Abstract

Abundant solar energy resources were existed in Gannan Tibetan area where passive solar houses are widely used in this area. However, due to their functions are single and unstable, the heating load in winter is very large. In winter, Tibetan residents still need to rely on coal and dry cow dung for heating. In order to solve this problem, a thermoelectric methane supply system was established on the basis of the original passive solar house by utilizing the abundant solar energy and biomass resources in Tibet. This system can provide users with winter heating, domestic electricity, domestic hot water and gas to meet different levels of energy demand. Through the whole heating season of test of the system, the results show that the system is stable, and the system heat supply accounts for 94.4% of the total building heat demand. For most of the time, the indoor temperature is higher than 14 ℃, the cumulative output of biogas is 95 m3, and the average daily output of biogas digester is 0.76 m3. During the test period of PV power generation subsystem, the daily generation capacity was 3.1 kWh. The system has good economy and environmental protection. The use and promotion of this system has changed the backward energy utilization mode, improved the energy utilization efficiency, reduced the use of fossil fuels, improved the quality of life for residents, and improved the natural environment.

Abstract

The high proportion renewable energy access is an important feature and trend of future power system development. With large scale access of wind power and photovoltaics, high proportion renewable energy will bring strong uncertainty, making the safety and stability characteristics increasingly complex and difficult to control, thus bringing large impact to the operation of traditional power system. As an important feature of the system operation mode, power flow will be deeply affected by the power supply structure change. Aiming at the problem that the traditional power system analysis is difficult to cope with the change of operation mode caused by renewable energy access, this paper proposes a framework of system operation assessment and adjustment: first build a set of assessment indicator system based on flow rationality, and then adopts artificial intelligence method to learn the training adjustment strategy, and finally realizes the intelligent adjustment of system operation mode which meets the operation requirements of safety, high efficiency, economy and intelligence in the case of high proportion renewable energy access.

Abstract

To evaluate potential infrastructure modifications in the energy sector that are required to mitigate climate change, interdisciplinary approaches are essential. Since the discussion is largely shaped by technical aspects, our objective is to combine a macroeconomic and sociological approach to exceed this perspective. As a result, we generated new insights by using the example of gas infrastructure modifications in Germany. These results would not have been possible with a single discipline’s analysis. Focusing on relevant stakeholders, we examined five aspects that we regard as crucial determinants of future developments. By doing so, we discussed the value of interdisciplinary analysis in the context of assessing the transformation towards a low carbon economy. In conclusion, combining different disciplines’ approaches revealed to be promising in at least two ways: first, to generate new insights and second, to provide feasible policy recommendation. To sum up, our paper exposes that interdisciplinary synergies have a large potential to strengthen research on factors that foster or hinder the transformation towards a low-carbon economy but also to foster social science in energy research.

Abstract

False data injection attack (FDIA) invades the automatic generation control (AGC) system and degrades the control performance, which may cause unstable operation of power system. Fast and accurate detection can help to reduce the impact of attacks. This paper presents a novel detection method, combining local predictor (LP) and support vector machine (SVM), for the FDIA of AGC system. The effects of different types of cyber attacks on AGC system are analyzed. The LP is applied to identify the local pattern of each point of historical data, and it extracts the information in a high dimension space with accurate predictions. The similar data obtained from LP are adopted to train the SVM, and the LP-SVM algorithm is presented to detect the attacks of AGC system. Simulation studies undertaken on a single-area AGC system reveal that the LP-SVM method outperforms traditional SVM and naive Bayes (NB).

Abstract

The self-preservation effect is crucial for hydrate storage, however, may also hinder the decomposition and even results in severe plug of production well for hydrates exploitation below freezing point. In this study, a high pressure micro-differential scanning calorimeter (HP μ-DSC) was used to study the effect of sediment particle size and type on the self-preservation of CH4 hydrate with low saturation. It was found that there is still a strong self-preservation effect, and the stability of CH4 hydrate degrades with the reduced sediment particle size as a whole. The CH4 hydrates, in different sediments, have significant differences of self-preservation phenomena. CCD camera and in-situ raman spectroscopy was used to further investigate the influence mechanism. The experimental results indicate that the ice and bentonite synergistically enhanced the self-preservation effect of CH4 hydrate, which should be paid great attention to the gas production process of hydrate below freezing point in the future. This is the first investigation on the sediment influence on self-preservation effect of methane hydrate. Our findings are of significance for both risk prevention of hydrate exploitation and a better understanding of self-preservation effect.

Abstract

With climate change threatening the status of affairs, governments have initiated counter-measures aimed at severely reducing energy consumption. This necessitates increasing the energy efficiency in the real estate sector. However, real estate owners often refuse to carry out profitable energetic retrofitting investments, a phenomenon referred to as the energy efficiency gap. To better understand which regional factors may influence individual investment decisions, a conjoint analysis comprising 104 policy makers, commercial property owners and private home owners was conducted, allowing to infer implicit preferences for four countybased attributes with respect to energetic retrofitting. Based on these results, an indicative energetic retrofitting attractiveness score is estimated for all 401 German counties. Furthermore, a comparison of the responses of the different stakeholder groups reveals that policy makers, commercial property owners and private home owners differ in their decision-making regarding energetic retrofitting.

Abstract

Wind turbine pitch systems play an important role in wind energy extraction. Such systems are known to suffer high failure rates resulting in long downtime maintenance. Consequently, industries wish to utilize failure prognostic techniques to schedule effective maintenance in order to reduce losses. A cost-effective method that analyses existing data collected from the built-in sensors in the wind turbine (WT), such as the supervisory control and data acquisition (SCADA) data is presented in this paper. Information such as wind speed, power generation and subsystem measurements are collected for every 1 second resulting in large datasets. By combining a SOM clustering technique and radial basis function neural network (RBFNN), patterns are revealed while reducing redundant information. The performance of the WT failure prediction system was evaluated and tested using 37 sensors from SCADA data for a single WT in Levenmouth, Scotland. A failure state classification accuracy of between 96%-99% was observed.

Abstract

This paper reported numerical simulation and optimization of nanostructured Cu2O/TiO2 pn heterojunction solar cells. This is with a view to providing an optimized cell efficiency to aid experiment and development of high-efficiency metal oxide solar cells. The solar cells equations were modelled and thereafter theoretically validated on the nanostructured metal oxides. The model working points include a room temperature of 300K, input power of 1000W/m2 using illumination of AM1.5 lamp, and under varied thickness of 0.5 µm to 10.0 µm for the absorber layer (Cu2O) and 0.05 µm to 6.0 µm for the buffer layer (TiO2). The modelled solar cell exhibits a short-circuit current (Jsc) of 24.0764 A, the open-circuit voltage (Voc) of 1.0486 V, the fill factor (FF) of 63.20 %, and the efficiency (η) of 1.6% at absorber layer thickness of 500 nm and buffer layer thickness of 50 nm. Also, the defect density was obtained for the solar cells. This will serve as a theoretical guide for laboratory research on the improvement of efficiency of Cu2O metal oxide solar cells. This will open a new frontier for modelling of metal oxide based thin film solar cells especially Cu2O thin films solar cells. This is a booster in the quest to develop affordable and sustainable energy by encouraging more research in solar cells technologies in low-income countries.

Abstract

Demand for energy storage facilities and non-fossil fuel powered vehicle has furthered the research into Liion batteries. In this work, an electrochemical Lithiumion battery model was built taking into account the electrochemical reactions is presented. The mathematical model is validated against experimental data for LCO cell operating under 1 C electrical load at room temperature. Electrode thickness and the porosity of the positive electrode were varied to determine the influence on the battery capacity.

Abstract

A novel superstructure is developed for the biochemical conversion of macroalgae to mixed alcohols (MAs). Microalgae production processes as well as wastewater treatment networks are integrated into the process synthesis framework to improve the environmental performance of the MAs manufacturing process. Based on the superstructure, techno-economic mixed integer linear programming model was formulated. The objective function was the maximization of the net present value (NPV). The results indicated that biofuel production from macroalgae is economically viable, at minimum ethanol selling price (MESP) of $1.26 $/gal. Furthermore, the optimal design has achieved a 90% reduction in CO2 emissions (CE). Sensitivity analysis indicated that the selling price of heavier alcohols and purchasing price of macroalgae are the most sensitive parameters to MESP.