International Transactions on Electrical Energy Systems最新文献

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.

Power quality disturbances (PQDs), including voltage sags, swells, harmonics, transients, flickers, and interruptions, affect the reliability and efficiency of modern power distribution systems. This study introduces a novel heuristic model that integrates a long short-term memory (LSTM) deep neural network with the sea lion optimization (SLO) algorithm for precise classification and elimination of PQDs. The system comprises a voltage-controlled distribution static compensator (VCM-DSTATCOM), including an LSTM-SLO optimized PI controller to enhance reactive power compensation and voltage regulation performance. The proposed LSTM-SLO classifier is executed using the SLO algorithm, which enhances hyperparameter optimization, increases accuracy, and reduces computing time. All simulations and coding were conducted in MATLAB/Simulink 2019b on a 400 V, 50 Hz distribution network and PyCharm 2022. The classifier achieved a test accuracy of 99.10% with a convergence rate of 0.97%. The proposed VCM-DSTATCOM, utilizing optimal gains of PI controllers, effectively eliminated PQDs and reduced total harmonic distortion (THD) to 0.52%, compared to 15.17% with a conventional PI controller; furthermore, voltage stabilization was achieved with instrument response times under 20 ms. This study offers a practical method for addressing real-time PQ problems, with potential applications in smart grid power distribution. Future endeavors can focus on a customized hardware solution that can be integrated with the IoT environment for enhanced monitoring and control tasks.

This research investigates the application of the parrot optimizer (PO) algorithm and the artemisinin optimization (AO) algorithm to determine the optimal placement of solar-powered distributed generators (SP-DGs) within the standard IEEE 33-node, 69-node, and the Vietnamese Hoang Dieu 26-node distribution power networks (DPNs). Both algorithms were implemented to minimize active power loss under peak load conditions for a one-hour period. The optimal placement problem was analyzed for two scenarios of renewable-based distributed generation (RBDG) operation: Case 1 (pure active power generation) and Case 2 (active and reactive power generation). The performance of the two algorithms was compared against previous studies to identify a more effective approach. Furthermore, the AO algorithm was applied to minimize the total one-year energy loss (OYEL) in the Hoang Dieu 26-node DPN, utilizing real solar radiation data for Hoang Dieu Street in Ho Chi Minh City, Vietnam, obtained from a solar global atlas. The findings indicate that AO demonstrates superior effectiveness compared to PO and certain existing algorithms for the two standard DPNs. For the Hoang Dieu 26-node DPN, AO achieved significant reductions in OYEL, suggesting its suitability for optimally placing and operating RBDGs to yield substantial benefits for this specific network.

This paper presents a three-step identification-based dynamic equivalencing approach for multimachine electric power systems. Viewed from the bus of a local machine, a multimachine power system is modeled as a lossy two-machine equivalent (LTME) system. The LTME system comprises the local machine, an equivalent transmission line, and an equivalent synchronous machine connected in series. The LTME system parameters are estimated using the simulated time responses of the multimachine system following a disturbance. The required parameters are categorized into two types: electrical parameters (EPs) and mechanical parameters (MPs). The first two steps of the proposed method are formulated as linear recursive least-squares (RLS) estimation problems. An RLS algorithm with a memorizing factor is then employed in these steps to estimate the LTME system parameters. In the third step, a critical clearing time (CCT)–based method is used to determine appropriate values for the memorizing factors applied in the first two steps. Transient stability studies of power systems can be conducted using the resulting LTME system. The proposed approach is validated on the New England 10-generator, 39-bus test system using the classical machine model, and the simulation results are discussed in detail.