Jiazhu Xu

The hybrid photovoltaic-thermoelectric generator (PV-TEG) system integrates two distinct energy conversion methods, representing a significant innovation in the progress of renewable-thermal energy technologies. This paper presents an innovative approach for addressing two critical challenges in PV-TEG with battery energy storage system (BESS) within isolated microgrids: global maximum power extraction and DC-link voltage regulation.The system leverages waste heat from the PV module to generate additional power through the thermoelectric generator, improving overall efficiency. However, nonlinear power characteristics, including partial shading (PS) and dynamic temperature scenarios (DTS), complicate efficient power extraction. To overcome this, A single maximum power point tracking (MPPT) strategy, utilizing the Spider Wasp Optimizer (SWO) is proposed to enhance global maximum power extraction under these dynamic PS conditions. In addition, the stability of the DC-Link under variable loads is maintained using a bidirectional DC–DC converter regulated by Linear Active Disturbance Rejection Control (LADRC), addressing voltage fluctuations in the DC microgrid. Simulation and experimental results demonstrate that the proposed SWO method achieves a lowest response time of 0.02 s and a highest tracking efficiency of 99.94%. Furthermore, under the most extreme dynamic conditions, the proposed LADRC-based BESS control has shown strong robustness and resilience in term of DC microgrid stability. This study contributes to the development of resilient and dependable power systems by tackling the complex challenges caused by dynamic environmental conditions in DC standalone microgrids, thereby supporting the progress of sustainable energy solutions.

Electric vehicles (EVs) and wheelchairs rely heavily on lithium-ion batteries (LIBs) for safe, reliable, and uninterrupted mobility. Accurate Remaining Useful Life (RUL) prediction of EV and wheelchair batteries is paramount for averting unexpected failures, extending service longevity, and devising proactive maintenance protocols. Although bidirectional gated recurrent unit (BiGRU) networks are proficient at modeling temporal dependencies, their exclusive dependence on sequential input constrains prediction precision. To circumvent this issue, this study introduces an encoder–decoder architecture that synergistically integrates dual attention mechanisms with BiGRU. Within the encoder, an attention-augmented BiGRU module selectively emphasizes critical aging features. An additional attention mechanism dynamically refines the input sequence to ensure no essential information is attenuated during encoding. The decoder, structured as a BiGRU network, subsequently delineates precise capacity degradation trajectories. Comprehensive evaluations conducted on the NASA and CALCE datasets substantiate the model efficacy. On the NASA dataset, the proposed framework yields absolute errors of 1, 4, 0, and 2 for cells B0005, B0006, B0007, and B0018, respectively. Comparably minimal error margins are observed on the CALCE dataset. Further comparative analyses reveal that the proposed approach surpasses existing benchmarks in predictive accuracy, robustness, and generalizability. This high-fidelity RUL prediction model holds substantial potential for advancing battery management systems in EVs and wheelchairs, reinforcing operational reliability, and elevating safety and end-user experience.

Load frequency control (LFC) in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system. Although various PI/PID and other advanced control strategies have been employed for LFC in power systems, the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems. Moreover, the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions. To overcome the limitations of the existing methods and optimization functions, a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization (MZOA-PSO) is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems. The MZOA enhances the original ZOA by chaotic initialization, adaptive parameter control, and Lévy-flight foraging to improve the global search ability, while PSO ensures efficient local search ability. The optimizer is first validated using four benchmark functions, achieving the global optimum for the Booth and Zakharov functions, a mean value of 2.13 × 10⁻²⁸ with a 98% success rate for Rosenbrock, and 3.21 × 10⁻⁸¹ for Schwefel 2.22. Under a 1% step load perturbation, the proposed controller achieves a 13 s settling time, zero negative deviation in Area 2, a maximum positive excursion of 0.10 Hz, and tie-line undershoot limited to −0.10 p.u. Under random load variations, deviations remain within ±0.03 Hz and ±0.02 p.u. Under RES and EV integration, the peak frequency deviation is reduced to 0.46 Hz in Area 1. These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping, faster stabilization, and stronger robustness for modern interconnected LFC systems.

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead.

This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA’s reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system’s response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

Many advanced techniques efficiently harvest the global maximum power (GMP) of the photovoltaic (PV) system, including machine learning techniques and metaheuristic algorithms based maximum power point tracking (MPPT). Nevertheless, they have shortcomings such as sluggish convergence and local maxima power (LMP) trapping. Combining techniques improves productivity. This work proposes an intelligent nonlinear proportional-integral-derivative (NPID) controller coupled with hybrid salp particle swarm optimization algorithm (SPSOA) to successfully harvest the GMP of PV system. The SPSOA-NPID controller’s performance is measured in regard to settle time, rising time, overshooting, peak time, undershoot, rotor rotation speed, and GMP under varied realistic irradiation and temperature profiles. In this work, the optimum parameter settings for the proposed NPID and the basic PID controllers were obtained utilizing the hybrid genetic algorithm and PSO (GA-PSO) techniques. Simulation findings proved the success and robustness of the SPSOA-NPID-based MPPT controller followed by GA-PSO-PID, GA-PID, GA-NPID, PSO-PID, PSO-NPID, P&O and INC respectively. The proposed SPSOA-NPID method obtained an average efficiency of (0.9946), the lowest average ripples (8.214 W), and the fastest average tracking time (0.052 s). Lastly, a hardware-in-loop (HIL) experimental carried out to confirm that the proposed SPSOA-NPID control can be practically implemented. Consequently, it is determined that the proposed SPSOA-NPID based GA-PSO control system is a potential MPPT approach based on the thorough research that have been given.

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

This paper studies the computational problems of the optimal renewable energy power’s investment and production strategies for a class of noncooperative game models of power sales with risk avoidance considering renewable energy power production quotas and Green Power Certificate System. To accommodate the problems, the following steps are implemented: firstly, according to the objects of sales of the power produced by the energy suppliers and whether the players have characteristic of the risk avoidance, the models are subdivided into four kinds of models. Secondly, the employment of the gradient-like optimization method and the setting of auxiliary systems verify the existence and uniqueness of the Nash equilibrium for each of models (ie., the optimal renewable energy utilization rates) that is globally exponentially stable. Then, the definition of the complete information static game is employed to compute the Nash equilibriums and then the optimal power production quantities and the optimal revenue functions would be calculated by relevant equations. Finally, the convergent validations for renewable energy utilization rates, the numerical validations for the optimal calculations for the four proposed models are carried out through a specific numerical example. Moreover, the impacts of players’ characteristic of the risk avoidance are investigated and remarks on the impacts are provided.

Insufficient damping and inertia are the one of the most tough challenges in the grid-connected power system with high penetration of renewable energy resources. Damping correction loop of virtual synchronous generator (VSG) connected to weak damping and high R/X ratio condition is introduced to solve the issues. First, the scheme of the proposed control is implemented in various system operating conditions, and the small-signal model of the proposed control is established. Subsequently, the influence of the damping correction loop on small-signal stability is demonstrated by Bode diagram analysis, and the interaction between reshaped VSG control and power compensation capacity is clarified in three aspects of damping, inertia and synchronization. Furthermore, the optimal design of additional damping and inertia support is provided to suppress the subsynchronous frequency fluctuation of the system, the proposed control method can be easily implemented by directly inserting it into active power control loop of VSG. Finally, the efficiency of the damping correction loop is verified by experiments, the results indicate that 70% reduction of maximum frequency deviation with the proposed control under 20% active power reference step.