Abstract:To reduce the influence of wind power output uncertainty on power system stability, demand response (DRPs) and energy storage systems (ESSs) are introduced while solving scheduling optimization problems. To simulate wind power scenarios, this paper uses Latin Hypercube Sampling (LHS) to generate the initial scenario set and constructs a scenario reduction strategy based on Kantorovich distance. Since DRPs and ESSs can influence the distribution of demand load, this paper constructs a joint scheduling optimization model for wind power, ESSs and DRPs under the objective of minimizing total coal cost, and constraints of power demand and supply balance, users' demand elasticity, thermal units' startup-shutdown, thermal units' output power climbing and wind power backup service. To analyze the influences of ESSs and DRPs on system wind power consumption capacity, example simulation is made in a 10 thermal units system with a 1000 MW wind farm and 400 MW energy storage systems under four simulation scenarios. The simulation results show that the introduction of DRPs and ESSs could promote system wind power consumption capacity with significantly economic and environment benefits,
OPEN ACCESSEnergies 2014, 7 7283 which include less coal consumption and less pollutant emission; and the optimization effect reaches the optimum when DRPs and ESSs are both introduced.
This paper presents a macroscopic view on the prevailing policy and potential issues in respect of the sustainable wind power development in China. It starts by analyzing the characteristics of wind power resources and pin-pointing the relationship between the regulatory policies and various economic, taxation, legal and grid integration attributes relating to the wind power development. Then it follows by analyzing the status quo and capabilities of the wind power manufacturing industry in China including its operational efficiency and grid integration standards. The economic and environmental benefits are estimated by relating to the associated costing analysis in respect of the major contributing factors such as manufacturing, operational and financial factors. Results of the associated benefits analysis indicate that the use of the wind power generation helps to save a significant equivalent amount of standard coal consumption resulting to reduction of emission effectively. Finally, the potential of wind power development in China is shown to be affirmative and the sustainable energy policy is effectively implemented in China.
Pancreatic adenosquamous carcinoma (ASPC) is a rare subtype of pancreatic cancer with lethal malignancy, and few studies have focused on the heterogeneity of ASPC. Here, we performed a single-cell sequencing procedure on pancreatic tumor tissue from an ASPC patient and a patient with high-grade intraductal papillary mucinous neoplasm (IPMN). Through the combined analysis of single-cell sequencing data from five pancreatic ductal adenocarcinoma (PDAC) patients, one IPMN patient, and one ASPC patient in a public database, we identified 11 main types of cells, including macrophages, B cells, cancer stem cells, ductal cells, fibroblasts, endo/stellate cells, neutrophils, acinar cells, T cells, natural killer (NK) cells, dendritic cells, and mast cells. Then, the different characteristics and differentiation paths of the immune microenvironment among IPMN, ASPC, and PDAC in macrophages, T cells, and cancer-associated fibroblasts (CAFs) were identified through multiple bioinformatics analyses. Two novel special cancer-associated fibroblasts were identified as nCAFs and imCAFs. Then, cancer cells in duct cells were identified using the infercnv software. Two ASPC-specific subgroups of cancer cells with squamous cell features were identified. Finally, the identified specific CAFs and cancer cells were mapped to TCGA-PAAD cohort through the cibersoftx software. All of these identified subgroups were calculated to have a significant prognostic value in pancreatic cancer patients. These findings will promote the clinical application of single-cell sequencing data of pancreatic cancer and deepen our understanding of ASPC.
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