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Abstract Distributed energy system becomes increasingly popular due to high efficiency and low pollution emissions. When it operates following the electricity load, thermal energy storage system can be used to accommodate surplus cooling and heating and improve the energy efficiency. This study investigates the energy and economic performance of thermal storage systems for surplus cooling and heating in distributed energy system, considering the impacts of climates, building combinations and cooling/heating supply systems. Results show that the thermal storage system can improve the primary energy efficiency and is more useful when residential buildings adopt centralized heating and decentralized cooling systems. By varying climates and building combinations, the thermal storage system can improve the primary energy efficiency by 0.28%–3.69%. The economic performance is not promising with a long payback period. The main reason is that the overall utilization rate of the surplus energy is low (less than 45%), especially when the cooling/heating load is low continually during spring and autumn. Additionally, the effect of static and dynamic thermal storage model is analyzed. Results show that the static model would over-estimate the performance of the thermal storage system significantly and the dynamic model is recommended.

China’s steel industry is an energy-intensive sector. Synergistic reduction of emissions of CO2 and air pollutants (SO2, NOx, and PM2.5) in the steel industry has an important practical significance for climate change and air pollution control. According to the CO2 emission reduction intensity targets (CERO) and air pollutant emission targets (PERO) for 2020 and 2030, 28 types of energy-saving and emission reduction technologies (20 types of carbon reduction technology and eight types of air pollution end-of-pipe technology) were selected for examination, and a two-stage dynamic optimization model with collaborative implementation of PERO and CERO was built to assess the near future (2015–2020) and long-term (2020–2030) implementation plans for synergistic emissions reduction of CO2 and air pollutants. The results show that in the near future, the implementation of PERO will have a greater synergistic effect on CO2 emission reduction. CO2 emission reduction under PERO in 2020 will be 97 million tons (Mt) higher than that of CERO, an increase of nearly 26%. However, the effects of implementing CERO are better in the long run. Under CERO, the emission reductions of SO2, NOx, and PM2.5 in 2030 are 2.44 Mt, 1.47 Mt, and 0.86 Mt, respectively, and 7%, 4%, and 5% higher than the implementation of PERO. As far as marginal abatement cost is concerned, in the near future, the marginal abatement costs of CO2 and air pollutant equivalents are 1.06 yuan/kgCO2 and 133 yuan/kg pollution equivalent (pe) under PERO, which are 23% and 11% lower than that of CERO, while in the long run, the marginal abatement costs of CO2 and pollutant equivalents under CERO are 0.025 yuan/kgCO2 and 2.73 yuan/kgpe, about 96% and 95% lower than that of PERO.

Bamboo was a popular material substituting for wood, especially for one-off commodity in China. In order to recover energy and materials from waste bamboo, the basic characteristics of bamboo pyrolysis were studied by a thermogravimetric analyzer. It implied that the reaction began at 190∼210 °C, and the percentage of solid product deceased from about 25% to 17% when temperature ranged from 400 °C to 700 °C. A lab-scale fluidized-bed furnace was setup to research the detailed properties of gaseous, liquid and solid products respectively. When temperature increased from 400 °C to 700 °C, the mass percent of solid product decreased from 27% to 17% approximately, while that of syngas rose up from 19% to 35%. When temperature was about 500°C, the percentage of tar reached the top, about 31%. The mass balance of these experiments was about 93%∼95%. It indicated that three reactions involved in the process: pyrolysis of exterior bamboo, pyrolysis of interior bamboo and secondary pyrolysis of heavy tar.

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. This article therefore provides data that can be used to create a simple zero order energy system model for Mauritania, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost ) for 2020-2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

The global increase in energy demand and exponential exhaustion of fossil recourses has favored the development of new systems of electricity production. Photovoltaic solar energy is undoubtedly one that has the highest application in housings, due to its simplicity and easy implementation. In this work, a new prototype of photovoltaic solar tracker with Arduino platform was developed. Feedback control system that allows carrying out solar tracking with two axes using a stepper motor and linear actuator was established through an electronic circuit based on photodiodes. Moreover, real construction of the prototype was carried out, where the effectiveness of the design and its capacity to draw a maximum benefit of an incident radiation can be observed, placing the panel perpendicularly to the received energy and improving its performance for its application in future installations in housings. Results obtained from the comparison between the developed prototype and a static panel oriented according to the latitude of the area, show about 18% energy gain.

In order to solve the problem of dynamic reconfiguration of distribution network connected with distributed generation (DG) and electric vehicle (EV), a dynamic reconfiguration method of distribution network based on time series symbolized segmentation is proposed. Firstly, the daily load curve of the distribution network accessed to distributed energy and electric vehicles is predicted. The time series of load is symbolized and the segmentation effect is evaluated by information entropy to determine the time division scheme. Then the model is established with the minimum daily operating loss cost as the objective function, and the distribution network is dynamically reconfigured according to the time division. The particle swarm optimization algorithm (PSO) based on branch group search is proposed to solve the model. Finally, the simulation calculation is carried out by using the IEEE33 node system as an example. The results show that the proposed method can reasonably divide the load time series, so that the daily opHrating loss cost of the distribution network after dynamic reconfiguration is reduced.

Abstract This study newly introduces a complementary cooperative algorithm considering generative adversarial network (GAN)-Conditional Latent Space (CLS) combined with bidirectional long short-term memory (BLSTM) for improved and efficient lithium-ion rechargeable battery state prediction. The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. In terms of battery state prediction, the combination of two methods (GAN-CLS and LSTM) surely provides more improved and efficient rechargeable battery state prediction in contrast to conventional state predictors. The procedure of this study is as follows. First, we propose methods to enhance the data from battery charge/discharge by converting prepared data to images; then, the GAN-CLS method is used to generate corresponding battery data from previous images. Subsequently, the generated data is used to train the BLSTM model. Finally, the trained model is used to predict the battery state. By various experiments and verification, it is concluded that the proposed study can be a good solution for rechargeable battery state prediction (reduction of the time cost 50 times in modeling and 20 times in train/test, provision of a more accurate prediction mean square error (MSE) smaller than 0.0025 and the average MSE less than 0.0013).

The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world’s energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs.