Efficient data reduction techniques, including deduplication and compression, are essential in storage systems, affecting performance and longevity. Existing data deduplication approaches often focus on intra-SSD deduplication, missing opportunities for cross-node deduplication, or have scalability issues when aiming for low latency and high-throughput data reduction on large-scale, distributed SSD arrays. We propose StreamDedup, a distributed stream accelerator implementing a transparent layer of deduplication as a network-attached, middle-tier service between the compute and storage tiers. StreamDedup manages all aspects of data deduplication and compression and can be seamlessly integrated into existing systems. It is RDMA-enabled and highly scalable, enhancing data processing capacities for large-scale storage systems. Our prototype, deployed on FPGAs, demonstrates that StreamDedup achieves a throughput of 12.7 GB/s on a single node, matching the network bandwidth of disaggregated storage, with a latency of less than 50 µs. Across 10 nodes, StreamDedup shows an almost linear increase in throughput with less than 60 µs of latency.

This article proposes a novel spatial–temporal resilience assessment scheme for the distribution system (DS) to address the issue of overoptimistic and potentially misleading assessment due to the imprecision of a single typhoon wind speed model and the neglect of the coupled impact of typhoon rainstorm. First, a dynamic weighted iterative algorithm (DWIA) is proposed for accurate modeling of the typhoon wind speed, and a DWIA-based equivalent wind speed (EWS) model is further proposed for improving the accuracy of wind speed calculation under the coupling effect of typhoon rainstorm. Then, EWS-based uncertain failure probability models are developed for passive and active components, such as distribution lines, poles, and photovoltaics, and the adverse impacts of typhoon coupled with rainstorm events (TCREs) on the outputs of active components are considered. Furthermore, a set of normalized resilience metrics is defined, and sequential Monte Carlo simulation is adopted to evaluate the DS resilience against the TCRE as it travels inland. Finally, the modified IEEE 33-bus system and the actual distribution system from Southern China affected by the TCRE are both tested and analyzed to validate the effectiveness of the proposed scheme. The numerical results show that the proposed scheme can effectively quantify the adverse impact of the TCRE on the DS, and provide assistance for the proactive resilience control of the DS.

This paper proposes a distributionally robust optimal operation methodology to coordinate multi-energy interactions and facilitate the emission mitigation for multi-energy systems (MESs) with uncertainty couplings in electricity and carbon markets. A carbon recycling model is proposed to exploit the operational flexibility of multi-energy synergies to enhance economic profits of MES operators under market incentives. Then, a generalized cost model incorporating the lifetime cost of carbon capture and power-to-gas degradation is formulated to provide a quantitative analysis for the coordinated electricity and carbon trading. The co-movements of price fluctuations in electricity and carbon markets are explored through a tailored explainable neural network and uncertainty couplings in the markets are further revealed by a Clayton copula based joint probability distribution (PD) model of price prediction residuals. Moreover, a distributionally robust optimization method is formulated for the optimal coordinated operation of MESs to cope with uncertainties from interrelated fluctuating prices. Numerical studies corroborate the effectiveness and superiority of the proposed methodology in the enhancement of economic and environmental benefits.

In modern Internet of electric energy, i.e., networked power systems, data-driven schemes based on advanced machine learning methods have shown high potential in system emergency stability control, e.g., undervoltage load shedding (UVLS) against the short-term voltage stability (SVS) problem. However, how to efficiently and adaptively select the most effective UVLS sites for online SVS enhancement is still a challenging task. Faced with this issue, this paper develops an intelligent short-term voltage trajectory sensitivity index (VTSI) prediction scheme for adaptive UVLS site selection. Specifically, the scheme is realized by designing a powerful structure-aware recurrent learning machine (SRLM), which systematically combines the emerging graph convolutional network (GCN) with the recurrent long short-term memory algorithm. By doing so, the SRLM is not only fully aware of the non-Euclidean structure of the power grid, but also capable of amply capturing temporal features during SVS dynamics. Consequently, it manages to implement efficient and precise VTSI prediction, thereby reliably identifying critical UVLS sites in various scenarios. Numerical case studies on the Nordic test system illustrate the efficacy of the proposed scheme.

In industrial informatics-enabled smart grids, machine learning approaches have exhibited high potential in data-driven electricity theft detection (ETD), whereas none of the existing studies pay sufficient attention to the high costs of manually labeling massive sensing data during learning data preparation. To address this defect, this article develops a cost-effective data-driven ETD approach that significantly reduces the data labeling costs without sacrificing the reliability of ETD. Specifically, the approach is systematically realized via an intelligent deep active learning (DAL) scheme. By seamlessly incorporating convolutional neural network (CNN) learning with Monte Carlo dropout-based Bayesian active query, the DAL scheme efficiently selects the most valuable instances for ETD model training. In this way, the proposed approach is able to derive a reliable CNN-based ETD model with limited labeled learning instances, thus largely reducing the data labeling costs. Experimental test results on an actual ETD dataset provided by the State Grid Corporation of China extensively illustrate the efficacy of the proposed approach.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

Spiking neural networks (SNNs) are highly computationally efficient artificial intelligence methods due to their advantages in having a biologically plausible computational framework. Recent research has shown that SNN trained using backpropagation (SNN‐BP) exhibits excellent performance and has shown great potential in tasks such as image classification and security detection. However, the backpropagation method limits the dynamics and biological plausibility of the neural models in SNN, which will limit the recognition and simulation performance of SNN. In order to make neural models more similar to biological neurons, this study proposes a leaky integrate‐and‐fire (LIF) neuron model with dense intralayer connections, as well as efficient forward and backward processes in BP training. The new model will make the interaction between neurons within the layer more frequent, enhancing the intrinsic information exchange capability of SNN. An effective probabilistic spike‐timing dependent plasticity (STDP) method is also proposed to reduce the overweighted connections between neurons, as well as a hybrid training method using BP and probabilistic STDP. The training method combines the advantages of BP and STDP to improve the performance of SNN models. An intralayer‐connected SNN with hybrid training (ISNN‐HY) is proposed with the combination of these improvements. The proposed model was evaluated on three static image datasets and one neuromorphic dataset. The results showed that the performance of ISNN‐HY is superior to that of other SNN‐BP models. The proposed method also makes it possible to accurately simulate biological neural systems.

This article proposes a multilateral multienergy trading framework for synergetic hydrogen (H2) and electricity transactions among renewable-dominated hybrid charging stations (HCSs). In this framework, each autonomous HCS with various renewable energy resource (RES) endowment can harvest local renewables for internal green H2and electricity generation to simultaneously meet demands of electric vehicles (EVs) and hydrogen-powered vehicles (HVs) from the transportation network. The surplus electricity/H2production of the HCS is accommodated by external multilateral transactions to increase the additional profit. Besides, each HCS is modeled as a sustainable energy hub, and multiple hubs with multienergy transactions contribute toward a low-carbon energy-transport nexus. A partial differential equation model based on fluid dynamic theory is formed to capture the temporal and spatial dynamics of traffic flows for estimating the EV/HV loads at HCSs. Furthermore, a distributed multilateral pricing algorithm is developed to iteratively derive the optimal prices and quantities for transactive electricity and H2. Comparative studies corroborate the superiority of the proposed methodology on economic merits and RES accommodation.

In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information.

In this paper, we propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional distributed algorithms implement voltage control only when the algorithms converge. However, our proposed algorithm is able to carry out voltage control immediately. In particular, we derive a closed-form solution for PV controllers to locally update the active and reactive power set points aiming at minimizing the total loss and maintaining bus voltages within the acceptable ranges. The optimality is guaranteed and the convergence is established analytically. Moreover, our proposed algorithm only requires the information exchange between neighboring PV systems, thus reducing communication complexity. Finally, numerical tests on IEEE 37-bus distribution system verify the effectiveness and robustness of our proposed algorithm.

A new classification logic for land cover at regional scale is proposed. The critical features of this classification are that: it is based on spectrum (color) and primary attributes of plant-canopy structure, that are important to globe change modeling and can be measured in the field for validation or/and by remote sensing; according to the phonological difference among broadly defined vegetation, some typical land cover is easily distinguished by using the characteristics of land cover with the change of seasons; mixed land cover is differentiated by its constituent characteristics and influence on land surface processes. A first test of this logic for the middle Qinling mountains in Shanxi province, China is presented based on time-series MODIS 250 m NDVI imageries. Seven basic classes and eleven sub-classes were identified and mapped for the study area. The overall classification accuracy equals to 74.41% and overall kappa statistics 66.65%.