The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: (i) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT), (ii) the effect of subsidies of PV, (iii) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find the optimal solution. Finally, 6 cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. And only when the subsidies of PV reach a certain level will the installation of PV be considered.

Accurate user-transformer connectivity relationship (UTCR) plays a key role in fine management of low-voltage distribution network (LVDN) i.e., load expansion, line loss management, and electrical service restoration after outage. Limited data and low discriminability and noise in data increase the difficulty to identify UTCR for the existing data analytics methods. To overcome these hurdles, this paper proposes a novel UTCR algorithm which combining the data preprocessing with multi-dimensional priori knowledge based on voltage characteristics in LVDN. Firstly, the prior knowledge related to UTCR are refined on account of voltage correlation characteristics of users at different locations to provide theoretical foundation. Then, Z-score and principal component analysis are combined to standardize and extract features from the original voltage data to magnify the differences between data and reduce the impact of data noise. Further, on the basis of the prior knowledge of voltage correlation characteristics, a knowledge-driven identification model is proposed to identify users with wrong UTCR and their real UTCR. Finally, the performance of the proposed algorithm is verified on simulated LVNDs. The comparison analysis between the proposed method and other published methods and the impact of the number of principal components on the identification accuracy are also investigated. The results indicate that the proposed method achieves higher recognition accuracy than other published methods with low discriminability and noise in data.

To bridge the gap in the research on the control and drive methods of the key equipment of the new subsea production control system, all-electric subsea gate valve actuator, and to solve the problems of the valve control and drive system in the traditional subsea production system, this paper proposed a full-featured and feasible electrical system of the all-electric subsea gate valve actuator containing the control system and the drive system. The key functions were realized, including status monitoring, redundant communication, redundant power supply, redundant drive, and low-power open-position holding. The electrical system is suitable for monitoring and controlling all-electric subsea gate valve actuators with various specifications and is highly integrated, open, efficient, and real-time. The electrical system prototype was developed and successfully tested for several functions. The results showed that the all-electric subsea gate valve actuator electrical system was capable of controlling and driving the actuator, monitoring the status information of the internal and external environment of the system, as well as the power output information of the drive system, and having redundancy features.

The new power system with new energy as the main body gives the low-voltage distribution network (LVDN) a richer connotation, requiring intelligent measurement equipment to have good scalability and collaborative ability. To address these requirements, an optimization configuration model for intelligent measurement multi-core modules considering "cloud-edge-end-core" collaboration is proposed in this paper. Initially, a technical framework for "cloud-edge-end-core" collaboration in intelligent measurement is designed, expanding the conventional "cloud-edge-end" vertical cooperation architecture with multi-core module collaboration and horizontal synergy at the same hierarchical level to enhance the flexibility of data interaction. Then, an optimization configuration model for multi-core modules is formulated, to minimize both the chip configuration costs and the data transmission costs associated with "cloud-edge-end-core" collaboration. The decision variables include the core chip level of the intelligent measurement terminal, the management core chip level for smart meters, and the placement of application configurations. Finally, the effectiveness of the proposed model is verified in the LVDN with 300 users. The performance of the proposed optimization configuration model in different scenarios is compared and the influence of model parameters on the optimization results is analyzed. The results show that the proposed multi-core module optimization configuration model can optimize the selection of intelligent measurement terminal core and smart meter management core according to different application data requirements. It meets the application requirements while minimizing the multi-core module optimization configuration cost and the "cloud-edge-end-core" collaboration cost.

Double-pulsed gas metal arc welding (DP-GMAW), in which strong and weak pulse groups alternate periodically at a low frequency, is promising for improving the weld quality of aluminum alloys. However, how the low-frequency modulation shapes the bead profile and microstructure remains unclear. Bead-on-plate DP-GMAW of AA6061 aluminum alloy was therefore performed, in which the numbers of pulses in the strong and weak groups were varied from 1:1 to 30:30 (low frequency 41.67–1.39 Hz). Fish-scale ripples appeared on the bead surface at all tested frequencies, whereas the penetration fluctuated periodically only when the number of pulses per group reached 7 or more, so that the onset of the periodic fluctuation is bracketed between 8.33 Hz and 5.95 Hz. Both the penetration and the reinforcement peak/valley differences increased as the low frequency decreased; the largest penetration peak/valley difference reached 664.33 μm at 2.78 Hz, and the ratio of the penetration to the reinforcement peak/valley difference was largest at 3.79 Hz, where the penetration difference (627.83 μm) amounted to 532.06% of the reinforcement difference, suggesting that the droplet-impingement-driven flow affects the pool bottom far more strongly than the bead surface. At 3.79 Hz, the valley and peak angles reached 91.52° and 158.33°, respectively. It is proposed that the droplet-driven melt erodes the solid front wall at weak-to-strong transitions and forms sharp valleys, while the unconstrained flow preserves blunt peaks. Columnar grains formed along the downslope edges, whereas the grains were refined along the upslope edges. These results provide a quantitative basis for tailoring the weld profile and microstructure via low-frequency current modulation.

The integration of renewable energy and rising electricity demand strain system flexibility. While price-based demand response (PBDR) improves flexibility through pricing signals, its efficacy hinges critically on accurate consumer modeling. Recognizing this pivotal role, this paper provides a comprehensive review of consumer models in PBDR and their applications to electric vehicles (EVs). First, a unified conceptual framework is presented, delineating the energy, information and financial flows among the system operator (SO), load aggregators (LAs), and end-users, and highlighting the central position of consumer modeling. Second, existing modeling approaches are systematically classified into four categories, namely rule-based, optimization-based, data-driven, and hybrid, to facilitate the selection of appropriate models by researchers and stakeholders for diverse scenarios. Furthermore, the application and adaptation of these models to EVs are critically analyzed, accounting for unique vehicular constraints. Subsequently, a systematic summary of the key characteristics and existing research gaps is provided. Finally, key directions for future research are proposed accordingly, aimed at incorporating bounded rationality into behavioral models, developing individualized consumer modeling coupled with user-specific dynamic pricing, and extending consumer modeling to residential multi-energy prosumers in integrated energy systems.