• Energy Research
• Closed Access close
• US close
• DE close

Abstract The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.

Abstract The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

Phasor-based frequency measurement techniques have very good steady-state performance, but their dynamic performance is not well documented. This paper analyzes a phasor-based frequency measurement method that considers the effect of dynamic frequency, and proposes a method to improve the dynamic performance of the phasor-based frequency measurements. Simulation results verify the effectiveness of the proposed method. Results of laboratory experiments are presented to show the performance of the proposed method compared to frequency measurements obtained from commercial phasor measurement units (PMUs).

Phasor-based frequency measurement techniques have very good steady-state performance, but their dynamic performance is not well documented. This paper analyzes a phasor-based frequency measurement method that considers the effect of dynamic frequency, and proposes a method to improve the dynamic performance of the phasor-based frequency measurements. Simulation results verify the effectiveness of the proposed method. Results of laboratory experiments are presented to show the performance of the proposed method compared to frequency measurements obtained from commercial phasor measurement units (PMUs).

A rich n-heptane/air mixture was burned in a flat flame burner in an inert environment, and the soot limits of stable one-stage flames were measured. With a small thin metal plate placed on the flame to suppress soot-containing cusps on the flamelets, the richest soot-free flame burned was with a mixture for which the equivalence ratio was 2.25. Hydrogen yield was, however, low. Extremely rich soot-free two-stage flames could be obtained, but they were unstable.

A rich n-heptane/air mixture was burned in a flat flame burner in an inert environment, and the soot limits of stable one-stage flames were measured. With a small thin metal plate placed on the flame to suppress soot-containing cusps on the flamelets, the richest soot-free flame burned was with a mixture for which the equivalence ratio was 2.25. Hydrogen yield was, however, low. Extremely rich soot-free two-stage flames could be obtained, but they were unstable.

Forty-nine schools (N = 5,680 fifth and sixth grade students) were assigned to pre test/treatment, pretest/no treatment, no pretest/treatment, and no pretest/no treat ment conditions in the context of an alcohol misuse prevention study. At the first posttest, five months after the pretest and two months after the intervention, the effects of the pretest and of the intervention were examined. The analyses showed that failure to correct for the design effect due to clustering within schools resulted in the overestimation of the significance of treatment and pretest effects. After correction for the design effect, a significant treatment effect in the hypothesized direction was found with respect to students' awareness of the content of the curriculum. As hypo thesized, significant treatment effects on the alcohol use and misuse measures had not yet developed but are expected to occur at subsequent posttest occasions. Significant pretest effects were found for indices measuring trouble with peers resulting from students' alcohol use, students' internal health locus of control, and their perceptions of adults as a locus of control for their health. Two of the three pretest effects were in the direction that would be hypothesized if the pretest were providing the same impe tus as the intervention. Implications of these findings for school-based substance abuse prevention programs are discussed.