Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
The severity of climate change and the urgency of ecological environment protection make\nthe transformation of coal power imperative. In this paper, the relevant policies of coal-biomass cofiring\npower generation are combed, and the technical and economic evaluation of coal-biomass cofiring\npower generation technology is carried out using Levelized Cost of Electricity (LCOE) model.\nThe result is that the LCOE of coal-biomass indirect co-firing power generation project is\nsignificantly higher than that of the pure coal-fired unit, with the LCOE rising by nearly 8%.\nThrough sensitivity analysis, the LCOE will increase by 10.7% when it combusts 15% biomass, and\nincrease by 19.1% when it combusts 20% biomass. The LCOE corresponding to wood chips\nincreased by 5.71% and the LCOE to rice husks decreased by 6.06%. Finally, this paper puts forward\nsome relevant policy suggestions, hoping to provide some reference for the promotion of coalbiomass\nco-firing power generation in China....
An Indian electricity system with very high shares of solar photovoltaics seems to be a\nplausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices,\nand governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal,\ndaily, and other weather-induced variations, could create an economic barrier. In this paper,\nwe analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending\nflexibility to the rapidly increasing electricity demand for air conditioning through either precooling\nor chilled water storage. With an open-source power sector model, we estimated the endogenous\ninvestments into and the hourly dispatching of these demand-side options for a broad range of\npotential PV shares in the Indian power system in 2040. We found that both options reduce the\nchallenges of variability by shifting electricity demand from the evening peak to midday, thereby\nreducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic\nvalue of solar PV, especially at shares above 40%, the level at which the economic value roughly\ndoubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal...........................
For conventional power plants, the integration of thermal energy storage opens up a\npromising opportunity to meet future technical requirements in terms of flexibility while at the same\ntime improving cost-effectiveness. In the FLEXI- TES joint project, the flexibilization of coal-fired\nsteam power plants by integrating thermal energy storage (TES) into the power plant process is\nbeing investigated. In the concept phase at the beginning of the research project, various storage\nintegration concepts were developed and evaluated. Finally, three lead concepts with different storage\ntechnologies and integration points in the power plant were identified. By means of stationary system\nsimulations, the changes of net power output during charging and discharging as well as different\nstorage efficiencies were calculated. Depending on the concept and the operating strategy, a reduction\nof the minimum load by up to 4% of the net capacity during charging and a load increase by up to 5%\nof the net capacity during discharging are possible. Storage efficiencies of up to 80% can be achieved....
In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation\ndata, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic\nminority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is\naffected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall\nclassification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm\npredicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest\nalgorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories\nhas increased from 0% to 100%....
Energy recovery from waste biomass can have significant impacts on the\nmost pressing development challenges of rural poverty and environmental\ndamages. In this paper, a techno-economic analysis is carried out for electricity\ngeneration by using timber and wood waste (T & WW) gasification in\nIceland. Different expenses were considered, like capital, installation, engineering,\noperation and maintenance costs and the interest rate of the investment.\nRegarding to revenues, they come from of the electricity sale and the\nfee paid by the Icelandic municipalities for waste collection and disposal. The\neconomic feasibility was conducted based on the economic indicators of net\npresent value (NPV) and discounted payback period (DPP), bringing together\nthree different subgroups based on gasifier capacities, subgroup a: 50 kW,\nsubgroup b: 100 kW and subgroup c: 200 kW. The results show that total cost\nincreases as the implemented power is increased.....................
Loading....