Search Results (145)

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article

With the increasing penetration of renewable energy, the stochastic and intermittent nature of its generation increases operational uncertainty and vulnerability, posing significant challenges for grid stability. However, traditional algorithms typically identify critical nodes by focusing solely on the network topology or power flow, or by combining the two, which leads to the inaccurate and incomplete identification of essential nodes. To address this, we propose the Three-Dimensional Value-Based Gravity Model (3V-GM), which integrates structural and electrical–physical attributes across three layers. In the plane layer, we combine each node’s global topological position with its real-time supply–demand voltage state. In the line layer, we introduce an electrical coupling distance to quantify the strength of electromagnetic interactions between nodes. In the point layer, we apply eigenvector centrality to detect latent hub nodes whose influence is not immediately apparent. The performance of our proposed method was evaluated by examining the change in the load loss rate as nodes were sequentially removed. To assess the effectiveness of the 3V-GM approach, simulations were conducted on the IEEE 39 system, as well as six other benchmark networks. The simulations were performed using Python scripts, with operational parameters such as bus voltages, active and reactive power flows, and branch impedances obtained from standard test cases provided by MATPOWER v7.1. The results consistently show that removing the same number of nodes identified by 3V-GM leads to a greater load loss compared to the six baseline methods. This demonstrates the superior accuracy and stability of our approach. Additionally, an ablation experiment, which decomposed and recombined the three layers, further highlights the unique contribution of each component to the overall performance. Full article

With a high proportion of renewable energy sources connected to the distribution network, traditional optimal power flow (OPF) methods face significant challenges including multi-objective co-optimization and dynamic scenario adaptation. This paper proposes a dynamic optimization framework based on the Dynamic Gravitational Search Algorithm (DynaG) for a multi-energy complementary distribution network incorporating wind power, photovoltaic, and energy storage systems. A multi-scenario OPF model is developed considering the time-varying characteristics of wind and solar penetration (low/medium/high), seasonal load variations, and demand response participation. The model aims to minimize both network loss and operational costs, while simultaneously optimizing power supply capability indicators such as power transfer rates and capacity-to-load ratios. Key enhancements to DynaG algorithm include the following: (1) an adaptive gravitational constant adjustment strategy to balance global exploration and local exploitation; (2) an inertial mass updating mechanism constrained to improve convergence for high-dimensional decision variables; and (3) integration of chaotic initialization and dynamic neighborhood search to enhance solution diversity under complex constraints. Validation using the IEEE 33-bus system demonstrates that under 30% penetration scenarios, the proposed DynaG algorithm reduces capacity ratio volatility by 3.37% and network losses by 1.91% compared to non-dominated sorting genetic algorithm III (NSGA-III), multi-objective particle swarm optimization (MOPSO), multi-objective atomic orbital search algorithm (MOAOS), and multi-objective gravitational search algorithm (MOGSA). These results show the algorithm’s robustness against renewable fluctuations and its potential for enhancing the resilience and operational efficiency of high-penetration renewable energy distribution networks. Full article

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource constraints. Full article

The topology of distribution networks changes frequently, and the uncertainty of load level and distributed generator (DG) output makes the operation scenarios more complex and variable. Based on this, a fault recovery method for active distribution networks based on graph-based deep reinforcement learning is proposed. Firstly, considering the time-varying characteristics of DG output and load, a fault recovery framework for distribution networks based on a graph attention network (GAT) and soft actor–critic (SAC) algorithm is constructed, and the fault recovery method and its algorithm principle are introduced. Then, a graph-based deep reinforcement learning model for distribution network fault recovery is established. By embedding GAT into the pre-neural network of the SAC algorithm, the agent’s perception ability of the distribution network operation status and topology is improved, and an invalid action masking mechanism is innovatively introduced to avoid illegal actions. Through the interaction between the agent and the environment, the optimal switch action control strategy is found to realize the optimal learning of recovery under high DG penetration. Finally, the proposed method is verified on IEEE 33-bus and 148-bus examples and, compared with multiple baseline methods, the proposed method can achieve the fastest fault recovery at the millisecond level, and has a more efficient and superior recovery effect; the load supply rate under topology change increased by 4% to 5% compared with the benchmark model. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

The rapid devolopment of Internet of Vehicles (IoV) and Autonomous Connected Vehicles (ACVs) has increased the complexity of in-vehicle networks, exposing security vulnerabilities in traditional Controller Area Network (CAN) systems. CAN security faces dual challenges: stringent computational constraints imposed by automotive functional safety requirements and the impracticality of protocol modifications in multi-device networks. To address this, we propose a lightweight intrusion detection algorithm leveraging information entropy to analyze side-channel CAN message ID distributions. Evaluated in terms of detection accuracy, false positive rate, and sensitivity to bus load variations, the algorithm was implemented on an NXP MPC-5748G embedded platform through the AutoSar Framework. Experimental results demonstrate robust performance under low computational resources, achieving high detection accuracy with high recall (>80%) even at 10% bus load fluctuation thresholds. This work provides a resource-efficient security framework compatible with existing CAN infrastructures, effectively balancing attack detection efficacy with the operational constraints of automotive embedded systems. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

This study aimed to assess the power capacity of electrical conductors under the long-term expansion of distribution systems over a 10-year horizon by considering voltage quality constraints and reliability indicators. The MATPOWER library in Matlab was employed, along with the IEEE 15-bus and 33-bus distribution system test cases. A 5% annual load growth was simulated for each system, which involved analyzing key parameters, such as the power loss, voltage deviation, and average failure rate. An algorithm was developed to perform a multi-criteria analysis, which provided optimal solutions for the system behavior in response to increasing demand. Given the close relationship between distribution systems and load growth, voltage quality and reliability indicators were evaluated annually to identify improvement opportunities by taking into account economic factors, implementation timelines, the replacement of electrical components, and medium- and long-term investments. The proposed algorithm recommended upgrades to electrical conductors without significantly affecting the system costs. For the initial year of the IEEE 15-bus system, enhancements were suggested for lines L1–L2, L2–L3, L3–L4, L2–L6, L3–L11, and L11–L12, which allowed the system to operate without further modifications for five years, maintained the minimum voltages above 0.95 p.u., and reduced the average failure rate while demand continued to grow. Full article

With the integration of an increasing number of outward-facing components in intelligent and connected vehicles, the open controller area network (CAN) bus environment faces increasingly severe security threats. However, existing security measures remain inadequate, and CAN bus messages lack effective security mechanisms and are vulnerable to malicious attacks. Although encryption algorithms can enhance system security, their high bandwidth consumption negatively impacts the real-time performance of intelligent and connected vehicles. Moreover, the message authentication mechanism of the CAN bus requires lengthy authentication codes, further exacerbating the bandwidth burden. To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. Additionally, we have proposed a novel dynamic key management approach, incorporating a dynamic key distribution mechanism, which effectively resolves the challenges associated with key management. Each Electronic Control Unit (ECU) node independently performs compression, encryption, and authentication while periodically updating its keys to enhance system security and strengthen defense capabilities. Experimental results show that the proposed dynamic compression algorithm improves the average compression rate by 2.24% and enhances compression time efficiency by 10% compared to existing solutions. The proposed security protocol effectively defends against four different types of attacks. In hardware tests, using an ECU operating at a frequency of 30 MHz, the computation time for the security algorithm on a single message was 0.85 ms, while at 400 MHz, the computation time was reduced to 0.064 ms. Additionally, for different vehicle models, the average CAN bus load rate was reduced by 8.28%. The proposed security mechanism ensures the security, real-time performance, and freshness of CAN bus messages while reducing bus load, providing a more efficient and reliable solution for the cybersecurity of intelligent and connected vehicles. Full article

To address the current limitations of spaceborne solid-state storage systems that cannot effectively support the parallel storage of multiple high-speed data streams, the throughput bottleneck of NAND FLASH-based solid-state storage systems was analyzed in relation to the high-speed data input requirements of payloads. A four-stage pipeline operation and bus parallel expansion scheme was proposed to enhance the throughput. Additionally, to support the parallel storage of multichannel data and continuity of pipeline loading, the shortcomings of existing caching schemes were analyzed, leading to the design of a storage system based on Synchronous Dynamic Random Access Memory (SDRAM). Model simulations indicate that, under extreme conditions, the proposed scheme could continuously receive and cache multiple high-speed file data streams into the SDRAM. File data were dynamically written into FLASH based on the priority and status of each partition cache autonomously, without overflow during caching. The system eventually entered a regular dynamic balance scheduling state to achieve parallel reception, caching, and autonomous scheduling of storage for multiple high-speed payload data streams. The data throughput rate of the storage system can reach 4 Gbps, thus satisfying future requirements for multichannel high-speed payload data storage in spaceborne solid-state storage systems. Full article

With the increasing demand for power supply reliability, efficient load restoration in large-scale distribution networks post-outage scenarios has become a critical challenge. However, traditional methods become computationally prohibitive as network expansion leads to exponential growth of decision variables. This study proposes a multi-agent reinforcement learning (MARL) framework enhanced by distribution network partitioning to address this challenge. Firstly, an improved Girvan–Newman algorithm is employed to achieve balanced partitioning of the network, defining the state space of each agent and action boundaries within the multi-agent system (MAS). Subsequently, a counterfactual reasoning framework solved by the QTRAN-alt algorithm is incorporated to refine action selection during training, thereby accelerating convergence and enhancing decision-making efficiency during execution. Experimental validation using a 27-bus system and a 70-bus system demonstrates that the proposed QTRAN-alt with the Girvan–Newman method achieves fast convergence and high returns compared to typical MARL approaches. Furthermore, the proposed methodology significantly improves the success rate of full system restoration without violating constraints. Full article

This research presents a novel methodology based on data analysis for improving voltage stability in transmission systems. The proposal aims to determine a single distributed generator’s optimal location and sizing using the Fast Voltage Stability Index (FVSI) as the primary metric under $N-1$ contingency conditions. The developed strategy systematically identifies the most critical transmission lines close to instability through a frequency analysis of the FVSI in the base case and across multiple contingency scenarios. Subsequently, the weak buses associated with the most critical line are determined, on which critical load increases are simulated. The Distributed Generator (DG) sizing and location parameters are then optimized through a statistical analysis of the inflection point and the rate of change of the FVSI statistical parameters. The methodology is validated in three case studies: IEEE systems with 14, 30, and 118 buses, demonstrating its scalability and effectiveness. The results show significant reductions in FVSI values and notable improvements in voltage profiles under stress and contingency conditions. For example, in the 30-bus IEEE system, the average FVSI for all contingency scenarios was reduced by 26% after applying the optimal solution. At the same time, the voltage profiles even exceeded those of the base case. This strategy represents a significant contribution, as it is capable of improving the stability of the electrical power system in all $N-1$ contingency scenarios with overload at critical nodes. Using a single DG as a low-cost and highly effective corrective measure, the proposed approach outperforms conventional solutions through statistical analysis and a data-centric approach. Full article

In distribution networks with high penetration of distributed generators (DGs), traditional fault reconfiguration strategies often fail to achieve maximum load recovery and encounter operational stability challenges. This paper proposes a novel two-stage fault reconfiguration strategy that addresses both the fault ride-through capability and output uncertainty of DGs. The first stage introduces a rapid power restoration reconfiguration model that integrates network reconfiguration with fault ride-through, enabling DGs to provide power support to the distribution network during faults, thereby significantly improving the recovery rate of lost loads. An AdaBoost-enhanced decision tree algorithm is utilized to accelerate the computational process. The second stage proposes a post-recovery optimal reconfiguration model that uses fuzzy mathematics theory and the transformation of chance constraints to quantify the uncertainty of both generation and load, thereby improving the system’s static voltage stability index. Case studies using the IEEE 69-bus system and a real-world distribution network validate the effectiveness of the proposed strategy. This two-stage strategy facilitates short-term rapid load power restoration and enhances long-term operational stability, improving both the resilience and reliability of distribution networks with high DG penetration. The findings of this research contribute to enhancing the fault tolerance and operational efficiency of modern power systems, which is essential for integrating higher levels of renewable energy. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article