Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming

A key hurdle for implementing real-time pricing of electricity is a lack of con-sumers’ responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plug-in hybrid electric vehicle charging (and discharging with vehicle-to-grid) via a two-way com-munication with the grid. Real-time pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plug-in hybrid electric vehicles. In addition, the intelligent energy controller on the consumer-side can help increase the utilization rate of the intermittent renewable resource, as the demand can be managed to match the output profile of renewables, thus making the intermittent resource such as wind and solar more economically competitive in the long run. One of the main goals of this dissertation is to present how real-time retail pricing, aided by control automation devices, can be integrated into the wholesale electricity market under various uncertainties through approximate dynamic programming. What distinguishes this study from the existing work in the literature is that whole-sale electricity prices are endogenously determined as we solve a system operator’s economic dispatch problem on an hourly basis over the entire optimization horizon. This modeling and algorithm framework will allow a feedback loop between electricity prices and electricity consumption to be fully captured. While we are interested in a near-optimal solution using approximate dynamic programming; deterministic linear programming benchmarks are use to demonstrate the quality of our solutions.The other goal of the dissertation is to use this framework to provide numerical ev-idence to the debate on whether real-time pricing is superior than the current flat rate structure in terms of both economic and environmental impacts. For this pur-pose, the modeling and algorithm framework is tested on a large-scale test case with hundreds of power plants based on data available for California, making our findings useful for policy makers, system operators and utility companies to gain a concrete understanding on the scale of the impact with real-time pricing

Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming

A key hurdle for implementing real-time pricing of electricity is a lack of con-sumers’ responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plug-in hybrid electric vehicle charging (and discharging with vehicle-to-grid) via a two-way com-munication with the grid. Real-time pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plug-in hybrid electric vehicles. In addition, the intelligent energy controller on the consumer-side can help increase the utilization rate of the intermittent renewable resource, as the demand can be managed to match the output profile of renewables, thus making the intermittent resource such as wind and solar more economically competitive in the long run. One of the main goals of this dissertation is to present how real-time retail pricing, aided by control automation devices, can be integrated into the wholesale electricity market under various uncertainties through approximate dynamic programming. What distinguishes this study from the existing work in the literature is that whole-sale electricity prices are endogenously determined as we solve a system operator’s economic dispatch problem on an hourly basis over the entire optimization horizon. This modeling and algorithm framework will allow a feedback loop between electricity prices and electricity consumption to be fully captured. While we are interested in a near-optimal solution using approximate dynamic programming; deterministic linear programming benchmarks are use to demonstrate the quality of our solutions.The other goal of the dissertation is to use this framework to provide numerical ev-idence to the debate on whether real-time pricing is superior than the current flat rate structure in terms of both economic and environmental impacts. For this pur-pose, the modeling and algorithm framework is tested on a large-scale test case with hundreds of power plants based on data available for California, making our findings useful for policy makers, system operators and utility companies to gain a concrete understanding on the scale of the impact with real-time pricing

Quantify Benefits of Home Energy Management System Under Dynamic Electricity Pricing

Retail electricity rates have been kept flat for the past century due to the lack of advanced metering technology and infrastructure. The flat-rate structure prevents consumers from responding to the fluctuation of actual costs of electricity generation, which varies hourly (or even minute-by-minute). The absence of demand response leads to an electricity system that is overly built with costly assets, solely to maintain system reliability. One of the core visions of the future electricity system, referred to as Smart Grid, is to use advanced metering infrastructure (AMI) and information technology to enable dynamic electricity rates. The main goal of this paper is to present an approximate dynamic programming (ADP) based modeling and algorithm framework that can make home energy management systems capable of optimally managing the appliance usage using the information of anticipated whole electricity prices. The other goal of the paper is to use the modeling framework to provide numerical evidence to the debate that if dynamic rate structure is superior than the current flat rate structure in terms of reducing peak demand and overall electricity costs

Optimal input potential functions in the interacting particle system method

The assessment of the probability of a rare event with a naive Monte-Carlo method is computationally intensive, so faster estimation methods, such as variance reduction methods, are needed. We focus on one of these methods which is the interacting particle (IPS) system method. The method requires to specify a set of potential functions. The choice of these functions is crucial, because it determines the magnitude of the variance reduction. So far, little information was available on how to choose the potential functions. To remedy this, we provide the expression of the optimal potential functions minimizing the asymptotic variance of the estimator of the IPS method

High prevalence of a globally disseminated hypervirulent clone, Staphylococcus aureus CC121, with reduced vancomycin susceptibility in community settings in China

Objectives: Most vancomycin-intermediate Staphylococcus aureus (VISA) and heterogeneous VISA (hVISA) are derived from hospital-associated MRSA due to treatment failure; however, the prevalence of hVISA/VISA in community settings remains unclear. Methods: Four hundred and seventy-six community-associated isolates were collected between 2010 and 2011 during national surveillance for antimicrobial resistance in 31 county hospitals across China. Drug susceptibility evaluation and mecA detection were performed by using broth microdilution and PCR analysis, respectively. hVISA/VISA were identified by using macro-Etest and a modified population analysis profile (PAP)-AUC method. The genetic features of all hVISA/VISA isolates were genotyped. Results: Among 476 isolates, MRSA and MSSA accounted for 19.7% (n = 94) and 80.3% (n = 382), respectively. Two VISA and 36 hVISA isolates were identified by PAP-AUC testing. The VISA isolates and 29 of the hVISA isolates were MRSA. The proportion of hVISA/VISA was significantly higher in MRSA (30.9%) than in MSSA (1.8%). The hVISA/VISA isolates were assigned to 18 STs classified into seven clonal complexes (CCs). CC121 (n = 12) followed by ST239 (n = 11) was the most prevalent hVISA/VISA clone. All ST239-hVISA/VISA were MRSA, while 12 CC121-hVISA isolates included 6 MSSA and 6 MRSA isolates. SCCmec III was predominant among MRSA-hVISA/VISA isolates. agr I and agr IV were detected in ST239 and CC121, respectively. All except two strains were positive for Panton-Valentine leucocidin genes. Conclusions: To the best of our knowledge, this is the first report of CC121 as a prevalent hVISA clone in community settings, highlighting the necessity of surveillance and stricter infection control measures for this globally disseminated lineage

High-efficiency 100-W Kerr-lens mode-locked Yb:YAG thin-disk oscillator

We demonstrate a Kerr-lens mode-locked femtosecond Yb:YAG thin-disk oscillator and investigate the approach to increase the optical-to-optical efficiency based on the scheme of direct multiple passes of the laser beam through the thin-disk medium. With twelve passes through the thin disk, 266-fs pulses were delivered from the oscillator with an average power of 105.6 W at a repetition rate of 20 MHz. The corresponding optical-to-optical efficiency is 31.1%, which is, to the best of our knowledge, the highest efficiency of any mode-locked thin-disk oscillator with pulse duration below 300 fs. This demonstration paves the way to even more efficient mode-locked femtosecond thin-disk oscillators, and provides an excellent laser source for the applications such as non-linear frequency conversion and high-precision industrial processing

Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming

A key hurdle for implementing real-time pricing of electricity is a lack of consumers\u27 responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plug-in hybrid electric vehicle charging (and discharging with vehicle-to-grid) via a two-way communication with the grid. Real-time pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plug-in hybrid electric vehicles. In addition, the intelligent energy controller on the consumer-side can help increase the utilization rate of the intermittent renewable resource, as the demand can be managed to match the output profile of renewables, thus making the intermittent resource such as wind and solar more economically competitive in the long run. One of the main goals of this dissertation is to present how real-time retail pricing, aided by control automation devices, can be integrated into the wholesale electricity market under various uncertainties through approximate dynamic programming. What distinguishes this study from the existing work in the literature is that whole- sale electricity prices are endogenously determined as we solve a system operator\u27s economic dispatch problem on an hourly basis over the entire optimization horizon. This modeling and algorithm framework will allow a feedback loop between electricity prices and electricity consumption to be fully captured. While we are interested in a near-optimal solution using approximate dynamic programming; deterministic linear programming benchmarks are use to demonstrate the quality of our solutions. The other goal of the dissertation is to use this framework to provide numerical evidence to the debate on whether real-time pricing is superior than the current flat rate structure in terms of both economic and environmental impacts. For this purpose, the modeling and algorithm framework is tested on a large-scale test case with hundreds of power plants based on data available for California, making our findings useful for policy makers, system operators and utility companies to gain a concrete understanding on the scale of the impact with real-time pricing

Computational Simulation of VARI Fluid Process Molding for Stiffened Panel Structural Composites

The resin filling time can be predicted and the flow pattern of resin can be simulated in Composites VARI Fluid Process Molding with simulation software PAM-RTM. The permeability is important parameter in VARI process. In-plane and transverse permeability are usually tested with complicate and expensive enclosed mold.A set of model with simple structure, easy operation, low cost, was built to obtain accurate permeability by using a process of vacuum-assisted resin infusion (VARI). Besides, the method of equivalent model was employed. The simulation results of effective model is compared with those of experimental VARI process. The filling times for simulation method is 254 s which is shorter than 301 s of the experimental process. Based on flow runner project with equivalent model, the stiffened panel structural composite is prepared to validate the selective process

Lake Evaporation in a Hyper-Arid Environment, Northwest of China—Measurement and Estimation

Lake evaporation is a critical component of the hydrological cycle. Quantifying lake evaporation in hyper-arid regions by measurement and estimation can both provide reliable potential evaporation (ET0) reference and promote a deeper understanding of the regional hydrological process and its response towards changing climate. We placed a floating E601 evaporation pan on East Juyan Lake, which is representative of arid regions’ terminal lakes, to measure daily evaporation and conducted simultaneous bankside synoptic observation during the growing season of 2013–2015. A semi-empirical evaporation model derived from Dalton model was parameterized and validated with measured data. The model was then used to estimate lake evaporation during 2002–2015. According to in situ measurements, maximum, minimum and mean lake evaporation were 8.1, 3.7 and 6.5 mm/day, and growing season evaporation was 1183.3 mm (~80% of the annual amount). Adding up non-growing season evaporation that we converted from φ20 pan evaporation at Ejina weather station, the annual mean lake evaporation, 1471.3 mm, was representative of lower Heihe River’s ET0. Model inter-comparison implied our model performed well both in simplicity and accuracy and has potential utilization in a data-sparse area. In 2002–2015, estimated mean daily evaporation was 6.5 mm/day and growing season evaporation was 1233.7 mm. Trend analysis of estimated evaporation proved the evaporation paradox’s existence in this hyper-arid region and validated complementary relationship theory’s adaptability