Export trade is not only the driving force for the economic growth of the manufacturing industry but also the key to its low-carbon transformation. To enrich the research on the embodied carbon in the export trade of manufacturing and manufacturing, this paper combines the factor decomposition method with the decoupling elasticity index model to establish an extended decoupling elasticity model to measure the decoupling relationship and degree between the measurable export trade and the corresponding decoupling elasticity values of the driving factors affecting the decoupling of the total effect; finally, the evaluation results of energy consumption decoupling in the industry is discussed. The input-output model is used to effectively calculate the embodied carbon in export trade; the decoupling elasticity model is used to analyze the decoupling of the embodied carbon in the export trade of the manufacturing industry. The research results are conducive to adjusting the trade structure according to local conditions and promoting the development of low-carbon economy. This provides literature support for the manufacturing industry to reasonably control the scale of exports of high-carbon products, adjust the export structure and develop low-carbon trade strategies. It is of great importance to achieve the goal of “carbon neutrality” and “carbon peaking”, and to form a green and low-carbon dual-cycle pattern at home and abroad.

This paper proposes an allocation method of daily carbon emission quota and the dispatch strategy in industrial parks considering typical operational scenarios. Firstly, data preprocessing is performed on the original electrical load of each feeder flowing into the park, and daily operation characteristics of each feeder are generated. Then, the K-means method is used to cluster the daily operational characteristics, where the Elbow Method is applied to determine the optimal number of clusters, thereby obtaining the typical daily operational scenarios for each feeder. Furthermore, the daily carbon emission quota for different operating scenarios of each feeder in the next year is allocated according to its proportion in the annual carbon emission. Finally, a dispatch optimization model of the park, considering demand response with the aid of flexible loads, is established, where the comfort level and the change rate of operating cost are introduced as objectives. This study provides a new method for carbon emission quota allocation in daily scheduling of park, and offers a powerful tool for the achievement of dual carbon goals in park.

The location and capacity of the energy storage system (ESS) affect the security, stability and economy of the distribution network. The location and capacity of the energy storage system interact with each other and form the optimal allocation problem of the energy storage system. In this paper, considering the constraints of hybrid energy storage technology parameters and operation status, an economic optimization model of hybrid energy storage system (HESS) with frequency division and constant capacity including capacity cost and power cost is established. Genetic algorithm is used to optimize and determine the optimal capacity allocation of hybrid energy storage system. Secondly, the influence of the HESS on the power flow distribution of the distribution network and the change of the voltage amplitude of each feeder node are analyzed. In this paper, the location of HESS is optimized by considering the node voltage sensitivity, feeder node voltage fluctuation and line section load rate, so as to improve the power quality of distribution network with intermittent loads.

Network reconfiguration and demand response can both reduce the loss and improve the security of the distribution networks (DNs). In this paper, we describe a day-ahead DN reconfiguration schedule model considering demand response, in which network reconfiguration is conducted by distribution system operator (DSO) while the demand response strategies are realized by customer aggregators. To preserve information privacy and reduce computational burden, a two-level decomposition & coordination algorithm is proposed to solve this model, which is based on a log-barrier cost function. In the upper level, the DSO optimizes the network reconfiguration schedule, calculates the barrier cost of every load bus and then sends it to the corresponding customer aggregator. In the lower level, every customer aggregator exploits available controllable demands like heat pump (HP), electric vehicle (EV) to minimize the nodal daily cost based on the barrier cost calculated in DSO. Numerical tests verify the promising convergence of this decomposition algorithm and show a reduction of the total cost in DN.