Electric vehicles (EVs), as carriers of moveable loads, can not only store energy during off-peak electricity consumption but also feed energy back to the grid during peak electricity consumption. However, along with the scale surge of the EV industry, massive EV charging-discharging behaviors present randomness and disorder characteristics, affecting the stable operation as well as the balance of supply and demand for the grid. Thus, this paper proposes a spatiotemporal bilevel charging-discharging scheduling strategy for urban EVs based on functional zoning. First, the EV charging demand, geographic characteristics, and node coupling degree within each functional region are analyzed. A functional zoning method based on reactive voltage sensitivity and modularity coefficients is proposed. Second, a spatiotemporal bilevel charging-discharging optimization scheduling model is established. The upper-level performs rolling prediction of EV charging-discharging time window length, whereas the lower-level provides spatial guidance of EV charging-discharging locations. Specifically, the upper-level utilizes long short term memory to predict EV charging-discharging time, minimizing the voltage deviation. The obtained temporal features are imported into the rolling optimization model, adjusting the EV charging-discharging time window length. Then, the lower-level inputs the optimal results of the upper-level. Additionally, differentiated EV user behavior guidance strategies are established based on the regret theory and the dynamic charging-discharging cost model, achieving spatial optimal scheduling. Finally, a modified binary gravity search algorithm is developed, integrating the binary coding mechanism, adaptive gravitational constant, and dynamic particle update scheme. Case studies are conducted within an IEEE-123 bus system. Numerous experimental results show that the proposed methodology optimizes the spatiotemporal distribution of urban EV charging loads and improves the operation efficiency of the grid. It provides a novel idea for the friendly interaction of vehicle-to-grid as well.

In response to the challenges where large-scale renewable energy integration leads to intricate source-network-load-storage elements and surging complexity in new power systems,rendering traditional balancing architectures and hierarchical analysis methods inadequate,theoretical achievements and research technologies regarding hierarchical and partitioned balance architectures are comprehensively reviewed.The adaptability requirements of new power systems for such architectures are elucidated,followed by a summary and comparative analysis of existing hierarchical control and partitioning strategies.Furthermore,layer-zone fusion mechanisms are explored,and existing technical limitations are analyzed from data and modeling perspectives.The results indicate that while renewable energy control pressures can be alleviated by existing strategies,deficiencies remain in handling massive heterogeneous data fusion and precise modeling of complex systems;moreover,high dynamic balance demands are difficult to be met by current layer-zone coordination mechanisms.Future hierarchical and partitioned balance architectures are identified as a critical direction for supporting the operation of new power systems.Notably,a novel technical pathway for achieving safe and efficient operation under the"carbon neutralization and carbon peaking"goals is offered by the introduction of large models and artificial intelligence technologies.

Delineating urban land use patterns and the close relation to transport has long been a core research topic, in which most are analyzed at aggregate level from a macroscopic perspective. This study tended to create a new homogeneity-related zone system from the basic grid cells and examining if the improved system outperforms than traditional zone systems when conducting aggregate analysis in transportation. Within ride-hailing schemes, the selection process of drop-off locations of each trip is fully recorded by the mobile transportation platform, which offers a promising way in addressing the spatial aggregation issue. Specifically, on the basis of destination retrieval record (DRR) data, this study explored the potential relevance among them and built homogeneity pairs. Based on the improved cluster algorithm, homogeneity pairs are used to aggregate basic grid cells into the homogeneous traffic analysis zones (HTAZs) that are high in inter-zone consistency. The third ring area of Chengdu City as a case study was divided into 260 HTAZs using the proposed partition approach. Newly developed homogeneity-related zone system performs better than traditional systems in terms of geometry and internal consistency. Findings from this study also suggest that the zone partition should maintain internal consistency, namely homogeneity, as much as possible so that the aggregate-level spatial units can reflect and summarize the features of intra-zone individuals. This study demonstrates the mining potential of trip record data within transport systems, and provides a more reliable spatial structure to reveal travel patterns and conduct transportation design.

Individual vehicle travel carbon dioxide (CO2) emission (CE) trajectories were crucial for targeting high-emitters for precise control and guiding low-carbon travel. Variations in CE arise from vehicle performance, traffic conditions, and trip purposes. Using real Automatic Vehicle Identification (AVI) data and integrating multi-source vehicle, road, trip, and environmental data, this study proposed an "Identification-Calculation-Evaluation" framework to quantify and analyze city-scale full-individual vehicle CE trajectories. A case in Xuancheng, China, was conducted and revealed spatiotemporal CE heterogeneity. The results showed that approximately 50 % of CE was contributed by the top 5 % of high-emission vehicles, exhibiting a significant "Pareto Principle". Among the top 5 % of high-emission vehicles, LPC-gasoline (57 % of vehicles, 40 % of CE), HDT-diesel (32 %, 42 %), and Taxi-gasoline (5 %, 12 %) were the main contributors. Their daily CE trajectory ranges were [0, 6] kg, [0, 15] kg, and [0, 8] kg, respectively. Taxi-gasoline and HDT-diesel exhibit more individual variation. Peak-time CE trajectories on these Top 5 % vehicles were 2-6 times higher than off-peak. For LPC-gasoline and Taxi-gasoline, over 60 % of CE occurred during congestion links. Peak times of CE trajectories occurred around 7:00 and 17:00 on a day, with spatial hotspots predominantly concentrated in urban core areas. Notably, Taxi-gasoline vehicles exhibited more clustered hotspots. HDT-diesel CE trajectories peaked earlier (6:00-7:00), with hotspots distributed along major urban corridors, and CE was 1-3 times higher than in ordinary areas. This study provided precise support for low-carbon traffic governance, and the framework could be extended to other cities to inform carbon reduction strategies.

Aiming at the fact that the traditional Maximum Power Point Tracking (MPPT) control method is prone to fall into local optimum in multi-peak states, resulting in low output efficiency of photovoltaic systems and energy loss, a photovoltaic MPPT control method combining an Improved Whale Migrating Algorithm and Perturb and Observe (IWMA-P&O) method is proposed. This method uses the improved Whale Migrating Algorithm (WMA) to track the global maximum power point of the photovoltaic power generation system. When approaching the maximum power point, the Perturb and Observe (P&O) method is used for precise search, effectively avoiding output power oscillation and improving the search accuracy. Simulations are implemented by MTALAB/Simulink. The results show that the improved whale migrating algorithm has better effects in terms of tracking speed and accuracy, and has better robustness.

In recent years, the rapid development of electric vehicles (EVs) has likewise led to the construction of EV charging infrastructure, so the research on charging reliability and safety of EV charging facilities has become a focus of attention. However, most of the data used in existing research are complete and sufficient simulation data, when faced with actual data, the prediction accuracy is often affected by insufficient or incomplete data. To solve these problems, a data-driven approach is used to achieve early warning of faults during the charging process of charging equipment. Firstly, feature selection is performed to select appropriate data features. Secondly, the order data is filtered, the dataset is constructed and normalized. Secondly, the dataset is divided into a training group and a test group, the training group is used for model training, and the test group is used to judge the advantages and disadvantages of model training. Then, the divided training group is augmented with generative adversarial networks (GAN) to expand the data size and form a sufficient amount of new data. Subsequently, the data are inputted into bi-directional long-short term memory (Bi-LSTM) and the initial parameters are optimized using particle swarm optimization (PSO). A number of trials are conducted to observe the results of the modeling tests. Finally, in comparison with other prediction models, it is verified that the GAN-PSO-Bi-LSTM model has higher prediction performance, which improves the fault prediction accuracy of charging piles.

This study investigates the optimal market trading strategy for community-based photovoltaic (PV) prosumers by leveraging shared energy storage (SES) and controllable loads. Specifically, a joint optimization framework is proposed for the community-based PV prosumers, combining resource configuration and market decision-making; concurrently, load resource configuration is modeled using a dual-incentive mechanism. Considering the multi-time scale characteristics of PV-storage-load resources, a bi-level optimal trading model is developed. The upper level determines the optimal annual capacity configurations of both the SES and controllable loads under typical scenarios, while the lower level optimizes seasonal power declarations, daily storage strategies, and incentive-driven load responses. An economic allocation model factoring the SES recovery preference is introduced for cooperative profit-sharing among PV prosumers, ensuring market participation of every prosumer. Comparisons and analyses of different case scenarios demonstrate that the proposed model increases annual revenue by 15.06 % compared to a baseline model without controllable resources. Moreover, simulation results confirm that the proposed approach can effectively enhance the market competitiveness of PV prosumers and enable equitable distribution of shared profits.

Channel prediction is an effective technology to support adaptive transmission in wireless communication. To solve the difficulty of accurately predicting channel state information (CSI) due to fast time-varying characteristics, a next-generation reservoir calculation network (NGRCN) is combined with CSI, and a channel prediction method for OFDM wireless communication systems based on an adaptive reinforced reservoir learning network (adaptive RRLN) is proposed. An adaptive elastic network (adaptive EN) is used to estimate the output weight matrix to avoid ill-conditioned solutions. Therefore, the adaptive RRLN has echo and oracle properties. In addition, an adaptive singular spectral analysis (adaptive SSA) method is proposed to improve the local predictability of CSI by decomposing and reconstructing CSI to improve the fitting accuracy of the channel prediction model. In the simulation section, the OFDM wireless communication systems are constructed using IEEE802.11ah and the one-step prediction, the multi-step prediction, and the robustness test are implemented and analyzed. The simulation results show that the prediction accuracy of the adaptive RRLN can reach 3 × 10−5 and 8.36 × 10−6, which offers satisfactory prediction performance and robustness.

In automated melt-casted Aluminium production process, lots of carbon dioxides are emitted. How to accurately calculate those carbon emissions is a challenging problem. In this paper, we mainly introduce a carbon emission calculation method based on global power consumption for the automated melt-casted Aluminium production process. Firstly, we analyse the carbon life cycle of the melt-casted Aluminium products and the carbon emission calculation boundary. Then, the power consumption based-calculation method of the carbon emissions of different stages of the melt-casted Aluminium product are explained in detail and finally, the total emissions are offered. The simulation results indicate that the global power consumption based-carbon emission calculation method works well in the automated melt-casted Aluminium product process.

This paper investigates the robust semiglobal containment control problem for multi-agent systems composed of linear agents with heterogeneous parameter uncertainties and input saturation constraints. A novel low-gain-based nonlinear containment control algorithm is proposed and analyzed. Theoretical analysis demonstrates that the semiglobal containment control objective can be achieved when the relationship between the lower bound of input saturation and the upper bound of parameter uncertainties satisfies specific conditions, which are explicitly characterized as design constraints in the proposed method. Furthermore, the control protocol is extended to an event-triggered framework, incorporating both the containment control law design and node-based event-driven updating mechanisms.

The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction of protection relays, etc. To ensure the reliable and flawless operation of active distribution networks, an electrical device enabling active network management is necessary, and a hybrid distribution transformer offers a promising solution. This study introduces a novel hybrid transformer topology and multi-mode control strategy to achieve coordinated voltage and reverse power regulation in active distribution networks. The proposed hybrid transformer combines conventional transformer windings with a partially rated SiC-MOSFET-based back-to-back converter, reducing additional investment costs and enhancing system reliability. A multi-mode control strategy is proposed to facilitate the concurrent reverse power control and voltage violation mitigation of the presented hybrid transformer, allowing a smooth transition between the P–Q control mode and the V–f control mode. The control mode switching can be activated manually or autonomously in response to voltage violations or reverse power overloading. The effectiveness of the proposed hybrid transformer configuration and its control mode transition mechanism are examined through comprehensive case studies conducted in the PSCAD/EMTDC environment. The proposed HT design has been confirmed to achieve a voltage regulation range of ±20% of the nominal voltage and effectively regulate bidirectional active power flow within a range of −25% to 25% of the rated power.

The structure of a DC–DC converter connected in series with a 400 Hz inverter is widely used in aviation airborne and ground power systems to provide a medium-frequency AC power supply within a wide input voltage range. To guarantee the dynamic and steady-state performance of a multi-module series converter, it is necessary to individually optimize the control loop and the parameters of each series-connected module. In addition, it is also necessary to consider the input–output impedance matching issues of each converter. In this study, a phase-shifted full-bridge converter employing an average current control strategy is used as the input-side DC–DC converter, and a medium-frequency three-phase four-leg inverter is used as the output-side inverter. The study also conducts a small-signal modeling analysis to reveal the intrinsic relationship between the closed-loop output impedance of the input-side converter and the circuit parameters. It summarizes the optimization criteria for controlling the parameters of the voltage and current loops. By suppressing the peaks of the output impedance of the input-side converter, the impedance matching between the input- and output-side converters is ensured over the entire frequency range, ensuring the stable operation of the overall system. Finally, the correctness of the theoretical analysis is verified through simulation and experimentation.

In this letter, we mainly focus on the significant multipath component identification issue for orthogonal frequency division multiplexing (OFDM) wireless communication systems in a fast time-varying scenario with a low signal-to-noise ratio (SNR). Considering the time-domain channel prediction demand of OFDM systems, we introduce the joint singular spectrum analysis (JSSA) to identify the significant multipath components of the channel impulse response (CIR) by investigating the local predictability of the multipath component in detail. The JSSA has two parts, i.e., the random SSA (R-SSA) and the predictable signal reconstruction (PSR). The former is used for multipath component decomposition, while the latter is used to reconstruct the multipath component based on the local predictabilities of those subcomponents. Therefore, the JSSA can reduce the negative effect of noise and solve the significant multipath component identification issue. The simulations indicate that the JSSA in our letter has good identification performance in the given communication scenario.

The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source — load — storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

As the COVID-19 pandemic worsened, many people saw bikes as one of the safest means of transportation in the hard-hit cities. All the bike sharing utilization patterns during the pandemic are worthy of careful attention. However, there is still a lack of comprehensive understanding of niche but notable cycling behaviors, such as multi-person round trip (MPRT), defined as two or more cyclists intentionally riding together then returning bikes to the original docking station. This study extends the relevant literature by firstly proposing a MPRT identification framework based on individual bike sharing trip records, with consideration of interpersonal relationships between co-travelers, as well as the specificity of round trips against one-way trips. Taking New York City as a case study, this study examines the changes over space and time of MPRT frequencies from 2019 (i.e. pre-pandemic period) to 2020 (i.e. pandemic period), and the reasons for it. Notably, special consideration of the aforementioned analysis is paid to the influence of the real-time situation of COVID-19 in terms of cases, deaths, hospitalizations, and tests. Results reveal that (1) the MPRT frequencies obey a long tail distribution, both prior to and during the COVID-19 outbreak; (2) the group size, temporal patterns and co-traveler community are profoundly affected by the COVID-19 outbreak; (3) four indicators related to COVID-19 show different influences on co-travelers over time; (4) bike sharing availability and personal economic situation are closely related with MPRT frequencies. These findings can help develop more targeted strategies for improving the operation of a bike sharing system to meet the possible diversified demands of cyclists during the future pandemics.

To address effectively the stochastic charging demand of electric vehicles, this paper proposes a charging guidance strategy for electric vehicles based on hierarchical multi-agent deep reinforcement learning. This strategy refines the charging guidance task into a double-layer finite Markov decision process for the complex feature information in the coupled system of electric vehicles, charging stations, and transportation networks. In this architecture, the upper network adopts a centralized learning and decentralized execution strategy, and each electric vehicle is regarded as an independent agent. These agents cooperate in diverse incentives and competitive environments to jointly recommend the best charging station locations. At the same time, the action decision outputs of the upper network are integrated into the state considerations of the lower multi-agents. The lower network adopts the same solution method as the upper network, and the lower multi-agent plans the optimal driving paths based on the latest EV and traffic network states after receiving the upper decision. Further, a multi-agent hybrid Q learning algorithm is introduced to solve the problem, and constraints are imposed on the agent rewards to guide the multi-agent learning. Finally, simulation results validate that the proposed strategy enhances electric vehicle charging efficiency and promotes efficient road network operation.

Bicycle-metro integration, in which bicycling is used as a flexible feeder mode to connect with public transport nodes presents new opportunities for sustainable transportation. It is known that the built environment can influence travel attitudes and choice, yet the empirical evidence for the role of built environment features in shaping the bicycle-metro integration remains rare. Inspired by the idea of text mining, this article is an attempt to demonstrate a data-driven semantic framework to capture key topic-based features of land use and bicycle-metro integrated usage in the vicinity of metro stations as well as their interactions. Latent Dirichlet Allocation topic modeling is analogously implemented here to generate a range of probability-based land use patterns and mobility patterns, and the associations between them are investigated by multivariate linear regression. A case study from Shanghai shows that the mixed land use and diversification of urban functions in the catchment areas of the metro stations can be detected effectively by 11 identified land use patterns. Based on 7 derived mobility patterns, this paper gives a probabilistic explanation to the time-varying properties of the bicycle-metro usage. All of the above thematic topics exhibit notably heterogeneous patterns in spatial distribution. The topic compositions in terms of land use pattern and mobility pattern at the station level reveal the current performance of station areas. Plus, results from the regression analysis confirm that most of the land use patterns that are related to various mixed use have close relationships with mobility patterns of bicycle-metro integration. Yet it is noteworthy that the effects of land use patterns often differ and change over time, namely affecting different mobility patterns. This study gives rise to alternative insights into the synergy between bike sharing and metro, which may help policymakers to develop more targeted TOD strategies.

With accelerating grid decarbonization and technological breakthroughs, grid-connected photovoltaic (PV) systems are continuously connected to distribution networks at all voltage levels. As the grid interaction interfaces between PV panels and the distribution network, PV inverters must operate flawlessly to avoid energy and financial losses. As the failure of semiconductor switches is the leading cause of abnormal operation of PV inverters and typically cannot be detected by internal protection circuits, this paper aims to develop a method for the autonomous diagnosis of semiconductor power switch open-circuit faults in three-phase grid-connected PV inverters. In this study, a ReliefF-mRMR-based multi-domain feature selection method is designed to ensure the completeness of the fault characteristics. An NGO-HKELM-based classification method is proposed to guarantee the desired balance between generalization and exploration capability. The proposed method overcomes the common problems of poor training efficiency and imbalances between generalization and exploration capabilities. The performance of the proposed method is verified with the detection of switch OC faults in a three-phase H-bridge inverter and neutral-point-clamped inverter, with diagnostic accuracy of 100% and 99.46% respectively.

Establishing a reasonable, feasible, and directional incentive carbon points operation model for residents is an effective means to enhance the low-carbon awareness of residents. This paper introduces dynamic carbon emission factors and power characteristics of carbon valley, and a carbon credit-oriented incentive decision-making method for residents to promote valley filling is proposed. Firstly, based on the carbon quota allocation model, considering three factors: monthly carbon quotas, basic carbon emission and incentive carbon credits, a residential carbon credits-oriented incentive model is proposed to promote carbon valley filling. Secondly, the carbon valley electricity consumption ratio is established, and the K-means algorithm is used to cluster residents. Then, considering the constraints, such as the average monthly carbon credits of residents, the proportion of below-zero credit residents and difference between the mean carbon credits of different clusters, a resident carbon credit decision-making model is constructed, and the Grey Wolf Optimization is utilized to solve it. Finally, a numerical example is given to verify the rationality and target incentive of the proposed model.

With the introduction of high-density distributed photovoltaic (DPV) into the distribution network, the reasonable evaluation of the voltage stability during the operation of the distribution network has become a research hotspot. In this paper, firstly, the influence of distributed photovoltaic on the voltage after it is connected to the distribution network is analyzed. Then, by building the hybrid simulation platform of MATLAB and Opendss, the change of the operation state of the typical distribution network when it is connected to the photovoltaic energy of different permeability and different capacity is analyzed. Two key nodes affecting the photovoltaic access capacity are introduced. The accuracy of the theoretical analysis was verified by the time series simulation analysis, and the general rule of the high-capacity photovoltaic access scheme was obtained based on the simulation results.