study, the Higashi-Fuji demonstration experiment conducted in Japan verified the proposed mechanism. In this experiment, 19 households and 9 plugin hybrid vehicles (PHV) were evaluated. As a result, the study confirmed that prosumers were able to sell their surplus electricity, and consumers were able to preferentially purchase renewable energy when it was available. In addition, those trades were made economically. All trades were made automatically, and this efficiency allowed the users to continue using the P2P energy trading. [ABSTRACT FROM AUTHOR]

There was an unprecedented growth of SHP (small hydropower) in Japan during recent years because the government has provided a high FIT (USD 0.32/kWh) for SHP development projects of less than 200 kW. The public and private sectors are committed to harnessing this natural energy to achieve the renewable energy goal as well as to improve local communities social and economic conditions. This paper will discuss about renewable energy initiatives by a food corporation enterprise (FCOOP) based in Fukuoka prefecture of Japan to reduce their net carbon emissions. A detailed approach for social enterprises-based SHP development has been discussed which will be a role model concept for other social enterprises in Japan and in the world that are willing to reduce CO2 emissions. Also, this kind of project connects urban stake holders with local community where resources are available and it leads to development of the local community living standards. As a demonstrated example, a case study of a 20 kW SHP installed in Japan by a FCOOP has been discussed. [ABSTRACT FROM AUTHOR]

Hydrogen is currently receiving significant attention and investment as a key enabler of defossilised global energy systems. Many believe this will eventually result in the international trade of hydrogen as a commodity from countries with significant renewable energy resources, for example New Zealand and Australia, to net energy importing countries including Japan and Korea. Japan has, since 2014, been actively exploring the components of the necessary supply chains, including the assessment of different hydrogen carriers. Public/private partnerships have invested in demonstration projects to assess the comparative merits of liquid hydrogen, ammonia, and organic carriers. On the supply side, significant projects have been proposed in Australia while the impending closure of New Zealand's Tiwai Point aluminium smelter at the end of 2024 may provide an opportunity for green hydrogen production. However, it is also evident that the transition to a hydrogen economy will take some years and confidence around the timing of supply and demand capacity is essential for new energy infrastructure investment. This paper reviews the expected development of an export market to Japan and concludes that large scale imports are unlikely before the late 2020s. Comparative evaluation of the energy efficiency of various hydrogen carriers concludes that it is too early to call a winner, but that ammonia has key advantages as a fungible commodity today, while liquid hydrogen has the potential to be a more efficient energy carrier. Ultimately it will be the delivered cost of hydrogen that will determine which carriers are used, and while energy efficiency is a key metric, there are other considerations such as infrastructure availability, and capital and operating costs. [ABSTRACT FROM AUTHOR]

Organisations attempt to contribute their share towards fighting the climate crisis by trying to reduce their emission of greenhouse gases effectively towards net zero. An instrument to guide their reduction efforts is internal carbon pricing. Next to choosing the right pricing tool, defining the exact value of an internal carbon price, especially against the background of potential regulatory external carbon prices, and assessing its impact on business units' energy systems poses a challenge for organisations. The academic literature has so far not examined the impact differences of an internal carbon price across different countries, which this paper addresses by using an optimisation model. First, it analyses the energy system cost increase of a real-world facility based on an internal carbon price compared to a potential regulatory carbon price within a country. Second, we evaluate the energy system cost increase based on an internal carbon price across different countries. The results show that with regard to internal carbon prices the additional total system cost compared to potential external carbon prices stays within 9%, 15%, and 59% for Germany, Japan, and the United Kingdom, respectively. The increase in the energy system cost in each country varies between 3% and 93%. For all countries, the cost differences can be reduced by allowing the installation of renewables. The integration of renewables via energy storage and power-to-heat technologies depends on the renewable potentials and the availability of carbon capture and storage. If organisations do not account for these differences, it might raise the disapproval of internal carbon prices within the organisation. [ABSTRACT FROM AUTHOR]