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The economy of the Regional Integrated Energy System (RIES) scheduling scheme is affected by the parameter accuracy of different energy conversion equipment models. The traditional static energy hub (EH) model regards the equipment efficiency as a constant and ignores the variable condition characteristics of the equipment efficiency changing with the load rate. This paper presents an optimal scheduling method of RIES considering the characteristics of equipment under variable operating conditions. Firstly, the architecture of the integrated energy system is analyzed. Secondly, based on the DEH model, an optimal scheduling method of RIES was proposed considering the characteristics of equipment under variable operating conditions. Finally, an optimal scheduling analysis is performed for a typical RIs. The results show that the proposed method can improve the accuracy of the equipment model and reduce the cost prediction error by considering the variable operating conditions of the equipment, thus providing a more economical scheduling scheme for Ries.

Economic transformation creates a new environment for grid investment. In the situation of high quality development, the traditional investment scale prediction model is no longer applicable. Aiming at the problems of single parameter of grid-driven investment scale prediction model and poor linear fitting accuracy, a provincial medium- and long-term investment scale prediction model based on support vector machine was proposed. Aiming at the single parameter and poor line fitting accuracy of the grid-driven investment scale prediction model under the new situation, the new environment, new directions and new requirements for grid investment are analyzed. Based on the support vector machine algorithm, a medium-and long-term investment scale prediction model for provincial grids based on support vector machines is proposed. The scale of provincial grid investment is expected from 2019 to 2022. The empirical results show that the prediction model constructed in this paper is effective and feasible. It is of great significance to explore the prediction model of medium and long-term investment scale of power grid enterprises in the new situation.

A novel mesoporous polymer-based composite, C7-BTBP/XAD-7, was prepared by impregnating 6,6’-bis(5,6-diheptyl-1,2,4-triazin-3-yl)-2,2’-bipyridine (C7-BTBP) into the pores of the polymeric XAD-7 carrier. The microstructure of the material was characterized by scanning electron microscopy (SEM), nitrogen adsorption-desorption isotherms, and thermogravimetric analysis (TGA). The selective adsorption of Pd(II) onto C7-BTBP/XAD-7 from a HNO3medium containing some typical metals, including Ru(III), Fe(III), Mo(VI), Zr(IV), Li(I), Na(I), K(I), Rb(I), Cs(I), Mg(II), Ca(II), Sr(II), Ba(II), Y(III), La(III), Nd(III), and Yb(III), was investigated. The polymeric composite exhibited strong adsorption affinity, high selectivity, and fast adsorption rate for Pd(II) in 3.0 M HNO3. The adsorption isotherm fitted well with Langmuir model, with a maximum adsorption capacity of 0.412 mmol/g. The hybrid polymeric composite was very stable in HNO3solution. The kinetics of Pd(II) coordinated with C7-BTBP was discussed by Density Function Theory (DFT) calculations. [ABSTRACT FROM PUBLISHER]