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Hydrogen energy is considered one of the main measures of zero carbonization in energy systems, but high equipment and hydrogen costs hinder the development of hydrogen energy technology. The objectives of this study are to quantify the environmental advantages of hydrogen energy through a carbon tax and study the application potential of hydrogen energy technology in a regional distributed energy system (RDES). In this study, various building types in the smart community covered by Japan's first hydrogen energy pipeline are used as an example. First, ten buildings of five types are selected as the research objectives. Subsequently, two comparative system models of a regional distributed hydrogen energy system (RDHES) and an RDES were established. Then, by studying the optimal RDHES and RDES configuration and combining the prediction of future downward trends of fuel cell (FC) costs and energy carbon emissions, the application effect of FC and hydrogen storage (HS) technologies on the demand side was analyzed. Finally, the adaptability of the demand-side hydrogen energy system was studied by analyzing the load characteristics of different types of buildings. The results show that, when the FC price is reduced to 1.5 times that of the internal combustion engine (ICE), the existing carbon tax system can sufficiently support the RDHES in gaining economic advantages in some regions. Notably, when the carbon emissions of the urban energy system are reduced, the RDHES demonstrates stronger anti-risk ability and has greater suitability for promotion in museums and shopping malls. The conclusions obtained in this study provide quantitative support for hydrogen energy promotion policies on the regional demand side and serve as a theoretical reference for the design and adaptability research of RDHESs. [ABSTRACT FROM AUTHOR]

• A TSSP model based on uncertainty is proposed and validated. • The influence of four different uncertainty methods is compared and studied. • The anti-risk ability of different equipment under load uncertainty is analyzed. • BCHP's configuration strategies and trends under different uncertain risks. The energy demand uncertainty is a complex issue for design tasks of building combined cooling, heating, and power (BCHP) systems. To overcome this problem, the characteristics of energy demand uncertainty need to be precisely described by generating massive scenarios. Here, the aim of the research is to study the effects of scenario generation methods on BCHP optimization considering energy demand uncertainty. Four scenario generation methods were compared and studied—two conventional probability-based methods using Monte-Carlo simulation and the Latin hypercube method to sample the basic scenario, as well as simulation-based methods based on building performance simulation. Two optimization models, including an independent scenario optimization (ISO) model and a two-stage stochastic programming (TSSP) model, were used to minimize the total annual cost of the BCHP system. Dynamic time warping (DTW) was applied for investigating differences between scenario generation methods. The results show that Monte Carlo building performance simulation (MCBPS) is the most similar to the basic scenario. The system cost obtained by the uncertainty based optimization method is 1.5% - 3.6% higher than that of the deterministic optimization method. This is mainly due to the capacity increase of peak shaving equipment. From the perspective of anti risk, relatively higher internal combustion engine (ICE) capacity has stronger anti risk ability, which can effectively reduce the total cost of the system. This study can provide a theoretical reference for architectural design and operation research based on uncertainty. [ABSTRACT FROM AUTHOR]

The equipment energy efficiency improvement policy (EEEIP) is one of the important measures of energy conservation and emission reduction in various countries. However, due to the simultaneous implementation of variety policies, the effect of the single policy cannot be clearly reflected. In this paper, a method of identification and evaluation of EEEIP was proposed, and the application was verified by analyzing the example of EEEIP in Japan (Top Runner policy, TRP). Firstly, through the factor decomposition model, this paper studied the energy conservation and emission reduction potential of this policy area in Japan. Then, the TRP was identified by using moving windows and correlation analysis, and the impact of specific equipment in TRP was analyzed. Finally, through the calculation of the rebound effect of the carbon footprint (REC), this paper analyzed the energy consumption and emission reduction effects of TRP in the short-term and whole life cycle. It showed that the policy has a good effect in tertiary industry and transportation, while the effect in residential is poor. For life cycle, the TRP of air conditioning and passenger car can bring better CO2 emission reduction effect, but the emission reduction effect of lighting is basically offset. [ABSTRACT FROM AUTHOR]

• Environmental and economic performances of multi energy system(MES) was evaluated. • The operation models of MES cause the difference of environmental performance. • System environmental performance also affected by the CO 2 emission coefficient. • The price of equipment and energy mainly affect the economic performance of MES. Due to the improvement of people's living standards, uptake of multi-functional household appliances, electricity consumption accounts for a rising ratio in the proportion of primary energy usage. Currently, electricity accounts for more than 50% of primary energy usage and city gas is about 21% of total. The aim of this research focus on improving the efficiency of household energy usage and optimizing the economy of residential energy systems. This paper classified the detail electricity consumption and generation for a Japan typical household with Hybrid Fuel cell and Photovoltaic System (HFPS) based on the history monitored data. Then a simulation model of the Hybrid Heat pump and Photovoltaic System (HHPS) was designed under the same user load for analyzing the energy consumption. Finally, the economy of the two systems is compared and optimized by considering the carbon tax, equipment price and energy price fluctuation. In general, by comparing the environmental and economic performances of both two systems, the results showed that HFPS has a better environmental performance than HHPS, but HHPS presented an economic advantage over HFPS. In addition, with the change of equipment price and the development trend of energy price in the future, the economic performance of HFPS is gradually improved, which is better than HHPS and has more development prospects. The results of this work provide policy guidance for the future Japanese government's promotion of residential fuel cell systems, while choosing the optimal path between user energy characteristics and power contract scheme. [ABSTRACT FROM AUTHOR]

• Multi-source models help solve energy prediction for different types of buildings. • LSTM networks with multi-kernel maximum mean difference build multi-source prediction models. • Multi-source transfer learning models achieve better average prediction performance compared with single-source models. • Dynamic time warping relatively accurately measures the similarity between building energy consumption datasets. Transfer learning can use the knowledge learned from the operating data of other buildings to facilitate the energy prediction of a target building. However, most of the current research focuses on the transfer of a single source building of the same type or from the same region. A single source domain produces domain shift due to the difficulty of aligning the distribution with the target domain. To address this problem, this paper proposes a novel multi-source transfer learning energy prediction model based on long short-term memory (LSTM) and multi-kernel maximum mean difference (MK-MMD) domain adaptation. This model was used for the short-term energy prediction of different types of buildings lacking historical data. In addition, dynamic time warping (DTW) was used to select the source domain. Multiple multi-source models and corresponding single-source models were compared on a collection of buildings in the Higashida area of Fukuoka Prefecture, Japan. On the experimental datasets, the results showed that DTW relatively accurately measured the similarity between building energy consumption datasets. Compared with the results of the single-source transfer learning models, the multi-source transfer learning models achieved better average prediction performance, and their mean absolute percentage error (MAPE) improved the prediction accuracy by 6.88–15.37%. [ABSTRACT FROM AUTHOR]