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Regular version of the site
Of all publications in the section: 4
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Article
Chertkov M., Backhaus S., V.V.Lebedev. Applied Energy. 2015. Vol. 160. P. 541-551.

The revolution of hydraulic fracturing has dramatically increased the supply and lowered the cost of natural gas in the United States driving an expansion of natural gas-fired generation capacity in many electrical grids. Unrelated to the natural gas expansion, lower capital costs and renewable portfolio standards are driving an expansion of intermittent renewable generation capacity such as wind and photovoltaic generation. These two changes may potentially combine to create new threats to the reliability of these interdependent energy infrastructures. Natural gas-fired generators are often used to balance the fluctuating output of wind generation. However, the time-varying output of these generators results in time-varying natural gas burn rates that impact the pressure in interstate transmission pipelines. Fluctuating pressure impacts the reliability of natural gas deliveries to those same generators and the safety of pipeline operations. We adopt a partial differential equation model of natural gas pipelines and use this model to explore the effect of intermittent wind generation on the fluctuations of pressure in natural gas pipelines. The mean square pressure fluctuations are found to grow linearly in time with points of maximum deviation occurring at the locations of flow reversals.

Added: Oct 27, 2016
Article
Proskuryakova L. N., Kovalev A. Applied Energy. 2015. No. 138. P. 450-459.

There is a widespread assumption in energy statistics and econometrics that energy intensity and energy efficiency are equivalent measures of energy performance of economies. The paper points to the discrepancy between the engineering concept of energy efficiency and the energy intensity as it is understood in macroeconomic statistics. This double discrepancy concerns definitions (while engineering concept of energy efficiency is based on the thermodynamic definition, energy intensity includes economic measures) and use. With regard to the latter, the authors conclude that energy intensity can only provide indirect and delayed evidence of technological and engineering energy efficiency of energy conversion processes, which entails shortcomings for management and policy-making. Therefore, we suggest to stop considering subsectoral, sectoral and other levels of energy intensities as aggregates of lower-level energy efficiency. It is suggested that the insufficiency of energy intensity indicators can be compensated with the introduction of thermodynamic indicators describing energy efficiency at the physical, technological, enterprise, sub-sector, sectoral and national levels without references to any economic or financial parameters. Structured statistical data on thermodynamic efficiency is offered as a better option for identifying break-through technologies and technological bottle-necks that constrain efficiency advancements. It is also suggested that macro-level thermodynamic indicators should be based on the thermodynamic first law efficiency and the energy quality problem may be left to enterprise-level thermoeconomic optimization.

Added: Apr 21, 2014
Article
Kovalev A., Liliana P. Applied Energy. 2015. Vol. 138. P. 450-459.

There is a widespread assumption in energy statistics and econometrics that energy intensity and energy efficiency are equivalent measures of energy performance of economies. The paper points to the discrepancy between the engineering concept of energy efficiency and the energy intensity as it is understood in macroeconomic statistics. This double discrepancy concerns definitions (while engineering concept of energy efficiency is based on the thermodynamic definition, energy intensity includes economic measures) and use. With regard to the latter, the authors conclude that energy intensity can only provide indirect and delayed evidence of technological and engineering energy efficiency of energy conversion processes, which entails shortcomings for management and policymaking. Therefore, we suggest to stop considering subsectoral, sectoral and other levels of energy intensities as aggregates of lower-level energy efficiency. It is suggested that the insufficiency of energy intensity indicators can be compensated with the introduction of thermodynamic indicators describing energy efficiency at the physical, technological, enterprise, sub-sector, sectoral and national levels without references to any economic or financial parameters. Structured statistical data on thermodynamic efficiency is offered as a better option for identifying break-through technologies and technological bottle-necks that constrain efficiency advancements. It is also suggested that macro-level thermodynamic indicators should be based on the thermodynamic first law efficiency and the energy quality problem may be left to enterprise-level thermoeconomic optimization.

Added: Mar 17, 2015
Article
Fedorova E., Афанасьев Д. О. Applied Energy. 2019. Vol. 236. P. 196-210.

Increasing the accuracy of short-term electricity price forecasting allows day-ahead power market participants to obtain a positive economic effect by bidding close to the equilibrium price. However the electricity price time-series is generally infested with extreme values due to high price volatility. This paper discusses the impact of outlier filtering on forecasting accuracy based on a recently introduced seasonal component autoregressive model. We consider such methods of outlier detection (with a priori defined cut-off parameter) as threshold, standard deviation, percentage, recursive, and moving filter on prices. It is shown that such data pre-processing often leads to the forecasting accuracy gain while the error decrease (relative to the approach without filtering) in a number of cases may reach 1.8–1.9% of the average weekly price (in absolute values). For an a priori defined cut-off parameter, the simple threshold and standard deviation filter on prices outperform other considered methods, and yield to the accuracy gain in 63% and 67% of cases, correspondingly. At the same time, in case of the out-of-sample filter parameter grid-optimization all of the methods demonstrate comparable prediction power (equal to the marginal performance). But, practically speaking, such optimization is time-consuming and cannot be carried out on unavailable future data. As an competitive alternative, we propose a combined filter on prices based on a committee machine which uses the results of individual non-optimized algorithms and is not time-consuming, but gives accuracy comparable to the best one obtained for each of the studied electricity markets and leads to forecast gain in 63% of the considered cases.

Added: Mar 9, 2020