Ultrasound technology is moving beyond the functional limits of traditional imaging and modalities, gradually developing into a new paradigm that integrates brain-state sensing, precise modulation, and system integration. This paper focuses on the representative advances made by Professor Zheng Yuanyi’s team in ultrasound tomography (UT), ultrasound localization microscopy (ULM), low-intensity pulsed ultrasound (LIPUS) neuromodulation, and wearable ultrasound systems. It systematically analyzes the technological evolution from single-modality imaging diagnostics to a closed-loop framework of “sensing—modulation—feedback”. A key contribution of this paper lies in the proposal of an ultrasound brain-computer interface (U-BCI) framework based on a unified acoustic carrier, promoting ultrasound from an auxiliary diagnostic and therapeutic tool to a medium for brain-computer interaction. This pathway provides a new framework for noninvasive deep neuromodulation, continuous physiological monitoring, and integrated in-hospital and out-of-hospital interventions, and offers a new direction for the clinical translation of brain-computer interfaces and the advancement of neurointerventional technologies.

To address the high penetration of renewable energy, it is necessary to advance the planning and operation of both renewable and coal-fired power, and to develop a source-grid-load-storage flexible planning method that considers uncertainty scenario generation. First, generative adversarial networks (GANs) are employed to generate seasonal (spring, summer, autumn, and winter) scenarios of wind and solar uncertainties. Based on a further analysis of renewable energy uncertainty and coal-fired power flexibility retrofit characteristics, a two-stage stochastic planning model with both long-term and short-term time scales is established, incorporating renewable energy and coal-fired power. This model explores flexible source-grid-load-storage planning schemes that account for renewable energy decisions and coal-fired power flexibility retrofit. Scenario accuracy indicators and insufficient flexibility indicators are used to investigate the correlation between uncertainty scenario generation methods and planning outcomes. Finally, the proposed method is validated via a practical case system. The proposed scheme exhibits a remarkable deep peak-regulation effect during severe noon peak-regulation periods in summer and autumn. It can accommodate large-scale renewable energy integration in the future and achieves superior economic performance for the source-grid-load-storage system.

Due to the ineffectiveness of fixed data window caused by time-varying frequency dynamics, an estimation for inertia of power systems based on variable data window is proposed. First, the process of power system inertia response is analyzed, and the auto-regressive with extra inputs model is used to depict frequency dynamics, on which a parameter identification model is developed. Next, the frequency measurement data are preprocessed based on the adaptive median-mean combined filter, and the fuzzy logic controller is used to set the parameters of the forgetting factor recursive least square method. Then, indices measuring the identification error and the difference of the results of successive windows are developed to realize the online variation of data window length and calculate the system inertia. Finally, case studies are conducted to validate the effectiveness of the proposed approach. The results show that the proposed approach can adapt to the changing inertia and is effective in reducing the inertia estimation error.

Large-scale integration of distributed photovoltaic (PV) systems into distribution networks can easily lead to line overloads, voltage instability, and other issues. The existing PV hosting capacity optimization methods, which mainly focus on economic performance, are often insufficient to ensure voltage stability in distribution networks. This paper proposes a quantitative method for assessing the hosting capacity of distributed PV in distribution networks based on static voltage stability margin (SVSM). First, an analytical expression for a static voltage stability index, characterized by PV output power, is derived, revealing a quadratic function relationship between distributed PV output and the static voltage stability index. Then, with the objective of maximizing PV integration capacity, a hosting capacity quantification model is established, considering constraints such as SVSM, distribution network line loss rate, voltage magnitude, and reactive power compensation devices. An improved adaptive genetic algorithm is used to solve this nonlinear model. Finally, the IEEE 33-node system is used as a case study to validate that the proposed method can effectively guide distributed PV deployment in distribution networks.

With the rapid development of the logistics industry, the load demand in logistics parks equipped with electric heavy-duty trucks and cold chain facilities has increased sharply, leading to higher overall operating costs. Therefore, a shared energy storage system is introduced as a third-party energy transaction platform, and a scheduling optimization method for multi-integrated energy systems (IESs) based on a game-theoretic pricing incentive mechanism is proposed. For the complementary characteristics between the high load demand of logistics parks and the renewable energy generation features of conventional integrated energy systems, the interaction strategies of all participants are pursued while their respective optimal objectives are addressed. A load-shifting model is constructed by analyzing the load characteristics of electric heavy-duty trucks and cold chain facilities. A flexible charging and discharging capacity-sharing rental model for energy storage is adopted, and an interaction model between the logistics park system with shared energy storage and conventional integrated energy systems is developed. Then, a dynamic pricing analysis based on a Stackelberg game is established to explore energy transaction interactions among multiple systems. The results show that the multi-system interaction model can meet the energy scheduling demands of electricity and cooling loads in logistics parks but also reduce wind and solar energy curtailment, and ensure the lowest cost across multiple energy systems. Compared with traditional models, the total cost of system achieves a 5.9% reduction in overall system costs.

To address the issues of limited profit methods and unstable earnings for virtual power plants (VPPs), the control of the grid-connected inverters within the photovoltaic systems of VPPs is optimized, enabling them to participate in the reactive power auxiliary services market to gain voltage regulation revenue, which ensures voltage stability under extreme weather scenarios and expands the revenue channels for the VPP. Additionally, a two-layer optimization model for VPPs to participate in the auxiliary services market considering demand response is established, with the upper layer being a comprehensive revenue maximization model for the VPP and the lower layer being a cost minimization model for the flexible load electricity consumption of the VPP. The model is solved by transforming the two-layer model into a single-layer model using Karush-Kuhn-Tucker (KKT) conditions. Case studies demonstrate that this strategy enables VPPs to secure stable profits in the auxiliary service market during extreme weather events, while remaining effective and adaptable under normal operating conditions.

Line icing caused by ice disasters significantly affects the safety of distribution network. The advent of microgrid with energy storage provides a new method to enhance the resilience of the system. This paper proposes a resilience enhancement strategy for the coordinated operation of microgrids and distribution networks, fully leveraging the emergency power supply capabilities of distributed generation and energy storage systems of microgrids. First, considering the fault evolution characteristics of transmission line icing disasters, a quantitative risk assessment model for icing on distribution network lines is developed. Next, a rolling optimization approach based on a hybrid long-short time scale is proposed, in which the long-term optimization focuses on cross-day microgrid energy storage pre-dispatch and distribution line repair strategies, while the short-term day-ahead optimization mainly addresses intraday uncertainties caused by renewable energy variability and network topology changes induced by icing-related fault evolution, with scheduling strategies generated using a column-and-constraint generation algorithm. The long-and-short time-scale optimization models are executed in a rolling manner over the whole ice disaster period. Finally, the effectiveness of the proposed resilience enhancement strategy is validated through simulation studies on a modified IEEE standard 33-node distribution network.

With the fast development of marine refrigerated transport business, the proportion of reefer container on container ships is increasing. To further improve the energy efficiency of container ships and reduce the full-voyage cost, an economic dispatching strategy for container ships considering reefer demand response is proposed. First, the operation characteristics of the reefer container is analyzed and its mathematical model is established. Then, a full-voyage economic dispatching framework of ship microgrid including voyage optimization, day-ahead dispatching, and reefer container dispatching is established. Variables continuous in time such as the state of energy storage equipment and vessel sailing distance are determined based on the voyage optimization. The day-ahead dispatching determines the generation plan of the generators and the virtual electricity price, and cooperates with the reefer container dispatching to realize the economic operation of the microgrid. A case study of the ship route from Hong Kong to Shanghai indicates that the proposed method can reduce the total voyage cost of container ships by 5.02% without increasing the investment cost, and realize the efficient operation of ship microgrid.

To address the rapidly growing demand for flexible ramping in power systems with the increasing penetration of renewable energy, an optimization operation strategy for a building-based virtual power plant (BVPP) is proposed to participate in energy and flexible ramping markets with the integration of demand-side flexible resources. First, flexible operation models for electric vehicle clusters and central air-conditioning systems are built to assess the flexible ramping supply capacity of the BVPP considering the physical characteristics of various devices within buildings, as well as environmental and human factors. Next, a day-ahead energy and flexible ramp market optimization operation model for BVPP is built based on conditional value-at-risk theory, considering the uncertainty in load and renewable energy output forecasts. Then, a risk effect factor is introduced and a revenue distribution mechanism within the BVPP is proposed based on a risk-modified Shapley method considering the impact of the risk preferences of BVPP on the revenue distribution among various entities. Finally, case studies are constructed to verify the economic and low-carbon advantages of the proposed BVPP market optimization operation strategy, as well as the rationality of the revenue distribution mechanism.

To address high carbon emissions in integrated energy systems (IESs) and under-utilized low-carbon potential of hydrogen, flexible devices such as power to gas (P2G), carbon capture, utilization and storage (CCUS), and hydrogen-doped gas equipment (HDGE) are introduced. An optimal dispatch model for IES with joint operation of P2G-CCUS-HDGE considering reward-penalty carbon trading is proposed. First, a P2G-CCUS-HDGE joint operation model is built, considering a dynamic hydrogen-doped ratio operation mode. Next, a reward-penalty stepped carbon trading mechanism is introduced to limit carbon emissions. Then, an optimization dispatch model is established considering relevant constraints and solved using Gurobi solver with the goal of minimizing the total operational cost. Finally, the proposed dispatch model is verified to reduce total costs, decrease carbon emissions, and improve renewable energy consumption via multiple scenario comparison. Additionally, the impact of different hydrogen-doped ratios and carbon trading parameters on the economy and low-carbon performance of the system is explored, providing insights into the low-carbon economic dispatch of IESs.

5G base stations are in a critical period of large-scale application, and high energy consumption poses economic challenges that hinder their development. At the same time, 5G base stations are usually equipped with energy storage batteries to ensure power supply reliability, of which idle energy provides flexible and dispatchable resources for the power grid. To reduce the power consumption of 5G base stations and make full use of energy storage resources, first, a 5G base station power consumption model is developed, and the impact of massive access of mobile users on backup energy storage and dispatchable energy of the 5G base station is analyzed. Then, a backup energy storage aggregation regulation model is established based on energy boundary projection. Finally, a cooperative game model is constructed considering the participation of 5G base station operators in power transactions, and solved by using the alternating direction multiplier method. The calculation example analysis shows that communication load transfer can effectively reduce the power consumption of 5G base stations in low load periods and increase the dispatchable energy of energy storage. The proposed distributed optimization operation model can effectively achieve mutual benefits for 5G base stations and other stakeholders.

To meet the requirements of large capacity energy storage optimal design, particulaly regarding optimization, a microgrid source-storage optimization method considering full-cycle chronological order characteristics is proposed. This method employs a clustering algorithm which preserves temporal features to reduce the volume of operation scenario, in two dimensions, while ensuring that the resulting typical scenarios can retain all chronological information from the raw data. On this basis, a microgrid source-storage optimization model incorporating dual time series is established. The sequence-preserving time series is used for constraints across the entire optimization cycle to ensure solution accuracy, while the reduced time series is applied to other constraints which do not require chronological order, thereby lowering model complexity. The simulation results show that the proposed method yields more accurate source-storage optimization results compared with other existing approaches while maintaining the same model complexity. Additionally, the proposed method significantly improves the optimization accuracy of large capacity energy storage, reducing the optimization error of hydrogen energy storage capacity by more than 16%.

With the increasing prominence of distributed characteristics driven by digital technologies extending to the distribution and utilization side, power terminals are evolving into “digital energy terminals” that provide object access and digital energy services at the edge. Cloud-native technology is the key technical foundation supporting this transformation. This paper analyzes the demand for digital energy terminals driven by a massive number of distributed objects and proposes the elements, architecture, and business organization of cloud-native digital energy terminals. Next, with the balance of computing supply and demand as the main thread, it analyzes related business models, computing resource models, and optimization methods for computing supply-demand matching of digital energy terminals. Finally, it envisions the technological ecosystem of cloud-native digital energy terminals. This paper attempts to unify existing work in the “cloud-native-energy and power terminals” domain under the framework of cloud-native digital energy terminals for next-generation power systems, highlighting that cloud-native technology will reshape the architecture, functionality, and operational model of digital energy terminals. This has significant implications for supporting the digital transformation of energy power systems and fostering a new ecosystem in the energy power industry.

Free metal particle defects are one of the frequent discharge defect types in gas insulated switchgear (GIS) equipment. Therefore, accurate estimation of their size and discharge severity is beneficial for understanding the insulation damage of equipment. In this paper, a method is proposed for estimating the size of internal metal particle in GIS through digital virtual test. First, the mathematical model of GIS metal particle jumping and its reciprocating motion is analyzed. Then, a relationship between particle size and its motion behavior is established by using the particle coordinate sequence obtained from digital inversion, and considering the back-and-forth motion characteristics and simulation analysis of metal particles. Finally, digital simulation traversal method and motion trajectory similarity analysis are proposed to achieve online estimation of metal particle size, and the simulation study verifies the feasibility and accuracy of the proposed method, which provides a basis for inferring the internal insulation condition of GIS equipment based on digital technology.

Gas insulated switchgear (GIS) and gas insulated transmission line are widely applied in high-voltage power transmission systems. However, internal insulator flashover faults critically undermine their safe and stable operation. Surface charge has been confirmed as a key factor affecting the flashover voltage of insulating components, but changes in the accumulation and dissipation characteristics of surface charges on insulators after prolonged operational aging remain unclear. To address this problem, an apparatus for measuring surface charge is constructed to examine the dissipation of surface charges under different thermal aging conditions, and to explore the dissipation characteristics at different voltage magnitudes and polarities. The results indicate that thermal aging increases the glass transition temperature of the epoxy samples, thereby reducing the rate of surface charge dissipation. At identical voltage magnitudes, the accumulation of surface charges is greater with the application of negative polarity voltage compared to positive polarity. The dissipation of surface charges on the insulator surfaces approximately follows an exponential decay, with a time constant ranging from 10⁴ to 10⁵ s. The findings on surface charge dissipation of epoxy resin samples under different conditions provide a novel perspective for assessing the aging degree of insulating components and can serve as a reference for fault analysis and reduction in actual engineering applications.

To address the high curtailment of wind and solar power, unstable output, and low economic efficiency in energy systems, a coordinated dispatch method for hybrid energy storage system in renewable-rich parks is proposed, aiming to improve economic benefits and reduce carbon emissions. The proposed method fully leverages the dynamic response characteristics capacity differences of different energy storage type. The hydrogen storage system (HSS) primarily handles large-power, small-fluctuation regulation demands, while the battery storage system (BSS) is utilized for the low power demand with high power fluctuation, and gas storage system (GSS) improves system flexibility of the hybrid energy storage, and operates for the power balance of the energy system. The method takes into full account of uncertainties of wind turbines (WTs) output, photovoltaics (PVs) output, local power demand, and electricity prices. During the cross-season dispatch stage, the state of charges of HSS and GSS are determined based on the typical seasonal predicted data. In the single-day dispatch stage, power dispatch instructions for the hybrid energy storage system are generated according to long-term daily prediction. In the real-time dispatch stage, the WTs output is adjusted based on single-day dispatch results and short-term predictions of uncertainty scenarios to formulate an optimal operation plan. The numerical results show that the proposed method effectively reduces wind and solar curtailment and carbon emissions, while enhancing economic efficiency.

With the promotion of the “dual carbon” goal, distribution networks are facing challenges posed by high penetration of distributed renewable energy. Efficient power flow analysis is thus essential for addressing these issues. However, existing back/forward power flow calculation methods are inefficient for large-scale networks, as determining the calculation sequence and constructing reactance matrix are extremely time-consuming. To solve this problem, a back/forward sweep power flow calculation method integrated with leaf nodes recursive pruning is proposed. By leveraging the topological characteristics of the distribution network, the method performs recursive pruning on leaf nodes to rapidly determine the calculation order for large-scale distribution networks with massive nodes. It also enables the efficient construction of the corresponding reactance matrix simultaneously, thereby effectively enhancing the efficiency of power flow calculations. Simulation results indicate that the proposed method outperforms existing layered and search-sorted back/forward power flow methods in terms of solution efficiency. The improvement is particularly pronounced for large-scale distribution networks, where the advantages are more evident.