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The utilization of repurposed second-life batteries from electric vehicles in DC microgrids presents a sustainable and cost-effective solution. However, efficiently integrating these batteries within DC microgrids poses two main challenges: (1) poor consistency among retired battery packs; (2) fluctuations in the DC bus voltage. Traditional approaches primarily focus on achieving the state of charge (SOC) balancing within retired battery packs, often neglecting the suppression of voltage fluctuations, thereby compromising system stability. To address these challenges, this paper proposes a virtual DC machine (VDCM)-based control strategy that enhances both the power quality of the DC microgrid and the consistency of retired battery packs. On the one hand, reconfigurable topology and SOC balancing strategies are developed to manage the equalization current, achieving SOC balancing within the retired battery packs. On the other hand, a VDCM strategy is designed to leverage the capacity characteristics of retired batteries to increase the inertia of the DC microgrid. Compared to conventional methods, experimental results show that the proposed strategy not only maintains SOC balancing during both the charging and discharging phases but also significantly reduces overshoot and settle times of dc voltage to 64.2% and 55.6%, respectively.

In the quest for effective solutions to address Environ. Pollut. and meet the escalating energy demands, heterojunction photocatalysts have emerged as a captivating and versatile technology. These photocatalysts have garnered significant interest due to their wide-ranging applications, including wastewater treatment, air purification, CO2 capture, and hydrogen generation via water splitting. This technique harnesses the power of semiconductors, which are activated under light illumination, providing the necessary energy for catalytic reactions. With visible light constituting a substantial portion (46%) of the solar spectrum, the development of visible-light-driven semiconductors has become imperative. Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light. In this comprehensive review, we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media, as well as the remarkable progress made in renewable energy production. Moreover, we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems. Finally, we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain. By unraveling the potential of heterojunction photocatalysts, this review contributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.

This letter has developed an electrical circuit analogy-based maximum latency calculation (MLC) method of the internet data center (IDC) in power-communication network. Firstly, by analogy with the circuit model, the basic concepts to describe information flow are defined, including information current, information resistance, information conductivity, and information voltage. Based on these concepts, the information processing model considering both channel blocking and user priority is established. By analogy with the electrical circuit, the information flow calculation laws are introduced to calculate the maximum latency of IDCs. Verification results show that the maximum latency of IDCs in power-communication network can be accurately calculated by the proposed MLC method.

With the increasing penetration of wind power and gradual retirement of conventional generating units (CGUs), wind turbines (WTs) become promising resources to provide steady-state energy reserve (ER) and frequency support for the grid to facilitate supply-demand balance and frequency security. In this regard, a novel frequency constrained dispatch framework with ER and virtual inertia from WTs is proposed. Firstly, this paper establishes the WT model with both ER and virtual inertia, whose energy sources are WT's deloading and rotor kinetic energy, respectively. Secondly, the system frequency response and CGUs' power response are derived while considering WTs exiting inertia response at frequency nadir. Then, this paper develops a stochastic-optimization-based frequency constrained dispatch model, where both WTs' frequency regulation parameters and rotor speeds are decision variables, so that the coupling between WT's mechanical and electrical parts and the coupling between system's transient dynamics and steady-state operation can be fully reflected. Finally, convex hull relaxation, convex hull approximation and deep neural networks are used to transform the original nonlinear model into a mixed-integer second-order cone programming model. Case studies on the 118-bus system verify the effectiveness of the proposed models and methods.

The transformerless cascaded H-bridge grid-connected inverter (TL-CHB-GCI) is an attractive topology in photovoltaic (PV) systems. However, due to lack of electrical isolation, the leakage current will inevitably appear between the grid and the PV panels, which causes electromagnetic interferences (EMI) and potential safety issues. For the operation of TL-CHB-GCI, another requirement is to maintain power balance between PV modules. Existing methods have not been able to address both issues adequately, therefore, a hybrid multicarrier modulation (HMM) method is proposed in this article. First, the TL-CHB-GCI model with parasitic capacitance is established, and the constraints of the leakage current suppression and power balance are derived respectively, where the intersection of vectors under dual constraint is taken as the candidate vectors. Then, based on the set of candidate vectors, a hybrid carrier is constructed, and a cycling strategy is designed. In HMM, the vector optimization ensures low leakage current, while the carrier cycling achieves intermodule power balancing. Furthermore, the proposed method can be generalized to any even-module TL-CHB-GCI. Finally, the effectiveness of the proposed method is verified by simulations and experimental results.

Short-term load forecasting (STLF) plays an important role in real-time decision-making and management of the power system while is still a challenging task. Considering that sleep improves brain memories and cognitive processes, this paper explores a approach of integrating biological mechanisms to reduce information loss of networks, and hence proposes a sleep-induced network (SI-Net) by analogy for achieving high-performance STLF. Firstly, through mimicking the sleep process, a multi-level bionic flowchart of the SI-Net is designed to integrate the gated, attention, parallel, cooperative, and asynchronous mechanisms, which not only encode features from coarse to fine but also enhance the fitting capability at the feature layer. Secondly, through imitating the brain memory paths during sleep, the primary and secondary memory paths are designed to update and store information, respectively, and their independence and collaboration avoid information loss in the SI-Net. Thirdly, the loss function constructed by the Gaussian kernel makes nonlinear errors linearly separable in the high-dimensional space, being beneficial to train the SI-Net. The experiments with real-world load datasets are performed and the results show that the SI-Net outperforms 15 baselines and presents high accuracy and stability. Bionically-inspired ideas are promising to design high-performance forecasting networks for energy systems.

This study evaluated three blue hydrogen production processes - solar-grid powered, wind-powered, and thermal power grid (TPG) - considering the context of Hong Kong, SAR, China. A process sustainability analysis was performed based on energy, economic, and environmental (3E) factors. The energy efficiency analysis indicates that the TPG system is the most energy-efficient with 64% efficiency, followed by the wind power system at 63% and the hybrid solar-grid powered system at 60%. The economic analysis results indicate that the levelized cost of hydrogen (LCH) is 2.165 $/kg for the TPG system, 2.132 $/kg for the hybrid solar-grid powered system, and 2.060 $/kg for the wind power system. The environmental assessment suggests that wind powered are eco-friendly with a unit point total (µPt) of 1.11, compared to the solar-grid 1.50 µPt and TPG system's 1.70 µPt. Therefore, 3E analysis proposes wind powered process is more sustainable for blue H2 production.