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Abstract The purpose of this paper is to investigate the influence of blending methods on the co-gasification of petroleum coke and lignite with CO 2 using a thermogravimetric system at 0.1 MPa. The weight loss curves, XRD analysis, SEM images, BET specific surface area, were investigated. It was observed that petroleum coke shows a low reactivity because of the graphitic carbon structure, low catalyst content and small specific surface area. Blending with lignite can get a high reactivity. The co-gasification reactivity was significantly influenced by blending methods. Wet grinding is much effective than dry grinding. Long grinding time made lignite show greater BET specific area. And the sample in long grinding time has more association chances between petroleum coke and AAEM species. The co-gasification reactivity increases linearly with a rise of BET specific area.

Abstract The improvement of thermodynamic efficiency of power plants is of great interest for the whole energy industry. The use of Kalina cycle has a great potential to improve the thermal efficiency of a nuclear power plant. This cycle uses a mixture of ammonia and water as working fluid. In this paper, we discuss the development of an Ammonia-Water mixture Property Code (AWProC). The estimation of the mixture properties are based on the Gibbs free energy functions. The code is verified and validated against experimental data available in the literature and REFPROP code. It is shown that AWProC can accurately estimate the thermodynamic properties of ammonia-water mixtures over a wide range of conditions, including high temperature and pressure regions. The code is then used to investigate the feasibility of applying the Kalina cycle to a typical Pressurizer Water Reactor (PWR) plant as an effective way to improve the plant efficiency. The fundamental of Basic-Kalina (B-K) cycle is described in detail firstly. Then, two modified configurations, Recuperation-Kalina (R-K) and Flash-Kalina (F-K) cycles respectively, are proposed for a typical 1000 MWe PWR. The simulation results indicate that the R-K type cycle can reach about 31.2% efficiency with simple equipment requirements, while the F-K type cycle can reach efficiencies up to about 34.8%, but at the expenses of a slightly more complex design. The present work demonstrates the applicability of the Kalina cycle as a way to improve the thermal efficiency of a nuclear power plant. This concept is meaningful for improving nuclear power plants economic and competitiveness.

Abstract This paper summarises studies undertaken towards the development of a laminated composite aluminium/hexadecane phase change material (PCM) drywall based on previous analytical work. The study also covered the selection and testing of various types of adhesive materials and identified Polyvinyl acetate (PVA) material as a suitable bonding material. For the purpose of comparison pure hexadecane and composite aluminium/hexadecane samples were developed and tested. The test results revealed faster thermal response by the aluminium/hexadecane sample regarding the rate of heat flux and also achieved about 10% and 15% heat transfer enhancements during the charging and discharging periods respectively. Its measured effective thermal conductivity also increased remarkably to 1.25 W/mK as compared with 0.15 W/mK for pure hexadecane. However there was about 5% less total cumulative thermal energy discharged at the end of the test which indicates that its effective thermal capacity was reduced by the presence of the aluminium particles. The study has shown that some of the scientific and technical barriers associated with the development of laminated composite PCM drywall systems can be overcome but further investigations of effects of adhesive materials are needed.

United States Energy Foundation project [G-1111-15134]; Ministry of Education, Philosophy and Social Planning project [12YJAZH056]; China Postdoctoral Science Foundation [20090460202]; National Natural Science Foundation of China [710201139]; Fundamental Research Funds for the Central Universities [2010221040]; National Bureau of Statistics Funds from China [2011LD002]

Researchers have been driven to investigate sustainable alternatives to cement production, such as geopolymers, due to the impact of global warming and climate change resulting from greenhouse gas emissions. Currently, they are exploring different methods and waste materials to enhance the mechanical and physical properties of geopolymer and expand its application range. This review paper offers a thorough analysis of the utilization of various waste materials in geopolymer manufacturing and shows the creative contribution of this research to the development of environmentally friendly cement substitutes. The article covers the properties, durability, and practical applications of geopolymer composites made from various waste binders. It includes a microstructure and chemical analysis. The research findings indicate that geopolymers are an effective cementitious binder substitute for cement in various applications. Additionally, the ecological and carbon footprint analysis highlights the sustainability of geopolymers compared to cement.

Abstract A hybrid charging management system (HCMS) is proposed to solve the nodal under-voltage problem in medium-voltage radial distribution networks caused by the concentrated high-power charging of electric vehicles (EVs), which contains a pre-centralized charging management platform (PCMP) and local charging management controllers (LCMCs) located at fast charging stations (FCSs). The proposed PCMP can formulate half-hourly charging power threshold plans for LCMCs based on a pre-centralized voltage regulation algorithm (PVRA) to prevent concerned nodes from under-voltage. Two sub-strategies, i.e., charging management sub-strategy (CMS) and cooperative control sub-strategy (CCS), are integrated in the LCMS, which can aggregate EVs charging at different FCSs to provide the voltage support service for the concerned node, so that the under-voltage problem of these concerned nodes can be alleviated. The load forecasting error, load uncertainty, and the need of the plug-and-play are considered as well. Finally, an IEEE 69-bus test system is used for simulation verification. Simulation results verify that the HCMS can be used to solve the nodal under-voltage problem caused by centralized high-power charging of EVs.

In this paper, the notion of a cohesive and self-sufficient grid is proposed. Based on a cohesive and self-sufficient virtual microgrid, an active distribution network is optimally planned, and an optimal configuration of demand-side resources, distributed generations, and energy storage systems are generated. To cope with stochastic uncertainty from forecast error in wind speed and load, flexibility reserves are needed. In this paper, the supply relation between flexibility and uncertainty is quantified and integrated in an innovative index which is defined as cohesion. The optimization objectives are a minimized operational cost and system net-ability cohesion as well as self-sufficiency, which is defined as the abilities both to supply local load and to deal with potential uncertainty. After testing the optimal configuration in the PG&E 69 bus system, it is found that with a more cohesive VM partition, the self-sufficiency of VMs is also increased. Also, a case study on uncertainty-caused system imbalance is carried out to show how flexibility resources are utilized in real-time operational balance.

With the development of smart devices and information technology, it is possible for users to optimize their usage of electrical equipment through the home energy management system (HEMS). To solve the problems of daily optimal scheduling and emergency demand response (DR) in an uncertain environment, this paper provides an opportunity constraint programming model for the random variables contained in the constraint conditions. Considering the probability distribution of the random variables, a home energy management method for DR based on chance-constrained programming is proposed. Different confidence levels are set to reflect the influence mechanism of random variables on constraint conditions. An improved particle swarm optimization algorithm is used to solve the problem. Finally, the demand response characteristics in daily and emergency situations are analyzed by simulation examples, and the effectiveness of the method is verified.