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Abstract As a basic task in energy consumption monitoring system, load forecasting has great effects on system operation safety, generation costs and economic benefits. In this paper, a long-term load forecasting algorithm using data dimension expansion and deep feature extraction is proposed. First, the outliers of the meteorological measurements are removed by median filter method, and then the time information is encoded to form the fingerprint of the training data. Next, the full connected network (FCN) is used to expand the dimensions of the fingerprint, and the convolutional neural network (CNN) is used to extract the deep features which can obtain better feature representation. Finally, the FCN, the CNN and regression learning model are combined for jointly offline training. The optimal parameters of these network can be obtained under global solution. Experimental results show that the proposed algorithm has better load forecasting performance than existing methods.

Abstract In this study, a β-agarase gene, agaB-4, was isolated for the first time from the agar-degrading bacterium Paenibacillus agarexedens BCRC 17346 by using next-generation sequencing. agaB-4 consists of 2652 bp and encodes an 883-amino acid protein with an 18-amino acid signal peptide. agaB-4 without the signal peptide DNA was cloned and expressed in Escherichia coli BL21(DE3). His-tagged recombinant AgaB-4 (rAgaB-4) was purified from the soluble fraction of E. coli cell lysate through immobilized metal ion affinity chromatography. The optimal temperature and pH of rAgaB-4 were 55 °C and 6.0, respectively. The results of a substrate specificity test showed that rAgaB-4 could degrade agar, high-melting point agarose, and low-melting point agarose. The V max and K m of rAgaB-4 for low-melting point agarose were 183.45 U/mg and 3.60 mg/mL versus 874.61 U/mg and 9.29 mg/mL for high-melting point agarose, respectively. The main products of agar and agarose hydrolysis by rAgaB-4 were confirmed to be neoagarotetraose. Purified rAgaB-4 can be used in the recovery of DNA from agarose gels and has potential application in agar degradation for the production of neoagarotetraose.

An integrated energy system (IES) can significantly enhance utilization efficiency and boost renewable energy consumption while also catering to various energy needs within the system. It is one of the essential models of future energy system development. With a focus on improving carbon market subjects and carbon transaction mechanisms, this paper proposes integrated energy optimization scheduling in high-speed railway stations considering demand response under the participation of the carbon market, considering both low-carbon and economic needs. In the context of participation in the carbon and energy markets, an integrated energy system in the high-speed railway station is constructed, comprising photovoltaic power generation, stored energy device, CCHP and gas boiler. Based on the simulations of each energy device, the demand response compensation price and carbon transaction revenue are taken into account, and the target is to economize the operating budget and optimize the arrangement for IES in a high-speed railway station under the carbon transaction market. Through simulation, the optimization performance of the proposed model is analyzed under the conditions of demand response and no carbon cap-and-trade market. The findings demonstrate that the best scheduling strategy for integrated energy at high-speed rail stations taking into account integrated demand response under the carbon market can successfully reduce carbon emissions from station operation while flattening the peak-to-valley load difference on the grid side, opening up ideas for the proposal of energy saving and emissions reduction. [ABSTRACT FROM AUTHOR]

To promote the consumption and development of new and green energy sources, we propose an economically optimal dispatch model to simulate virtual power plants (VPPs) in parks considering low-carbon objectives based on a dualcarbon background. First, individual models involved in the optimal dispatch of VPPs are introduced. A two-stage operation process of electricity-to-hydrogen transformation and hydrogen methanation of power-to-gas equipment is refined and analysed. Unstable wind power is converted into hydrogen and supplied to the hydrogen load during the electrolysis of the water-tohydrogen stage in conjunction with the development of hydrogen energy demands. Second, to minimise the sum of operating and carbon emission costs, an optimal dispatch model utilised by the virtual power plant considering a low-carbon objective is established. A simulation of the calculation case shows that the proposed model can achieve efficient energy utilisation and improved economic and carbon costs for a VPP in the park. Finally, a sensitivity analysis of the system cost is conducted by adding weighting coefficients; the optimal range of weighting coefficients for the VPP in the park proposed in this paper are derived for reference by considering economic benefits and environmental protection. [ABSTRACT FROM AUTHOR]