Results 1 - 10 of 646
Results 1 - 10 of 646. Search took: 0.019 seconds
|Sort by: date | relevance|
[en] To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability. - Highlights: • A scenario-generation scheme is proposed for the uncertain wind speed and demand. • Two solution approaches for the periodic wind-thermal DED problems are developed. • A heuristic technique is proposed to handle the uncertainty of DED problem. • Both deterministic and stochastic wind-thermal DED problems are solved. • The performances of the proposed approaches are found superior than existing ones.
[en] This paper presents a study on a modified ejector enhanced auto-cascade freezer cycle with conventional thermodynamic and advanced exergy analysis methods. The energetic analysis shows that the modified cycle exhibits better performance than the conventional auto-cascade freezer cycle, and the system COP and volumetric refrigeration capacity could be improved by 19.93% and 28.42%. Furthermore, advanced exergy analysis is adopted to better evaluate the performance of the proposed cycle. The exergy destruction within a system component is split into endogenous/exogenous and unavoidable/avoidable parts in the advanced exergy analysis. The results show that the compressor with the largest avoidable endogenous exergy destruction has highest improvement priority, followed by the condenser, evaporator and ejector, which is different from the conclusion obtained from the conventional exergy analysis. The evaporator/condenser greatly affects the exogenous exergy destruction within the system components, and the compressor has large impact on the exergy destruction within the condenser. Improving the efficiencies of the compressor efficiency and the ejector could effectively reduce the corresponding avoidable endogenous exergy destruction. The exergy destruction within the evaporator almost entirely belongs to the endogenous part, and reducing the temperature difference at the evaporator is the main approach of reducing its exergy destruction. - Highlights: • A modified ejector enhanced auto-cascade freezer cycle is proposed. • Conventional and advanced exergy analyses are performed in this study. • Compressor should be firstly improved first, followed by condenser and evaporator. • Interactions among the system components are assessed with advanced exergy analysis.
[en] We estimate the environmental and public health benefits that may be realized if solar energy cost reductions continue until solar power is competitive across the U.S. without subsidies. Specifically, we model, from 2015 to 2050, solar power–induced reductions to greenhouse gas (GHG) emissions, air pollutant emissions, and water usage. To find the incremental benefits of new solar deployment, we compare the difference between two scenarios, one where solar costs have fallen such that solar supplies 14% of the nation's electricity by 2030 and 27% by 2050, and a baseline scenario in which no solar is added after 2014. We monetize benefits, where credible methods exist to do so. We find that under these scenarios, solar power reduces GHG and air pollutants by ∼10%, from 2015 to 2050, providing a discounted present value of $56–$789 billion (central value of ∼$250 billion, equivalent to ∼2 ¢/kWh-solar) in climate benefits and $77–$298 billion (central value of $167 billion, or ∼1.4 ¢/kWh-solar) in air quality and public health benefits. The ranges reflect uncertainty within the literature about the marginal impact of emissions of GHG and air pollutants. Solar power is also found to reduce water withdrawals and consumption by 4% and 9%, respectively, including in many drought-prone states. - Highlights: • With feasible cost reductions, solar power can provide major environmental benefits. • U.S. electric-sector modeling indicates climate benefits worth ∼2 ¢/kWh-solar. • Further modeling indicates air quality public health benefits worth 1.4 ¢/kWh-solar. • Solar could reduce power-sector water withdrawals and consumption by 4% and 9%.
[en] As an alternative energy source, Jatropha is a promising biomass resource due to its high content of oil contained in the seed. However, after the oil extraction process, more than 50% of initial weight remained as residue. This Jatropha de-oiled cake was considered a valuable feedstock for thermochemical conversion process due to its high volatile matter (73%) and energy content (20.5 MJ/kg). Pyrolysis turned biomass into solid product of biochar, liquid product (bio-oil and aqueous phase), and pyrolysis gas. The effects of pyrolysis temperature under the pressure of 0.69 MPa on the product yields and characteristics were investigated using a bench-scale batch reactor. The gross calorific value of pyrolytic oil was measured to be 35 MJ/kg with high carbon content (71%) and low oxygen content (10%). Phenols and hydrocarbons were the main compounds present in the pyrolytic oil. The heating value of the biochar was also high (28 MJ/kg), which was comparable to the fuel coke. More combustible gases were released at high pyrolysis temperature with methane as a main constituent. Pyrolysis temperature of 500 °C, was determined to be an optimum condition for the mass and energy conversions with 89% of the mass and 77% of the energy recovered. - Highlights: • Pressurized pyrolysis of Jatropha wastes at different temperatures was studied. • Full analysis of biochar, bio-oil and pyro gas at different temperatures were done. • Highest aromatics (32%) and HHV (35 MJ/kg) found in bio-oil at 500 °C. • Large amount of paraffins (C_1_3–C_1_6 range) was found in bio-oil.
[en] A more realistic thermodynamic model of the pumped thermal electricity storage (PTES) system consisting of a Brayton cycle and a reverse Brayton cycle is proposed, where the internal and external irreversible losses are took into account and several important controlling parameters, e.g., the pressure ratio and heat flows of the two isobaric processes in the Brayton cycle, are introduced. Analytic expressions for the round trip efficiency and power output of the PTES system are derived. The general performance characteristics of the PTES system are revealed. The optimal relationship between the round trip efficiency and the power output is obtained. The influences of some important controlling parameters on the performance characteristics of the PTES system are discussed and the optimally operating regions of these parameters are determined. - Highlights: • A cycle model of the Brayton pumped thermal electricity storage system is proposed. • Internal and external irreversible losses are considered. • Maximum power output and efficiency of the system are calculated. • Optimum performance characteristics of the system are revealed. • Rational ranges of key controlling parameters are determined.
[en] The fast growing Revenue Passenger Kilometers and the relatively lagged energy supply of aviation industry impels the airlines to improve energy efficiency. In this paper, we focus on evaluating and analyzing influencing factors for airline energy efficiency. Number of employees and aviation kerosene are chosen as the inputs. Revenue Ton Kilometers, Revenue Passenger Kilometers and total business income are the outputs. Capital stock is selected as the dynamic factor. A new model, Virtual Frontier Dynamic Slacks Based Measure, is proposed to calculate the energy efficiencies of 21 airlines from 2008 to 2012. We verify two important properties to manifest the advantages of the new model. Then a regression is run to analyze the influencing factors of airline energy efficiency. The main findings are: 1. The overall energy efficiency of Malaysia Airlines is the highest during 2008–2012.2. Per capita Gross Domestic Product, the average service age of fleet size and average haul distance have significant impacts on the efficiency score. 3. The difference between full-service carriers and low-cost carriers has no significant effects on airline energy efficiency. - Highlights: • A Virtual Frontier Dynamic Slacks Based Measure is developed. • 21 airlines' energy efficiencies are evaluated. • Malaysia Airlines has the highest overall energy efficiency. • Three explanatory variables have significant impacts.
[en] A hybrid photovoltaic-thermal (PVT) greenhouse solar dryer under forced mode has been proposed and different parameters have been evaluated for different climatic condition of Indian Institute of Technology, New Delhi (28-350 N, 77-120E, 216 m above MSL), India. In the present study, radiation data and ambient air temperature have been taken from IMD (Indian Meteorological Department) Pune. Further, thermal modelling has been done for the PVT greenhouse dryer and different parameter such as crop temperature, greenhouse temperature, outlet air temperature and cell temperature have been calculated by the help of program made on MATLAB 2013a. Fair agreement has been found between theoretical and experimental data with correlation coefficient value (r) and root mean square percentage deviation (e) are 0.92 and 4.64, 0.99 and 0.97, 0.99 and 0.96 for solar cell, greenhouse room and crop temperature respectively. Further, on yearly basis useful thermal energy, useful electrical energy, useful equivalent thermal energy, thermal exergy and overall thermal efficiency have been calculated. Further, embodied energy, energy payback time, CO_2 mitigation and carbon credit earn have also been calculated. It was found that payback time for system is 1.23 and 10 years on the basis of overall thermal energy and overall exergy basis respectively. - Highlights: • Present system is designed for rural area in developing country. • Thermal modelling has been done for dryer analysis with experimental validation. • Energy and exergy analysis have been done for throughout the year. • Embodied energy, payback time and carbon credit earn have been evaluated.
[en] The rapid expansion of renewable energy sources (RES) in many European countries brings about transmission grid expansion requirements. While the transition towards RES-based energy systems is largely perceived positively in general, locally both RES and grid expansion are often confronted with a lack of public acceptance. Using Germany as a case study, we analyse public acceptance of energy infrastructure and its main drivers on local vs. national levels. For this purpose, we conducted a nationally representative survey. Our results show that, on a national level, the acceptance of RES is very high and there is also a high acceptance of grid expansion if it helps to increase the share of RES in the system. In terms of local acceptance problems that may arise for most considered technologies, concerns about landscape modification turn out to be the main driving factor. Moreover, the distance between places of residence and places of energy infrastructure construction is crucial. While acceptance or rejection of technologies will never be entirely tangible or explicable, we find the explicability of rejections to be lowest for new technologies. Finally, age and education turn out to be the most relevant socio-demographic variables determining the participants' acceptance. - Highlights: • A survey to understand drivers of energy technology acceptance was conducted. • Participants were asked to rank energy policy objectives. • Strong differences between acceptance on a national vs. a local level were found. • Landscape modification is the most important factor driving the local acceptance. • Age and education turned out to be the most relevant socio-demographic factors.
[en] Extreme learning machine (ELM), which is a simple single-hidden-layer feed-forward neural network with fast implementation, has been widely applied in many engineering fields. However, it is difficult to enhance the modeling ability of extreme learning in disposing the high-dimensional noisy data. And the predictive modeling method based on the ELM integrated fuzzy C-Means integrating analytic hierarchy process (FAHP) (FAHP-ELM) is proposed. The fuzzy C-Means algorithm is used to cluster the input attributes of the high-dimensional data. The Analytic Hierarchy Process (AHP) based on the entropy weights is proposed to filter the redundant information and extracts characteristic components. Then, the fusion data is used as the input of the ELM. Compared with the back-propagation (BP) neural network and the ELM, the proposed model has better performance in terms of the speed of convergence, generalization and modeling accuracy based on University of California Irvine (UCI) benchmark datasets. Finally, the proposed method was applied to build the energy saving and predictive model of the purified terephthalic acid (PTA) solvent system and the ethylene production system. The experimental results demonstrated the validity of the proposed method. Meanwhile, it could enhance the efficiency of energy utilization and achieve energy conservation and emission reduction. - Highlights: • The ELM integrated FAHP approach is proposed. • The FAHP-ELM prediction model is effectively verified through UCI datasets. • The energy saving and prediction model of petrochemical industries is obtained. • The method is efficient in improvement of energy efficiency and emission reduction.
[en] We analyse the climate implications of producing electricity in large-scale conversion plants using coal, forest slash and municipal solid waste with and without carbon capture and storage (CCS). We calculate the primary energy, carbon dioxide (CO_2) and methane (CH_4) emission profiles, and the cumulative radiative forcing (CRF) of different systems that produce the same amount of electricity. We find that using slash or waste for electricity production instead of coal somewhat increases the instantaneous CO_2 emission from the power plant, but avoids significant subsequent emissions from decaying slash in forests or waste in landfills. For slash used instead of coal, we find robust near- and long-term reductions in total emissions and CRF. Climate effects of using waste instead of coal are more ambiguous: CRF is reduced when CCS is used, but without CCS there is little or no climate benefits of using waste directly for energy, assuming that landfill gas is recovered and used for electricity production. The application of CCS requires more fuel, but strongly reduces the CO_2 emissions. The use of slash or waste together with CCS results in negative net emissions and CRF, i.e. global cooling. - Highlights: • Using slash or waste for energy emits CO_2 from power plants, but avoids CO_2 and CH_4 emissions from forests or landfills. • Using forest slash for energy instead of coal gives robust short- and long-term climate benefits. • Using waste instead of coal gives questionable climate benefits, if the waste would otherwise be landfilled properly.