Abstract:With the gradual integration of vehicle-to-grid technology in electric vehicles (EVs), the interaction between transportation and distribution networks has become increasingly critical, intensifying the demand for power grid communication and transforming the power grid into a cyber-physical-transportation system. In response to these challenges, this paper proposes a pricing and control strategy for battery changing stations (BCSs) across multiple markets. Firstly, a Bayesian adaptive spline surface based sensitivity analysis method is employed to quantify the impact of pricing on road congestion rates. In the intraday market, a dynamic pricing strategy, guided by sensitivity analysis, is designed to influence EV traffic flow with minimal price adjustments. This optimizes BCS revenue in energy and reserve capacity markets while alleviating traffic congestion and reducing the communication burden. In the real-time market, a game-based subjective and objective evaluation method is developed to assess the response characteristics of BCSs considering factors such as communication delays, regulation capacity, and market revenue, enabling an equitable allocation of frequency regulation tasks among BCSs. Additionally, this method ensures fair compensation to balance the financial impact of price changes across multiple BCSs. Simulation results validate the effectiveness of the proposed method.

Abstract:Previous studies have demonstrated that disharmony among voltage-source-controlled units may occur on an AC transmission or distribution line under steady-state operating conditions or quasi-static operating conditions. To prevent disharmony oscillations from threatening the secure and stable operation of power grids, two novel disharmony oscillation suppression (DOS) methods are proposed in this paper. First, the oscillation characteristics of single-phase instantaneous current and power under disharmony operating conditions are investigated. The key parameters and typical indicators for monitoring disharmony oscillations, such as disharmony frequency and average power, are listed. Second, two DOS strategies are designed based on directly monitoring the frequency difference and indirectly monitoring line power, respectively. Finally, the characteristics of disharmony oscillations are thoroughly analyzed through a case study, and the proposed DOS strategies are validated in simulations using MATLAB/Simulink.

Abstract:To enhance the deployment capability and low-carbon degree of virtual power plants (VPPs), a novel optimized scheduling model is proposed in this paper for a multi-energy VPP. To explore the distribution potential of the VPP and bolster its multi-energy complementarity, an architecture integrated with electric vehicle (EV) charging stations is introduced, and a battery health degradation mechanism is constructed. To address the uncertainty exhibited by EV behaviors, a feature extraction method based on deep Q-network and maximum relevance-minimum redundancy (mRMR) is then proposed. This method optimizes the applicability of mRMR in large datasets, thereby improving the accuracy of charge behavior prediction. Next, to achieve a complex optimization dispatch, a twin delayed deep deterministic policy gradient algorithm is employed. The twin Q-value truncation mechanism and smooth regularization effectively suppress the issue of policy overestimation biases. Further-more, to validate the performance of the proposed model and algorithm, four different cases are designed, and the scheduling effects achieved for EVs are compared with those of the traditional battery energy storage system framework. The simulation results show that the proposed model significantly reduces both the operational cost and carbon emission level while slowing the battery health degradation process.

Abstract:Low-frequency oscillations (LFOs) in traction power supply systems (TPSSs) frequently arise when multiple electric trains simultaneously raise their pantographs under the same power supply arm. This phenomenon is characterized by low-frequency fluctuations (2–8 Hz) in the envelope waveforms of traction network voltage and current. It can lead to operational issues such as insufficient current acquisition and difficulties in depot entry or exit, thereby adversely affecting railway operations. Given the time-varying nature of LFOs, timely and accurate detection is critical for implementing effective mitigation strategies, with faster detection enabling improved outcomes. This paper proposes a real-time detection algorithm for LFOs in AC traction networks that integrates signal preprocessing, spectral analysis, and parameter optimization. First, the voltage signal is processed using a low-pass filter to suppress high-frequency noise. Then, a fast Fourier transform (FFT)-based spectral estimation method is applied to extract frequency-domain features. Oscillation parameter identification is triggered when the identified signal amplitude exceeds predefined thresholds in the 42–48 Hz and 52–58 Hz bands. Subsequently, during the identification stage, an LFO atom dictionary is constructed based on the FFT pre-analysis results. Finally, the matching pursuit algorithm is employed to achieve fast and accurate extraction of LFO parameters. The proposed method is validated using both simulated and real-world measurement data. Experimental results confirm its effectiveness in detecting LFOs under noisy conditions, demonstrating high accuracy and computational efficiency. The approach provides valuable insights for the threshold selection of protection devices, thereby enhancing the stability and reliability of TPSSs.

Abstract:This study develops a hybrid photovoltaic-thermoelectric generator (PV-TEG) system to reduce dependence on fossil fuels and promote sustainable energy generation. However, the inherent randomness of real-world operational environments introduces challenges such as partial shading conditions and uneven temperature distribution within PV and TEG modules. These factors can significantly degrade system performance and reduce energy conversion efficiency. To tackle these challenges, this paper proposes an advanced optimal power extraction strategy and develops a chaotic RIME (c-RIME) optimizer to achieve dynamic maximum power point tracking (MPPT) across varying operational scenarios. Compared with existing methods, this approach enhances the effectiveness and robustness of MPPT, particularly under complex working conditions. Furthermore, the study incorporates a comprehensive assessment framework that integrates both technical performance and sustainability considerations. A broader range of realistic operational scenarios are analyzed, with case studies utilizing onsite data from Hong Kong and Ningxia for technical and environmental evaluations. Simulation results reveal that the c-RIME-based MPPT technique can effectively enhance system energy output with smaller power fluctuations than existing methods. For instance, under startup testing conditions, the c-RIME optimizer achieves energy output increase by up to 126.67% compared to the arithmetic optimization algorithm.

Abstract:In order to improve the control accuracy of switched reluctance motors (SRMs) without reducing the reliability of the driving system, multilevel power converters can be adopted. Compared to traditional asymmetric half bridge power converters which output three voltages (-U, 0, +U), asymmetric three-level T-type power converters can output five voltages (-U,-U/2, 0, U/2, U/2, U). Meanwhile, asymmetrical three-level T-type power converters can still independently control each phase winding in the SRM. However, research in the field of fault diagnosis for asymmetric three-level T-type power converters is insufficient. To optimize the control of SRM drive systems, this study adopts the asymmetric three-level T-type power converter as the research focus and conducts analysis in conjunction with an appropriate control strategy, thereby advancing both theoretical understanding and practical application in this field. A simulation model and an experimental platform of the SRM drive system based on the asymmetric three-level T-type power converter are developed. Both simulation and experimental results confirm the feasibility of the adopted converter topology and its control strategy, demonstrate the speed and accuracy of the proposed fault diagnosis method, and confirm that the drive system exhibits good dynamic performance.

Abstract:Unmanned aerial vehicle (UAV) path planning plays an important role in power systems. In order to address the challenge in UAV path planning, an improved crested porcupine optimizer (ICPO) combining the Cauchy inverse cumulative distribution function and JAYA algorithm is proposed in this paper. First, the traditional random initialization is replaced by sine chaotic mapping, making the initial population more evenly distributed in the search space and improving the quality of the initial solution. Since the global search ability of the crested porcupine optimizer (CPO) is limited, the Cauchy inverse cumulative distribution strategy is introduced. In addition, as CPO is prone to fall into local optima in later stages, a weighted JAYA-CPO attack strategy is proposed to balance the global exploration and local exploitation, thereby improving the algorithm's ability to escape from local optima. Finally, ICPO is compared with another 10 algorithms on the cec2017 and cec2020 test sets. The experimental results show that ICPO has excellent competitiveness and optimization performance. The ICPO algorithm is applied to the path planning problem of power inspection UAV and is compared with four algorithms. The results show that the algorithm can generate more feasible path trajectories across two terrains with varying complexity, demonstrating the effectiveness and significance of the ICPO algorithm for UAV power inspection path planning.

Abstract:Under thermal abuse conditions, lithium-ion batteries are subject to multiple sources of uncertainty, which can potentially trigger thermal runaway. To enable reliable structural design under thermal safety constraints, this study proposes a reliability-based design optimization (RBDO) method for lithium-ion batteries based on critical venting prediction. First, an analytical model is developed that couples electrochemical reactions, heat conduction, gas dynamics, and nonlinear elasticity, enabling a comprehensive characterization of the thermogas-mechanical evolution, with the critical venting time adopted as the performance metric. Second, global sensitivity analysis using a variance-based decomposition method identifies high-sensitivity parameters for dimensionality reduction. Finally, an RBDO model with venting response probability as the constraint is formulated, and an efficient solution strategy is established by integrating the performance measure approach with a decoupled optimization frame-work to ensure computational efficiency and numerical stability. Experimental results show that the proposed model achieves prediction errors lower than 3% for temperature and critical response. Compared to existing methods, it achieves a superior balance between accuracy and efficiency, with RBDO solution time under two hours. The proposed approach demonstrates strong engineering applicability and extensibility, offering an effective tool for safety-oriented structural optimization of lithium-ion batteries.

Abstract:With the increasing penetration of renewables, low voltage (LV) distribution networks face rising demands to accommodate an increasing range of renewable energies. Low voltage direct current (LVDC) distribution networks are considered as a viable approach to alleviate the strain on existing distribution networks. Nonetheless, establishing reliable and selective protection solutions is recognized as crucial challenges to facilitate the wide adoption of LVDC. Several existing protection algorithms focus on significant fault currents, fixed threshold settings, and high sampling frequency to establish effective coordination. However, these approaches are either constrained in low fault current scenarios or require expensive data acquisition techniques. To address such issues, this paper develops a novel self-adjusting based protection scheme. The method only utilizes the inherent post-transient voltage derivative (PTVD) sign and value with a self-adjusting mechanism to distinguish faults. The proposed method overcomes the selectivity limitations of existing voltage-based solutions when a lower sampling frequency is employed. The effectiveness is validated on an LVDC test network constructed in PSCAD/EMTDC and an RT-Box-based hardware test bed. Results under injected noise signals further demonstrate the robustness of the proposed method.

Abstract:This paper proposes a dual alternative iteration algorithm-based hierarchical MPC (DAMPC) strategy to realize frequency regulation control and active power allocation of wind-storage coupling system. The proposed DAMPC strategy involves a top-level grid frequency model predictive control (FMPC) strategy and a bottom-level multi-objective model predictive control (MMPC) strategy. In the FMPC strategy, to improve the frequency regulation performance, the active power reference of the wind-storage coupling system is generated by minimizing the frequency deviation, where the frequency reference is calculated by considering the active power deviation and its integral. In the MMPC strategy, the active power reference is optimally allocated to the wind turbine generators (WTGs) and battery energy storage system (BESS) by raising the minimum rotor speed, minimizing the pitch angle deviation and state of charge (SOC) deviation. To solve the multi-objective allocation optimization problem with high efficiency, a dual alternative iteration algorithm (DAIA) is proposed to update the global and local control vectors with the dual vector. Extensive simulations validate the effectiveness of the proposed DAMPC strategy in frequency regulation and active power allocation.

Abstract:A time-varying optimization strategy for battery cluster power allocation is proposed to minimize energy loss in battery energy storage systems (BESS). First, the time-dependent loss characteristics of both storage and non-storage components in BESS are analyzed. Based on this analysis, steady-state and transient methods for evaluating battery loss are proposed. Second, considering the distinct time-varying characteristics of various BESS components, the load-rate vs. equivalent-efficiency curve and the current-loss power component gradient field are introduced as analytical tools. These tools facilitate the derivation of optimization path for both time-varying and time-invariant energy components of BESS. Building on this foundation, a time-varying optimization strategy for battery cluster power allocation is developed, aiming to minimize energy loss while fully accounting for the dynamic characteristics of BESS. Compared to real-time optimization, this strategy prioritizes global optimality in the time domain, mitigates the risk of dimensionality curse, and enhances BESS efficiency. Finally, a Simulink/Simscape model is established based on real-world data to simulate internal component losses within BESS. The effectiveness of the proposed strategy is validated under a peak shaving scenario. Results indicate that, after optimization, the annual operational loss of BESS is reduced by 2.40%, while the energy round-trip efficiency is improved by 0.59%.