• Energy Research
• 2021-2025close
• 13. Climate action close
• Southeast University close

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations.

Since the official launch of the Belt and Road Initiative (BRI) in 2013, China and the BRI countries have been working for the implementation of certain environmental measures to make the BRI project green and clean. For this purpose, China and the BRI countries have planned to implement certain environmental measures. Although China can efficiently implement these measures, most of the BRI countries face technological deficiencies and lack of proper environmental plannings. To tackle these deficiencies, the BRI countries can import environmental technology from China. Moreover, they can communicate their environmental protection policies with China for better policy guidance. The current study, therefore, aims to examine whether the BRI countries’ import of environmental technology from China can reduce carbon emissions in these countries. Moreover, it also examines that whether these countries should follow the environmental policy and the import policy of China or they should follow the six European countries (EU-6) with minimum carbon emissions intensity. This study considers a sample of 88 selected BRI countries (BRI-88) for the period 2001–2019. The results obtained with β convergence (based on the panel quantile regression model) suggest that not all the BRI countries but only the BRI countries with relatively higher carbon emissions intensity can significantly reduce their average carbon emissions intensity by importing environmental technology from China. Moreover, BRI countries can follow the environmental policy of China which is more feasible for them. However, regarding the environmental goods import policy, BRI countries can follow both China and the EU-6.

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

With the rapid development of building technology, transparent envelope is more and more widely used, which makes the indoor environment of buildings more and more affected by solar radiation. However, the effects of solar radiation are not included in the PMV model. The Corrected Predicted Mean Vote (CPMV) model considering solar radiation was previously proposed and verified in northern China. In order to expand the applicability of the CPMV model to the hot summer and cold winter (HSCW) zone of southern China, a field study was conducted in an office building in Nanjing. A total of 686 valid questionnaires were recovered during the surveys in two summers in 2019 and 2020. The results show that the evaluation value of CPMV is highly consistent with the actual thermal sensation vote (TSV) when the corrected operative temperature is below 30 °C. However, when the corrected operative temperature is above 30 °C, the CPMV value is higher than TSV, because it underestimates the tolerance of human body to the hot environment in Nanjing. The thermal neutral temperature is 26.12 °C (CPMV) and 26.28 °C (TSV) respectively, which is higher than that in winter and summer in northern China. This study fills the blank in the application of CPMV model in southern China. The CPMV model can accurately evaluate the thermal comfort of indoor environment affected by solar radiation, which is worthy of promotion and application to other types of buildings and areas.

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

Thermo-chemical conversion of carbonaceous wastes such as tyres, plastics, biomass and crude glycerol is a promising technology compared to traditional waste treatment options (e.g. incineration and landfill).

Abstract Municipal solid waste (MSW) combustion in CFB boiler is a promising waste-to-energy technology. However, inorganic minerals in MSW can be transformed into particulate matters (PM) through a series of ash partitioning mechanisms. These generated PM not only can deposit on heat transfer surface and significantly reduce the boiler efficiency, but also might be released to the atmosphere and cause serious health issues for human beings. This work investigated the particulate matter formation mechanism in a full-scale CFB boiler, which incinerates shoe manufacturing waste (SMW) with 576 t/d treatment capability. During the experimental work, PM samples near superheaters were directly sampled through a water-cooled isokinetic probe. Particle size distribution showed that there exists an accumulation mode in submicron particle region. The compositional analysis indicated that ultrafine particles with particle size smaller than 0.1 μm are mainly composed of alkali chlorides. The supermicron particles are mostly composed of calcium. Furthermore, heavy metal contents (Pb and Cr) are fairly enriched in ultrafine particles. Overall, this work sheds the light on the ash partitioning behavior during SMW combustion in circulating fluidized bed, which can provide some insights on the design and optimization of waste-to-energy CFB boiler.

Abstract Post-combustion CO2 capture with solid sorbents is a promising technology. The feature of temperature swing adsorption for such process requires sufficient gas-sorbent contact, sustained driving force for adsorption and adjustable sorbent circulation between reactors. This, however, is hard to fully realize by traditional single-regime fluidized reactors. In view of this, we proposed a novel two-stage integrated bubbling-transport bed reactor and examined its CO2 capture performance using K2CO3/Al2O3 sorbents. The results show that the optimal adsorption temperature ranges from 60 °C to 100 °C, much wider than that of traditional reactors. The CO2 capture efficiency increases with both sorbent circulation rate and desorption temperature. Better CO2 capture performance was observed when using air instead of CO2 as the desorption gas. Nevertheless, the difference is negligible as the desorption temperature exceeds 200 °C. Stage I is the region where CO2 adsorption mainly takes place, owing to the over-adsorption of water vapor. This limits the CO2 capture performance of the entire system. To this regard, water vapor step-feeding was carried out, acting as an in-situ activation of sorbents. It enhances the maximum CO2 capture efficiency from 87% to 96%, meanwhile showing a strong anti-interference ability for water vapor fluctuation even at a fixed total water vapor. The 24 h continuous test verifies the superiority of the present system as the CO2 capture efficiency maintains around 93% and the desorption CO2 purity keeps above 98%.