Aimed at the problem of high backflow power in dual active bridge (DAB) converters under single phase shift modulation, a global backflow power optimization scheme based on dual phase shift modulation and super twisting sliding mode control is proposed. First, the characteristics of a DAB converter in four working modes under dual phase shift modulation are analyzed, a mathematical model is established, and the calculation formulas for the converter’s transmission power and backflow power are derived. Then, an optimization analysis is performed on the backflow power in each of the four working modes, so as to obtain the optimal phase shift ratio combination that produces the minimum backflow power in the global mode. Third, a super twisting sliding mode control algorithm is introduced based on a reduced order model of the DAB converter to further improve the static and dynamic performance of the system. Finally, an experimental comparison was conducted on an OP4512 semi-physical simulation platform based on OPAL-RT, and experimental results verified the effectiveness of the proposed optimization scheme.

Under the scenario of a power system with high proportion of renewable energy integration and high power electronic penetration (i.e., a “double-high” power system), grid-following inverters rely on phase-locked loop (PLL) to accurately extract the phase of grid voltage. However, problems such as tracking lag and large phase deviation may occur under complex operating conditions. Meanwhile, the traditional grid-connected control strategy based on proportional-integral (PI) regulators involves tedious parameter tuning, which increases the design difficulty of the controller and makes it difficult to adapt to variations in different grid strengths. To address these issues, a full model predictive control (MPC) strategy for grid-following inverters is proposed by leveraging the nonlinear objective constraint handling capability and fast dynamic response characteristics of MPC. First, an MPC-PLL structure under a discrete control set is established to accurately extract the grid voltage phase. Second, continuous control set-model predictive control (CCS-MPC) is adopted to construct an MPC current loop, which can directly output the optimal switching states through control calculations. Finally, an MPC voltage loop with the control objective of DC-side voltage stabilization is built to provide reliable DC-side support and generate the optimal current reference commands. Simulation and experimental results demonstrate that the proposed full MPC strategy outperforms the traditional PI-based strategy in both the transient and steady-state performance under different operating conditions of weak and strong grids. These results provide a novel reference for the development of control technologies for grid-following inverters and offer theoretical support for the stable operation of large-scale renewable energy grid-connected inverters.

The recovery overvoltage after the fault ride through of direct-driven wind power permanent magnetic synchronous generators (D-PMSGs) caused by control delay and delayed fault clearing identification may lead to cascading disconnection of generators, which has become a key factor limiting the renewable energy accommodation. Aimed at this problem, the generation mechanism of post-fault terminal overvoltage was quantitatively analyzed, and an analytical expression for the relationship among the terminal voltage, grid parameters and output active/reactive current was derived. A reactive current instruction analytical generation and compensation control method during the fault ride recovery process was proposed, and the logic of strategy start/exit was given. The strategy adaptively adjusts the reactive current reference value of generators according to the grid parameters and measured active current, so as to eliminate the impact of part of the control delay links. The simulation results of a case study show that the proposed control strategy can effectively suppress the overvoltage and mitigate the potential risk of system destabilization after fault clearing, thus improving the safety and stability of the renewable energy grid-connected system.

Aimed at the problems of poor steady-state performance, high switching loss and susceptibility to changes in motor parameters of duty cycle model predictive current control, the low switching frequency model predictive current control strategy for a doubly-fed induction generator (DFIG) with an inductance identification function is studied. First, according to the principle of deadbeat control of rotor voltage, six expected voltage vectors are synthesized by distributing the action time of two adjacent basic voltage vectors and zero vectors. Second, a value function is constructed by taking the minimum rotor voltage prediction error and the minimum switching times at the switching points of two adjacent control cycles as the constraint conditions, and six vector combinations are enumerated and optimized. Finally, the d axis and q axis stator and rotor flux state equations are established. The model reference adaptive system (MRAS) algorithm is used to identify the inductance parameters in the motor system online, and the real-time identification results are fed back to the system. Simulation and experimental results show that the proposed strategy has good control performance and parameter robustness while effectively reducing the switching frequency.

At present, the wind and photovoltaic (PV) power generation units still face the problem of a lack of flexibility due to the dual roles of intermittency of resources such as solar and wind energy and real-time fluctuations in power grid. Therefore, the studies of the flexibility assessment of wind and PV power generation units and the corresponding enhancement strategy are critical. Aimed at both the quantification of flexibility assessment and the flexibility control strategies, a flexibility assessment system is explored at first, i.e., the flexibility assessment indexes are selected from the aspects of power output capability, stability, voltage support capability, etc., and the flexibility of wind and PV power generation units under different control strategies is quantified based on the CRITIC weight method. Second, based on the flexibility assessment results, a control strategy with an advantage in flexibility is selected for wind and PV power generation units, and non-perturbative switching is carried out, so as to realize the flexibility control of wind and PV power generation units. Finally, the feasibility of the proposed approach was verified by simulations conducted on a model of a high-power PV grid-connected converter applied in practice.

As the environmental problems escalate, fuel cells have emerged as a pivotal technology in the development of new energy for reducing carbon emissions and optimizing energy structures. Under the application scenarios demanding high power, multi-stack proton exchange membrane fuel cell systems (multi-stack fuel cell systems) demonstrate significant potential. In this context, the research status and progress of multi-stack fuel cell systems are reviewed in detail, focusing on their electrical, fluid and thermal, power management strategies, lifetime, efficiency and hydrogen consumption. In addition, the differences in key indicators such as lifetime, efficiency and hydrogen consumption between multi- and single-stack fuel cell systems are also discussed, with an aim of highlighting the substantial advantages of multi-stack fuel cell systems.

To characterize the working characteristics of batteries more accurately, a second-order fractional-order model (FOM) is established based on the fractional-order calculus theory, with a Samsung power lithium-ion battery as the research object. Aimed at the nonlinear and multi-extremum characteristics of the second-order FOM parameters, an adaptive genetic algorithm is adopted for parameter identification. Based on the traditional unscented Kalman filter (UKF) algorithm, a fractional-order singular value decomposition-multi-information adaptive UKF (FOSVD-MIAUKF) algorithm is proposed. Simulation results show that the improved FOSVD-MIAUKF algorithm combined with second-order FOM can better estimate the state-of-charge (SOC) of lithium-ion battery. The root mean square errors under both the dynamic stress test (DST) and federal urban driving schedule (FUDS) conditions are less than 0.5%, which verifies that the improved method has better adaptability and higher accuracy.

The state-of-charge (SOC) of a lithium-ion battery is an important parameter that reflects the state of its remaining energy. To address the issues of the initial SOC being indeterminate and the cumulative error increasing over time in the ampere-hour integral method, an algorithm is proposed to correct the error in the ampere-hour integral method. Through the study of the charge and discharge characteristics of a lithium-ion iron phosphate battery, the open circuit voltage-SOC (OCV-SOC) curve and the DC internal resistance-terminal voltage curve are obtained. The proposed algorithm calibrates the SOC when the battery reaches the full charge condition during charging, and it performs online error calibration based on the battery’s characteristic curves when the values of capacity are 7% in the discharge state respectively. The experimental results show that it can effectively reduce the error in the SOC estimation process and control it within 2.00%, indicating a high estimation accuracy.

The state-of-health (SOH) estimation of batteries is crucial for ensuring the safe and stable operation of energy storage systems. However, the methods which use fixed voltage segments as model inputs face limitations in practical applications due to varying user habits, and most of the studies based on incremental capacity (IC) analysis still face challenges in selecting the smoothing parameters. A sliding window smoothing method is proposed to obtain smooth IC curves while simplifying the parameter selection process in real-world applications. This method uses two complementary Wasserstein distance calculation methods to evaluate the differences between the target IC curves and reference curves, which serve as health feature (HF) and are combined with the corresponding starting voltages of voltage segments as model inputs. These inputs are further used to train a Gaussian process regression model. Test results indicate that the voltage segments at any position, even those that do not include the IC curve peak, can be used as model inputs to achieve SOH estimation results with a root mean square error (RMSE) below 2%. In addition, the effects of different smoothing parameters, sampling frequencies and estimation models on the estimation results are also analyzed. The analysis results indicate that the extracted HFs exhibit strong robustness, and the model achieves high estimation accuracy.

Temperature is the main factor affecting the safety and operating efficiency of lithium-ion battery energy storage systems, and an excellent thermal management design is helpful in improving the operating temperature environment of batteries, which is the basic guarantee for the safety and high efficiency of energy storage systems. To improve the operating temperature environment of batteries, an open-type air-cooled heat dissipation battery module used in lithium-ion battery energy storage systems was designed, and a numerical simulation method was used to study the effects of fanflow rate, cross-sectional shape of heat dissipation harmonica tubes and the contact heat transfer coefficient between the battery and harmonica tubes on the heat dissipation performance of the air-cooled battery module. Results showed that as the fan flow rate increased from 0.400 m³/min to 2.400 m³/min, both the maximum and minimum temperatures of the air-cooled heat dissipation battery module decreased considerably until an equilibrium was reached. The maximum temperature difference of the battery module decreased from 9.3 ℃ to 6.6 ℃, with a decrease of 2.7 ℃. The air flow distribution rationality of harmonica tubes was improved by changing the harmonica tube cross-sectioal shape from uniform distribution state to partitioned layout, which reduced the maximum temperature difference of the battery module from 6.8 ℃ to 4.5 ℃ at a fan flow rate of 2.000 m³/min, with a substantial decrease of 33.8%. In addition, the overall temperature rise of the air-cooled heat dissipation battery module can be effectively reduced by increasing the contact heat transfer coefficient between the battery and harmonica tubeswithin a certain range.