International Transactions on Electrical Energy Systems最新文献

Multiport DC-DC converters are essential for modern renewable energy systems where the integration of multiple energy sources and dynamic loads demands flexible, reliable, and efficient power management. However, conventional two-port converter topologies face significant limitations when addressing high-power applications exceeding 600 W, particularly under fluctuating input and load conditions. To overcome these challenges, this paper proposes a novel four-port nonisolated bidirectional DC-DC converter designed specifically for solar photovoltaic (PV) and grid-integrated energy systems. The converter supports multiple operating modes—Single Input Triple Output (SITO), Single Input Double Output (SIDO), Double Input Double Output (DIDO), and Single Input Single Output (SISO)—allowing adaptable power flow between solar PV, energy storage systems (ESS), DC loads, and AC loads via an inverter. A key innovation of this work lies in the use of a cost-effective Arduino UNO microcontroller to govern the MOSFET-based switching system. Compared to conventional control techniques, the Arduino-based controller significantly reduces complexity, cost, and component count while improving switching efficiency. The converter architecture further minimizes switching losses by employing fewer switches, enhancing overall performance for high-power applications. The system is simulated under both open-loop and closed-loop configurations using PSIM and Proteus software to evaluate functionality across various operational states and load conditions. A hardware prototype is developed to experimentally validate the simulation results under real-world constraints, including switching losses, voltage drops, and parasitic effects. The comparative analysis reveals a 10% average deviation between simulation and hardware results, which is within acceptable limits for practical deployment. Across all operating modes, the converter maintains stable power delivery, demonstrating high reliability and system adaptability. The results confirm that the proposed four-port converter is well-suited for solar PV-powered systems, energy storage integration, and electric vehicle (EV) applications, offering enhanced scalability, control simplicity, and energy transfer efficiency.

The study optimizes the location and capacity of soft open points (SOPs) and renewable energies-based distributed generators (REDGs) in two IEEE distribution power grids (DPGs) with 33 and 69 nodes for reducing power loss, energy loss, and total electric purchase cost by using war strategy optimization (WSO). The previous studies put one SOPs device each on several predetermined selections while the locations of SOPs are optimally determined in the study. So, WSO can find smaller losses than in previous studies for the two grids over 1 h. Then, the WSO is run to reduce 1-day energy loss and grid energy purchase cost for the IEEE 69-node DPG. The grid with renewable power sources (RPSs) and SOPs can reduce the energy loss by 3638.6229 kWh (about 96.1%) compared to the original grid and 1257.2779 kWh (about 89.49%) compared to the grid with RPSs. In addition, the grid with SOPs and RPSs can reach a 3869.9684 and $246.0011 smaller cost than the grid without and with RPSs. The cost reduction is about 61.44% and 9.2% of the total cost of the grids. So, optimal connection and capacity determination of SOPs and REDGs in DPGs is essential to reduce energy loss and energy purchase costs from conventional power grids.

The dependability and robustness of electric vehicle (EV) charging infrastructure, which functions as a pivotal nexus between consumers and the electrical network, play a crucial role in enhancing the efficiency of EV usage and ensuring the safe management of the power grid. Therefore, it is highly urgent to develop an effective and robust anomaly detection system to provide early warnings of potential risks and address the issue of imbalanced data distribution. In this paper, a conditional variational autoencoder (CVAE) is employed to construct an anomaly detection model for charging pile data. In contrast to other anomaly detection methodologies, the method proposed in this study demonstrates more desirable performance. Furthermore, this paper extends the investigation by modifying the architecture of the CVAE to facilitate supervised learning. The reconfigured network structure yields enhanced detection accuracy, obtaining better anomaly detection performance when evaluated on the charging pile dataset.

The port-integrated energy system (PIES) presents a transformative pathway for decarbonizing port operations through multienergy synergies. Power-to-hydrogen technology converts surplus renewable energy into green hydrogen, which is stored and reconverted to electricity via fuel cells during supply shortages. However, joint optimization of heterogeneous ports, with divergent resource endowments and load profiles, remains challenging. This study proposes an optimal scheduling method for electricity–hydrogen–heat–integrated energy systems accounting for port-specific energy characteristics and establishes a multienergy coupling model of PIES with vessel-based mobile hydrogen storage and a cooperative optimization framework incorporating energy consumption dynamics. Simulation results demonstrate that multiport joint dispatch reduces total operating costs by 2.3%–8.2%, compared to isolated schemes, while hydrogen-powered vessels enable spatiotemporal arbitrage and serve dual roles as mobile energy carriers/temporary storage units, with hydrogen interchange critically constrained by vessel logistics scheduling.

This study proposes an advanced hybrid fault-tolerant control (FTC) architecture for permanent magnet synchronous motors (PMSMs) operating under speed sensor faults (SSFs), integrating model predictive control (MPC), third-order sliding mode control (TOR-SMC), and a model reference adaptive system (MRAS). The key innovation lies in the synergistic combination of MPC’s predictive optimization with the robustness of TOR-SMC and the real-time adaptive estimation capability of MRAS, enabling reliable operation in the presence of sensor degradation or failure. A residual-based fault detection mechanism is embedded to monitor discrepancies between actual and estimated rotor speeds, enabling rapid fault identification and seamless transition to observer-based control. The proposed hybrid control system is designed within a hierarchical architecture, wherein MPC optimizes inverter switching actions, TOR-SMC ensures robust disturbance rejection and chattering suppression, and MRAS delivers high-fidelity speed estimation. Simulation studies under various operating scenarios—encompassing step changes in speed, variable torque loads, and fault scenarios—demonstrate that the system achieves a maximum speed estimation error of 1.8%, speed tracking accuracy of 97.6%, and a fault detection time of less than 2.5 ms, which is 41.3% faster than extended Kalman filter (EKF)–based schemes. Quantitatively, the proposed method reduces torque ripples by 32.5%, current overshoot by 35.7%, and transient response time by 27%, while improving overall fault tolerance by 63% compared to conventional FTC approaches. The TOR-SMC module contributes to a 78% reduction in chattering and ensures stable electromagnetic torque behavior even under dynamic torque disturbances. In parallel, MRAS offers faster convergence (2.5 ms) and smoother transitions compared to SMO and EKF observers, maintaining control integrity despite sensor anomalies. This comprehensive and modular FTC approach addresses a critical vulnerability in PMSM drive systems and is particularly well-suited for deployment in electric vehicles, aerospace systems, and renewable energy platforms, where high reliability, real-time responsiveness, and robustness to sensor degradation are paramount. The results confirm the proposed hybrid MPC–TOR-SMC–MRAS framework as a scalable and high-performance solution for next-generation motor control systems under fault-prone​ environments.

The increasing integration of renewable energy resources necessitates efficient energy management frameworks, among which we can refer to microgrids. Microgrids have many advantages, one of which is the reduction of reliability costs. In this paper, as the first contribution, a novel model of microgrid’s capacity value has been developed to be used in a long-term problem such as providing the resource adequacy in the capacity market. Also, as the second contribution, the model of shape factor of the load curve has been developed to consider the value of developing microgrids in countries with different load curves. To demonstrate the effectiveness of the developed model, simulations have been implemented on the IEEE 57-bus test system. Numerical results showed that the optimal limit of the number of microgrids used in the considered test network is 15. By using this number of microgrids in the test network, considering the load curve coefficient of 1, the amount of reliability costs was reduced by 8%. Also, using this number of microgrids in a network with a load curve factor of 2 reduced reliability costs by 15%. These results showed that first, the use of microgrids has a significant effect in reducing reliability costs, and second, networks with uneven load curves benefit to a greater extent from the advantages of microgrids in reducing reliability costs.

Current research on electricity-gas–integrated energy systems (EG-IESs) often overlooks power quality issues prevalent in power systems. Voltage sags, critical and frequent power quality disturbance, significantly affect the EG-IES due to sensitive coupling devices. To minimize economic losses from voltage sags in the EG-IES, this study introduces an optimal configuration methodology for EG-IES coupling devices, considering fault propagation within both electrical and gas subsystems. Initially, the impact of voltage sags on the bidirectional interaction of the EG-IES is analyzed, with a focus on the influence of coupling devices. Subsequently, tolerance characteristic curves for compressors and gas turbines are presented, and a system economic loss model, based on the tolerance curves of coupling devices, is developed. An objective function is then formulated to minimize economic losses, incorporating a coupling device cost model, and solved using an enhanced particle swarm optimization algorithm to determine the optimal configuration of coupling devices. The efficacy and applicability of the proposed method are validated using an EG-IES model comprising the IEEE 14-bus system and an 11-node gas network. The results indicate that the proposed optimal configuration method for EG-IES coupling devices, implemented during the planning phase, effectively reduces losses caused by voltage sags in the EG-IES while accounting for equipment installation costs.

This paper presents a novel control approach for the yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor, which holds significant promise for manufacturing and electric vehicle (EV) applications. Addressing the challenges of achieving high-performance control, we establish mathematical models based on the rotor rotating coordinate system and analyze the motor’s working principle. An active disturbance rejection controller (ADRC), recognized for its simplicity and robustness, is utilized to manage both internal and external disturbances without a strong dependence on precise mathematical models. A vector control strategy is implemented, incorporating linear ADRC (LADRC) for speed control and nonlinear ADRC (NADRC) for current regulation. The proposed ADRC reduces torque ripple to ±1.5 Nm (60% improvement vs. PI control) and achieves 6 ms settling time with zero overshoot during no-load starts. Experimental validation demonstrates superior dynamic performance: under sudden load changes (0–6 Nm), the system maintains stability with minimal fluctuations (±0.57 Nm torque, ±0.42 A q-axis current), while speed transitions (120–200 rpm) show 40% faster response than conventional PI control. The control architecture’s model-agnostic approach enables robust operation without requiring precise motor parameters, making it particularly suitable for EV and industrial applications where both precision and reliability are critical.

The proliferation of microgrids and the rapid electrification of transportation have intensified competition for distribution-level resources, highlighting the need for effective coordination strategies for energy sharing and electric vehicle (EV) charging. This study aims to tackle the complexities of optimizing EV charging within an energy-sharing market, where multiple microgrids engage in electricity trading and compete for flexible demand. We develop a robust game-theoretic framework that incorporates two critical components: a generalized Nash equilibrium game model and a nested Stackelberg game. The Nash equilibrium model captures the interdependent bidding behaviors of microgrids, while the Stackelberg game treats EV users as price-sensitive followers, who adjust their charging strategies based on station-specific tariffs and travel costs. The two models are integrated into a bilevel generalized Nash–Stackelberg formulation that holistically represents the strategic interactions among all stakeholders. To solve this coupled equilibrium, we utilize a fixed-point scheme embedded in a modified best-response algorithm, ensuring convergence to the joint solution of the inner Nash game and the outer Stackelberg game. Numerical experiments demonstrate that the proposed strategy effectively guides EV users toward economically rational charging patterns, balances utilization across charging stations, and enhances overall network efficiency and microgrid profitability compared to conventional decentralized scheduling methods. These results underline the practical value of the framework for integrated management of transportation and power infrastructures.

In this paper, we present an enhanced Q-learning approach with constraint-aware reward shaping for solving the optimal power flow (OPF) problem in smart grids. Unlike conventional reinforcement learning applications, our methodology integrates adaptive exploration strategies and multiobjective optimization specifically designed for power system operational constraints. The smart grid environment incorporates real-time phasor measurement unit (PMU) data, dynamic load variations, and renewable energy fluctuations to provide comprehensive system observability. Our approach achieved significant performance improvements with a 7.5% operational cost reduction compared to the Newton–Raphson method ($45,200 versus $48,900 daily operational cost), 5.2% improvement over the interior point method, and 3.8% enhancement over particle swarm optimization. The reinforcement learning agent demonstrated superior convergence speed of 5 ms compared to 120 ms for traditional methods, reduced constraint violations to 0.3% compared to 0.8% for conventional approaches, and achieved rapid adaptation to sudden load changes within 2–3 versus 10–15 s required by traditional optimization methods. Comprehensive validation on IEEE 30-bus system with scalability analysis extending to IEEE 57 and 118-bus systems confirms the approach’s effectiveness for real-time smart grid control, achieving computational efficiency of 200 solutions per second. The study addresses practical implementation challenges including communication delays, measurement uncertainties, and cybersecurity considerations, providing a robust framework for real-world deployment in modern power systems.