An efficient power plant model of electric vehicles for unit commitment of large scale wind farms

AbstractAn efficient power plant model of electric vehicles (E-EPP) considering the travelling comfort levels of EV users is developed to investigate the contribution of EVs on the unit commitment (UC) of large scale wind farms. Firstly, a generic EV battery model (GEBM) is established considering the uncertainties of battery parameters. Then, a Monte Carlo Simulation (MCS) method is implemented within the E-EPP to obtain the available response capacity of EV charging load over time. And a UC strategy using the E-EPP based on power flow tracing is developed. Finally, a modified IEEE 118-bus system integrated with wind farms is used to verify the effectiveness of the E-EPP for the UC of large scale wind farms

A Spatial-Temporal model for grid impact analysis of plug-in electric vehicles

A Spatial–Temporal model (STM) was developed to evaluate the impact of large scale deployment of plug-in electric vehicles (EVs) on urban distribution networks. The STM runs based on the integration of power system analysis and transportation analysis. Origin–Destination (OD) analysis from intelligent transportation research was used to model the EV mobility. Based on the EV technical and market information provided by the EU MERGE project and the output of OD analysis, a Monte Carlo simulation method was developed within the STM to obtain the EV charging load of each load busbar over time. The STM aims to facilitate power system evaluation and planning, and is able to provide both average values and probabilities of nodal bus voltages and branch loadings. The STM is able to identify the critical network components that will require to be upgraded. A high customer density urban network from the United Kingdom Generic Distribution System combined with geographic information was used as a test system. Two EV charging strategies, “dumb” charging and “smart” charging, were simulated and compared under different EV penetration levels (0%, 25% and 50%) to verify the effectiveness of STM

Effects of a novel pH-sensitive liposome with cleavable esterase-catalyzed and pH-responsive double smart mPEG lipid derivative on ABC phenomenon

Daquan Chen1,2, Wanhui Liu1,2, Yan Shen3, Hongjie Mu1,2, Yanchun Zhang4 , Rongcai Liang1,2, Aiping Wang1,2, Kaoxiang Sun1,2, Fenghua Fu1,2 1School of Pharmacy, Yantai University, Yantai, People’s Republic of China; 2State Key Laboratory of Long-acting and Targeting Drug Delivery System, Yantai, People’s Republic of China; 3College of Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China; 4College of Pharmacy, Anhui University of Traditional Chinese Medicine, Hefei, People’s Republic of China Background: The ABC phenomenon is described as a syndrome of accelerated clearance of polyethylene glycol (PEG)-modified liposomes from the bloodstream when repeatedly injected, with their increased accumulation in the liver and spleen. Methods: To clarify this immune response phenomenon, we evaluated a novel modified pH-sensitive liposome with a cleavable double smart PEG-lipid derivative (mPEG-Hz-CHEMS). Results: The ABC phenomenon in mice was brought about by repeated injection of conventional PEG-PE liposomes and was accompanied by a greatly increased uptake in the liver. However, a slight ABC phenomenon was brought about by repeated injection of mPEG-CHEMS liposomes and was accompanied by only a slightly increased uptake in the liver, and repeated injection of mPEG-Hz-CHEMS liposomes did not induce the ABC phenomenon and there was no increase in liver accumulation. This finding indicates that the cleavable mPEG-Hz-CHEMS derivative could lessen or eliminate the ABC phenomenon induced by repeated injection of PEGylated liposomes. Conclusion: This research has shed some light on a solution to the ABC phenomenon using a cleavable PEG-Hz-CHEMS derivative encapsulated in nanoparticles. Keywords: accelerated blood clearance, double smart, cleavable, mPEG-lipid derivates, pH-sensitive liposom

Epidemic characteristics, high-risk townships and space-time clusters of human brucellosis in Shanxi Province of China, 2005–2014

BACKGROUND: Brucellosis, one of the world's most important zoonosis, has been re-emerging in China. Shanxi Province, located in northern China, where husbandry development has been accelerated in recent years, has a rather high incidence of human brucellosis but drew little attention from the researchers. This study aimed to describe the changing epidemiology of human brucellosis in Shanxi Province from 2005 to 2014 and explore high-risk towns and space-time clusters for elucidating the necessity of decentralizing disease control resource to township level in epidemic regions, particularly in hotspot areas.METHODS: We extracted data from the Chinese National Notifiable Infectious Disease Reporting System to describe the incidence and spatiotemporal distribution of human brucellosis in Shanxi Province. Geographic information system was used to identify townships at high risk for the disease. Space-Time Scan Statistic was applied to detect the space-time clusters of human brucellosis during the past decade.RESULTS: From 2005 to 2014, a total of 50,002 cases of human brucellosis were recorded in Shanxi, with a male-to-female ratio of 3.9:1. The reported incidence rate increased dramatically from 7.0/100,000 in 2005 to 23.5/100,000 in 2014, with an average annual increase of 14.5%. There were still 33.8% cases delaying diagnosis in 2014. The proportion of the affected towns increased from 31.5% in 2005 to 82.5% in 2014. High-risk towns spread from the north to the center and then south of Shanxi Province, which were basins and adjacent highlands suitable for livestock cultivation. During the past decade, there were 55 space-time clusters of human brucellosis detected in high risk towns; the clusters could happen in any season. Some clusters' location maintained stable over time.CONCLUSIONS: During the last decade, Shanxi province's human brucellosis epidemic had been aggravated and high-risk areas concentrated in some towns located in basins and adjacent highlands. Space-time clusters existed and some located steadily over time. Quite a few cases still missed timely diagnosis. Greater resources should be allocated and decentralized to mitigate the momentum of rise and improve the accessibility of prompt diagnosis treatment in the high-risk townships

A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks

With the increasing number of electric vehicles (EVs), the EV fast charging load will significantly affect the voltage quality of electricity distribution networks. On the other hand, EVs have potentials to change the choices of charging locations due to the incentives from the variations of charging prices, which can be considered as a flexible response resource for electricity distribution networks. In this paper, a charging pricing strategy of EV fast charging stations (FCSs) was developed to determine the pricing scheme for the voltage control of electricity distribution networks, which consisted of a simulation model of EV mobility and a double-layer optimization model. Considering the travel characteristics of users, the simulation model of EV mobility was developed to accurately determine the fast charging demand. Taking the total income of FCSs and the users’ response to the pricing scheme into account, the double-layer optimization model was developed to optimize the charging pricing scheme and minimize the total voltage magnitude deviation of distribution networks. A test case was used to verify the proposed strategy. The results show that the spatial distribution of EV fast charging loads was reallocated by the proposed charging pricing scheme. It can also be seen that the proposed strategy can make full use of the response capacity from EVs to improve the voltage profiles without decreasing the income of the FCSs

Scheduling distributed energy resources and smart buildings of a microgrid via multi-time scale and model predictive control method

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context