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We empirically investigate the effect of emissions trading scheme (ETS) on the corporate performance of Chinese listed firms from 2010 to 2016, treating China’s pilot ETS as a quasi-natural experim...

Manufacturing servitization may reduce energy consumption and achieve low-carbonization of global value chain. We calculated and analyzed carbon emissions embodied in export and manufacturing servi...

The grid-scale battery energy storage system (BESS) plays an important role in improving power system operation performance and promoting renewable energy integration. However, operation sa...

Residents with advanced measuring and controlling appliances can participate in demand response (DR) by the home energy management system (HEMS), thereby improving the reliability and flexibility of the power system. However, the domestic incentive mechanism is deficient, and there is short of home energy optimization model that comprehensively considers price-based and incentive-based DR. First at all, a variety of flexible appliances such as distributed photovoltaic (PV), electric vehicle (EV), and energy storage (ES) were considered, a mixed-integer load model under a complex power consumption environment was classified and established. Secondly, the price-based DR based on the time-of-use electricity price and the incentive-based DR based on the user agreement were considered comprehensively. Taking reducing household electricity costs and household net load power fluctuations as joint optimization targets, a day-ahead scheduling model considering users' willingness to participate in incentive-based DR was established. Finally, the effectiveness of the proposed model was verified based on the MATLAB.

In order to solve the problems that may exist in the large-scale application of energy storage, the “plug-and-play” technology is realized through battery storage power plants. The concept of plug-and-play originates from the network, which refers to the ability of the computer system to configure expansion boards and other devices automatically. This paper researches plug-and-play key technologies for battery storage power stations, aiming to overcome the grid-connected bottlenecks after large-scale application of energy storage systems, improve the effectiveness and convenience of the grid-connected operation of energy storage systems, and effectively improve the practicality of energy storage systems level, supporting the company’s energy Internet development strategy. Based on the technical framework of plug-and-play realization of battery storage power stations, this paper will conduct technical research on the realization of information flow. First, based on the IED modeling method and the actual functional requirements of BESS, we select logical nodes with corresponding functions to form logical devices and establish the BESS information model. Then, we express the self-describing characteristics through the SCL configuration file and analyze the configuration process of the configuration description file CID. Finally, the plug-and-play information flow implementation architecture is constructed with the heterogeneous information model mapping and data conversion module as the core part. Our design can realize the real plug-and-play operation of the battery storage power station connected to the regional power grid, thereby promoting the friendly interaction between the regional power grid and the battery storage power station and improving the adaptability of the battery storage power station in different application scenarios.

Lithium-ion batteries have been widely used in the field of energy storage, due to the high energy density, wide temperature range and long service life. However, in application, the parameters such as the capacity and voltage of each cell in the battery pack are inconsistent due to unreasonable use, poor operating environment and other factors. In this paper, the qualitative relationship between the voltage variation of lithium-ion battery and the cell capacity and SOC is firstly analyzed, and then an inconsistent cell identification method is proposed based on three feature parameters of voltage outliers, voltage range and voltage difference. The static component is obtained from the voltage in the unstable discharge data by empirical mode decomposition and the inconsistency of capacity and SOC of static components is analyzed. The online identification of inconsistency of lithium-ion battery pack is realized based on the threshold and $K$ -means algorithm.

The rapid development of microgrid (MG) has made it ready to connect multiple MGs for providing regional power supply. The energy management of a multi-MG (MMG) system is the key to the reliable, economic operation of the interconnected power system. The MMG system is rich in distributed generation sources. The intermittence and volatility of renewable energy sources (RESs), such as wind and solar, cannot be ignored in a large-scale hybrid power system. Therefore, the energy management of a MMG system is a complex problem that coordinates distributed generation units within individual MGs, the power exchange among MGs, and the power trading between the grid and MGs. System of systems (SoS) has been identified as an effective way of forming a MMG system and coordinating the participating MGs for energy management. This paper proposes a hierarchical decentralized SoS architecture for the energy management of a MMG system and, accordingly, formulates it as a bilevel optimization problem. Energy management at the level of individual MGs is modeled as a multiple-stage robust optimization to handle the issue of RES uncertainty. Energy management at the level of MMG system further addresses the problem of spatially unbalanced demand and generation in the area. The proposed MMG energy management is further verified through a case study.

The effect of spherical aberration on the radius of image beam waist in the design of laser optical system is discussed in detail, and the relationship between the spherical aberration and the image beam waist radius in the laser optical system is deduced. Radius variation of image beam waist due to spherical aberration which varies with the position of object beam waist and the optical system focus is analyzed by the form of curve. The analysis results indicate that the effect of spherical aberration on the radius of image beam waist should be considered according to the position of object beam waist in the design of a laser optical system. Finally, a design example of the optical system used for atmospheric laser communication system is given to illustrate the effect of spherical aberration on the image beam waist.

Considering the uncertainties from renewable energy sources and loads, the paper presents a novel two-stage robust optimal economic dispatch model for grid-connected AC/DC hybrid microgrids. The model is decomposed into a master and slave min-max-min problem with mixed integer linear structure. A robust economic dispatch result is obtained by using the column-and-constraint generation algorithm(C&CG) to solve the day-ahead optimization problem for AC/DC hybrid microgrids, which minimizes the daily operating cost. A case study is conducted to further demonstrate the effectiveness and capability of the proposed model in this paper for the operation dispatching of AC/DC hybrid microgrids.

Strategies combining active learning Kriging (ALK) model and Monte Carlo simulation (MCS) method can accurately estimate the failure probability of a performance function with a minimal number of training points. That is because training points are close to the limit state surface and the size of approximation region can be minimized. However, the estimation of a rare event with very low failure probability remains an issue, because purely building the ALK model is time-demanding. This paper is intended to address this issue by researching the fusion of ALK model with kernel-density-estimation (KDE)-based importance sampling (IS) method. Two stages are involved in the proposed strategy. First, ALK model built in an approximation region as small as possible is utilized to recognize the most probable failure region(s) (MPFRs) of the performance function. Consequentially, the priori information for IS are obtained with as few training points as possible. In the second stage, the KDE method is utilized to build an instrumental density function for IS and the ALK model is continually updated by treating the important samples as candidate samples. The proposed method is termed as ALK-KDE-IS. The efficiency and accuracy of ALK-KDE-IS are compared with relevant methods by four complicated numerical examples.