77 resultados para solar aided power generation (SAPG)


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Fossil fuel based power generation is and will still be the back bone of our world economy, albeit such form of power generation significantly contributes to global CO2 emissions. Solar energy is a clean, environmental friendly energy source for power generation, however solar photovoltaic electricity generation is not practical for large commercial scales due to its cost and high-tech nature. Solar thermal is another way to use solar energy to generate power. Many attempts to establish solar (solo) thermal power stations have been practiced all over the world. Although there are some advantages in solo solar thermal power systems, the efficiencies and costs of these systems are not so attractive. Alternately by modifying, if possible, the existing coal-fired power stations to generate green sustainable power, a much more efficient means of power generation can be reached. This paper presents the concept of solar aided power generation in conventional coal-fired power stations, i.e., integrating solar (thermal) energy into conventional fossil fuelled power generation cycles (termed as solar aided thermal power). The solar aided power generation (SAPG) concept has technically been derived to use the strong points of the two technologies (traditional regenerative Rankine cycle with relatively higher efficiency and solar heating at relatively low temperature range). The SAPG does not only contribute to increase the efficiencies of the conventional power station and reduce its emission of the greenhouse gases, but also provides a better way to use solar heat to generate the power. This paper presents the advantages of the SAPG at conceptual level.

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Although the thermodynamic advantages of using solar energy to replace the bleed off steam in the regeneration system of Rankine cycle coal fired power stations has been proven theoretically, the practical techno/economic feasibility of the concept has yet to be confirmed relative to real power station applications. To investigate this concept further, a computer modelling software “THERMSOLV” was developed by Deakin University researchers, together with the support of the Victorian power industry and Australian Research Council (ARC). This newly developed software simulates the steam cycle to assess the techno/economic merit of the solar aided concept for various power station structures, locations and local electricity market conditions. Two case studies, one in Victoria Australia and one in Yunnan Province, China, have been carried out to show the application of the software. This paper reports the structure and functions of the software, and the results of the two case studies.

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Solar-aided power generation (SAPG) is capable of integrating solar thermal energy into a conventional thermal power plant, at multi-points and multi-levels, to replace parts of steam extractions in the regenerative Rankine cycle. The integration assists the power plant to reduce coal (gas) consumption and pollution emission or to increase power output. The overall efficiencies of the SAPG plants with different solar replacements of extraction steam have been studied in this paper. The results indicate that the solar thermal to electricity conversion efficiencies of the SAPG system are higher than those of a solar-alone power plant with the same temperature level of solar input. The efficiency with solar input at 330 °C can be as high as 45% theoretically in a SAPG plant. Even the low-temperature solar heat at about 85 °C can be used in the SAPG system to heat the lower temperature feedwater, and the solar to electricity efficiency is nearly 10%. However, the low-temperature heat resource is very hard to be used for power generation in other types of solar power plants. Therefore, the SAPG plant is one of the most efficient ways for solar thermal power generation.

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Although the thermodynamic advantages of using solar energy to replace the bled off steam in the regeneration system of Rankine cycle coal fired power stations has been proven theoretically, the practical techno/economic feasibility of the concept has yet to be confirmed relative to real power station applications. To investigate this concept further, computer modelling software “THERMSOLV” was specifically developed for this project at Deakin University, together with the support of the Victorian power industry and Australian Research Council (ARC). This newly developed software simulates the steam cycle to assess the techno/economic merit of the solar aided concept for various power station structures, locations and local electricity market conditions. Two case studies, one in Victoria Australia and one in Yunnan Province, China, have been carried out with the software. Chapter one of this thesis defines the aims and scope of this study. Chapter two details the literature search in the related areas for this study. The thermodynamic concept of solar aid power generation technology has been described in chapter three. In addition, thermodynamic analysis i.e. exergy/availability has been described in this chapter. The “Thermosolv” software developed in this study is detailed in chapter four with its structure, functions and operation manual included. In chapter five the outcomes of two case studies using the “Thermosolv” software are presented, with discussions and conclusions about the study in chapters 6 and 7 respectfully. The relevant recommendations are then made in chapter eight.

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This paper firstly expounds that the reheat-regenerative Rankine power cycle is a suitable cycle for the parabolic trough collector, a popular kind of collector in the power industry. In a thermal power cycle, the higher the temperature at which heat is supplied, the higher the efficiency of the cycle. On the other hand, for a given kind of collector at the same exiting temperature, the higher the temperature of the fluid entering the collector, the lower the efficiency of the collector. With the same exiting temperature of the solar field and the same temperature differences at the hottest end of the superheater/reheater and at the pinch points in the heat exchangers (e.g., the boiler) in the cycle, the efficiencies of the system are subject to the temperature of the fluid entering the collector or the saturation temperature at the boiler. This paper also investigates the optimal thermal and exergetic efficiencies for the combined system of the power cycle and collector. To make most advantage of the collector, the exiting fluid is supposed to be at the maximum temperature the collector can harvest. Hence, the thermal and exergetic efficiencies of the system are related to the saturation temperature at the boiler here.


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Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

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Randomly orientated electrospun poly(vinylidene fluoride) nanofiber membranes were directly used as active layers to make mechanical-to-electrical energy conversion devices. Without any extra poling treatment, the device can generate high electrical outputs upon receiving a mechanical impact. The device also showed long-term working stability and ability to drive electronic devices. Such a nanofiber membrane device may serve as a simple but efficient energy source for self-powered electronics.

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Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

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In this study, we have demonstrated that randomly-oriented electrospun PVDF nanofiber nonwovens can be used directly as an active layer to generate electrical power with a voltage output as high as 4 volt and current 4 micoramp scales on a small nonwoven piece. This discovery may provide a simple, efficient, cost-effective and flexible solution to self-powering of microelectronics for various purposes.