• Energy Research
• Open Access close
• 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.

AbstractUsing a low supply water temperature in heating conditions and a high water temperature during cooling can increase energy efficiency, use renewable energy sources, and provide a comfortable and healthy indoor climate. High temperature cooling and low temperature heating is achieved by reducing temperature difference in heat trans er and energy transportation process. The losses in temperature difference can be classified into three types: by heat/moisture exchange; by energy transportation through air/water circulation; by indoor terminal that releases heat/cooling to indoor condit oned space. The air handling process of HVAC system and indoor terminals are the key factor of reducing temperature differen e.Aiming at the losses in HVAC system, Annex 59, titled High Temperature Cooling & Low Temperature Heating in Buildings, is a new international cooperative work under the framework of International Energy Agency (IEA) Energy in Buildings and Communities (EBC). This paper introduc s the background, scope, objective, structure and deliverables of Annex 59. Annex 59 will try to present a new perspective and a new concept to analyze HVAC system in buildings. The goal of the Annex is to build up new concept of analyzing HVAC system from the perspective of reducing mixture loss and transfer loss and th n apply it in high temperature cooling and low temperature heating system.

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.

Remanufacturing has received extensive attention due to its advantages in material and energy saving, emission reduction and is often considered a viable approach for the realization of a circular economy. Remanufacturing ecological performance reflects the ability of an enterprise to balance economic and environmental benefits. Therefore, evaluating the remanufacturing ecological performance is of great significance for leveraging the benefits of remanufacturing and promoting the concept of sustainability and the implementation of a circular economy in the industry. To this end, a set of data-driven techniques, i.e., data envelopment analysis, R clustering and grey relational analysis, are deployed to analyze and evaluate the ecological performance of a remanufacturing process. The effectiveness and feasibility of the proposed method are illustrated via a case study of remanufacturing for hydraulic cylinder and boom cylinder. Furthermore, a number of critical factors, e.g., energy-saving rate, remanufacturing process cost and rate of remanufacturing, for end-of-life products have been identified as the key drivers impacting the remanufacturing ecological performance. So as to improve remanufacturing ecological performance, optimizing production technology, implementing lean remanufacturing and raising public acceptability over remanufacturing products are effective measures. The research results of the present work can provide support for remanufacturing enterprises to guide and improve their ecological performance and formulate better development strategies.

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.

Abstract Energy crisis and environment damage are the two worldwide urgent problems and building energy plays an important role in the energy and environment field. To reduce heating and cooling energy consumption in buildings, a novel solar assisted hybrid power gas heat pump system is proposed. The novel system runs in five working conditions and applies solar energy combined with battery pack to guarantee the engine always works in its economic zones. Because of the complexity of photovoltaic panels in environment effect, the life cycle assessment method is introduced to evaluate the whole system’s energy and environment effect. According to different photovoltaic ratios, simulation shows that the application of solar panels achieves a significant improvement in energy consumption while also causes a large mount in environment damage. At last the environmental payback time is put forward and the best environment payback time for the photovoltaic hybrid power gas heat pump system is 13.4 years at a photovoltaic ratio of 40%.

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).