Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

In a complex urban scene,observation from a single sensor unavoidably leads to voids in observations,failing to describe urban objects in a comprehensive manner.In this paper,we propose a spatio-temporal-spectral-angular observation model to integrate observations from UAV and mobile mapping vehicle platform,realizing a joint,coordinated observation operation from both air and ground.We develop a multi-source remote sensing data acquisition system to effectively acquire multi-angle data of complex urban scenes.Multi-source data fusion solves the missing data problem caused by occlusion and achieves accurate,rapid,and complete collection of holographic spatial and temporal information in complex urban scenes.We carried out an experiment on Baisha Town,Chongqing,China and obtained multi-sensor,multi-angle data from UAV and mobile mapping vehicle.We first extracted the point cloud from UAV and then integrated the UAV and mobile mapping vehicle point cloud.The inte-grated results combined both the characteristics of UAV and mobile mapping vehicle point cloud,confirming the practicability of the proposed joint data acquisition platform and the effectiveness of spatio-temporal-spectral-angular observation model.Compared with the observation from UAV or mobile mapping vehicle alone,the integrated system provides an effective data acquisition solution toward comprehensive urban monitoring.

IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo.However,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.Methods:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS data.IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS data.Results:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data.HIS is implemented in Python and freely available at http://iris.zhanglab.net.Conclusion:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles.We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.