• Energy Research
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Abstract Propane (C3H8) is being considered as an alternative refrigerant, besides being used as an alternative fuel, because of its low Global Warming Potential and zero Ozone Depletion Potential. Using blends of C3H8 with CO2 as refrigerants, diminishes the fire safety concerns in case of accidental leak of this flammable substance in refrigeration applications. This paper reports on the effects of CO2 on laminar burning speed and flame instability of C3H8/air blends at elevated temperatures and pressures. The flame structures were investigated in a Schlieren system. The laminar burning speeds of C3H8/CO2/air mixtures were measured in a spherical chamber and were fitted by a power-law mathematical correlation. The one-dimensional flame code from Cantera with kinetic model was also used to predict laminar burning speed. The high-speed photography showed that CO2 inhibits the flame instability because of its hydrodynamic and diffusional-thermal effects. Results showed that the laminar burning speed decreased with increasing CO2 mole fraction in the mixtures and that CO2 promotes the flame stability. The high temperature C3H8 oxidation was governed by the reaction of H + O 2 = O + OH . The effects of CO2 on laminar burning speed were mainly determined by the reaction of CO 2 + H = CO + OH and the high energy capacity (specific heat) of CO2.

Abstract This paper uses quantile regression to demonstrate how electricity price distributions are linked to fundamental supply and demand variables. It investigates the California electricity market (zone SP15) for selected trading hours using data from January 8, 2013 to September 24, 2016. The approach quantifies a non-linear relationship between the fundamentals and electricity prices, just as predicted by the merit order curve. Natural gas, greenhouse gas allowance prices and load all have a positive effect on electricity prices, with the effect increasing with the quantiles. In contrast, solar production and wind production both have a negative effect on electricity prices. The effect of solar production increases with quantiles, whereas the effect of wind production decreases with quantiles. This paper also includes a stress testing case study in which a producer faces the risk of high solar and wind production, and investigates the effect on the lower tail of the price distribution. Overall, the results demonstrate how the proposed approach can be a helpful risk management tool for participants in the electricity market.

Abstract The use of compressed air in industry is an important and yet overlooked energy carrier. Although there are different energy-saving measures discussed in the specialized literature, there is little discussion on the energy performance of the production and use of compressed air. This study developed a new approach to assess the energy performance of compressed air systems based on a six-step local energy benchmarking methodology. The methodology includes an energy management procedure to monitor and control the electricity consumption and sustain the energy performance of compressed air systems in time. The procedure monitors the production and use of compressed at plant and at manufacturing section levels based on the real-time monitoring of relevant variables to calculate energy performance indicators, energy baselines, and CUSUM charts. Monitoring the consumption of compressed air at the section level in a case study reduced the demand between 11 and 47%. While electricity consumption to produce compressed air at the plant level reduced by an estimated 23%. This approach permits the rapid detection of inefficiencies in the production and demand sides of the compressed air system, highlighting inefficiencies that are frequently hidden in the total electricity consumption of manufacturing plants.

Abstract Using the Bakken shale play as a case study, the previous part of this two-part series demonstrated how small-scale mobile plants could be used to monetize associated or stranded gas effectively. Here, we address the issue of uncertainty in future supply, demand and price conditions. To this end, we modified our multi-period optimization framework to a stochastic programming framework to account for various scenarios with different parameter realizations in the future. The maximum ENPV (expected net present value) obtained was $2.01 billion, higher than the NPV obtained in the previous part. In addition, the value of the stochastic solution was 0.11% of the optimal ENPV, indicating that the flexible nature of mobile plants affords them a great advantage when dealing with uncertainty.

Abstract A CCP (combined cooling and power) system, which integrated a flash-binary power generation system with a bottom combined cooling and power subsystem operating through the combination of an organic Rankine cycle and an ejector refrigeration cycle, was developed to utilize geothermal energy. Thermodynamic and exergoeconomic analyses were performed on the system. A performance indicator, namely the average levelized costs per unit of exergy products for the overall system, was developed to assess the exergoeconomic performance of the system. The effects of four key parameters including flash pressure, pinch point temperature difference in the vapor generator, inlet pressure and back pressure of the ORC turbine on the system performance were evaluated through a parametric analysis. Two single-objective optimizations were conducted to reach the maximum exergy efficiency and the minimum average levelized costs per unit of exergy products for the overall system, respectively. The optimization results implied that the most exergoeconomically effective system couldn't obtain the best system thermodynamic performance and vice versa. An exergy analysis based on the thermodynamic optimization result revealed that the biggest exergy destruction occurred in the vapor generator and the next two largest exergy destruction were respectively caused by the steam turbine and the flashing device.

Abstract This work presents a simultaneous mixed-integer nonlinear programming (MINLP) optimisation model and an efficient iterative solution strategy that can be successfully applied to various heat-integrated water networks (HIWNs), including large-scale problems with a large number of water streams. These problems are highly nonlinear, non-convex and combinatorial. To circumvent such difficulties, including network complexity as well as identifying the roles of water streams in the heat exchanger network (HEN) whether they are hot or cold, a modified convex hull formulation proposed by Ahmetovic and Kravanja [1] is applied. The overall model combines the water network (WN), wastewater treatment network (WTN), heat integration (HI), and heat exchanger network synthesis (HENS) models. This model is iteratively solved in three steps including targeting and design steps. The proposed model and solution strategy are tested on large-scale problems. To the best of our knowledge, the results obtained for all the problems in this paper are better than those reported in the current literature.

Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

Abstract The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l 1 -norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.