Wind turbines can provide reserves through overspeed control to enhance the operational economy of the power system. However, existing research has not adequately considered the dangerous operating region of wind turbines during overspeed control. To address this issue, this study starts from the principle of induction generator effect, derives and defines the prohibited rotor speed zone, analyzes its impact on wind power overspeed reserves, and establishes a wind power reserve model that incorporates the prohibited rotor speed zone. Building upon this, a coordinated reserve optimization model for wind and thermal power generation, considering the prohibited rotor speed zone, is developed, and the multi-cut Benders decomposition method is employed for solution. The case study validates that wind power reserves can improve system frequency stability and operational economy, emphasizing the necessity of considering the prohibited rotor speed zone.

U-I droop schemes possess excellent adaptability and scalability, which is suitable for voltage control and load sharing of autonomous DC microgrids. However, it has not been given how to make a trade-off between the steady-state errors of the common bus voltage control and the accuracy of load sharing. The paper presents an adaptive control scheme for autonomous DC microgrids. The proposed scheme is accomplished by voltage reference regulation combined with current reference regulation. The small signal model of the microgrid under the proposed scheme is formed for the stability analysis. Simulation results show that the load is shared among DG units at the desired ratio and the voltage of common bus is restored accurately by the proposed scheme during load fluctuation, DG unit fault and change of system parameter.

The capacitor voltage full feedback scheme employed in LCL-type grid-connected inverters can suppress grid background harmonics. However, the existing schemes exhibit insufficient suppression capability for high-order harmonics. To address this problem, this paper proposes an improved harmonic suppression strategy. By introducing a compensation link into both the unit feedback path and the second-derivative feedback path, the phase deviation caused by neglecting the control delay is compensated. The improved capacitor voltage full feedback method more closely approximates the ideal effect, particularly in the high-frequency band. Furthermore, by introducing a weighting coefficient into the compensation link of the second -derivative feedback path, the equivalent resistance induced by the capacitor voltage full feedback is reshaped, thereby ensuring stable system operation. Experimental results demonstrate that the grid -connected inverter utilizing the proposed improved harmonic suppression method can effectively suppress the grid current distortion caused by background harmonics in the grid voltage, showing particularly effective suppression for harmonics in the medium and high-frequency bands.

Aiming at the problem of output voltage oscillation and deterioration of power quality caused by parameter changes of Boost cascade converter, an active disturbance rejection suppression strategy that improved coati algorithm is proposed. First, a simplified discrete model of the cascade converter was established to prove the existence of period-doubling bifurcation in system and combined with simulation verification, it was concluded that the low -frequency oscillation of the port output voltage was due to the period-doubling bifurcation disturbance in the system; Secondly, using linear active disturbance rejection controller compensates for disturbance estimation. Since manual tuning of parameters is inefficient and difficult to ensure accuracy, improved coati algorithm is introduced to realize adaptive tuning of controller parameters. In terms of algorithm, SPM chaos mapping is used for initialization to increase population diversity; Incorporating Levy flight and refractive backward learning strategies to avoid the algorithm falling into local optimum and to improve the efficiency and quality of parameter finding. Finally, through simulation verification and analysis, improved coati algorithm parameter configuration is more accurate and efficient, and the tuned controller can effectively suppress period -doubling bifurcation disturbances and improve the quality of transmitted power.

Against the backdrop of global energy structure transformation and rapid development of renewable energy, how to achieve efficient aggregation and optimized scheduling of heterogeneous virtual power plants (VPPs) has become an important challenge. A heterogeneous VPPs frequency and power coordination scheduling model based on target cascade analysis method is proposed by improving the deep integration of clustering algorithm and control system. Introducing a learning mechanism to optimize the clustering process of photovoltaic battery energy storage systems (PVpBESSs) to meet the energy capacity requirements of a large number of power sources. A distributed control system for PVpBESSs was designed and validated on IEEE 13 node distributed photovoltaic power and energy storage systems. The experimental results show that compared with traditional scheduling methods, the scheduling efficiency has been improved by 18%. The model stabilizes the voltage deviation within 1% and maintains the frequency deviation at around 50 Hz; At the same time, the distribution of battery charging status is evenly maintained at 30%~90%, and efficient collaborative scheduling of photovoltaic and energy storage resources is achieved. Compared to methods that do not use learning mechanisms, the proposed model has strong practicality and potential for expansion, providing a new path for the practical application of virtual power plants.

Deepening the response potential of flexible resources can enhance the operational economy, flexibility and low-carbon nature of the park integrated energy system (PIES). For this reason, this paper proposes an optimal scheduling strategy for PIES that considers the flexible response of supply and demand. First, on the basis of analyzing the dynamic energy efficiency characteristics of the equipment, the organic flash cycle and the electric boiler are introduced to establish a supply -side flexible response model for the multimodal and variable operating conditions of the combined heat and power unit. Then, an electricity-cooling-heating multivariate demand response model with differential rationing of curtailment, substitution and transfer of flexible loads is established on the load side based on a multilevel stepped incentive compensation mechanism. Finally, the coordination optimization model for flexible response of supply and demand of PIES is constructed with the optimization objective of minimizing the daily operation cost. Simulation shows that the proposed strategy not only improves the accuracy of the dispatch model, but also achieves a multi-dimensional optimal balance of system economy and low carbon with energy supply reliability and satisfaction while promoting wind power consumption.

As a highly efficient thermochemical conversion method, hydrothermal treatment technology demonstrates broad application potential for the harmless disposal and value-added utilization of food waste and other organic biomass residues. In this paper, subcritical hydrothermal treatment was systematically investigated, and a polit-scale hydrothermal treatment system for food waste was developed with a processing capacity of 10 t/d and a total capital investment of 3.279 4 million yuan. The bio-oil yield obtained from food waste hydrothermal treatment system reached 9%, with a higher heating value of 38.63 MJ/kg, and low-boiling-point fractions (<343 ℃) accounted for 82.62% of the bio-oil, indicating its strong potential as an alternative fuel. The aqueous-phase yield was 85%, and the aqueous products could be formulated into humic acid-based liquid organic fertilizer. Economic evaluation showed that a net profit of 1 302.93 yuan could be achieved per ton of food waste treated, with an investment payback period of 0.84 years. The developed industrial hydrothermal treatment system successfully overcame several core technologies, including the efficient separation of inorganic components from organic solid waste and the cascade recovery and utilization of residual heat. Moreover, the system provides an integrated solution to key technological bottlenecks that have hindered the industrialization of food waste hydrothermal treatment. Furthermore, it systematically resolved the technical bottlenecks facing the industrialization of food waste hydrothermal treatment technology.

In order to investigate the product distribution and synergistic effect during co-liquefaction of plastic and biomass. Six types of lignocellulosic biomass and polypropylene (PP) were selected for co-liquefaction. Based on the characterization of raw materials, the liquefaction behavior and product distribution of different biomass with PP were examined. The results indicate that the biomass with higher volatile matter content and lower lignin content generally exhibits a higher conversion rate during solvent thermal liquefaction. Products distribution from co-liquefaction of biomass and PP varied with biomass types. Higher lignin content in biomass was associated with increased co-liquefied oil yield, suggesting a synergistic effect during the co-liquefaction process. Furthermore, the ash content in biomass influenced the extent of synergy in oil yield-higher ash content enhanced the synergistic improvement in oil production. Among the tested biomass samples, poplar showed the strongest synergy with PP in co-liquefaction oil yield. Individual liquefaction of PP and poplar gave conversion rates of 17% and 36%, respectively, whereas co-liquefaction reached a conversion rate of 51% with an oil yield of 45%, indicating that poplar promotes the hydrothermal degradation of PP. The co-solvent thermal liquefaction of biomass and PP demonstrates advantages in enhancing the formation of target oil products and improving oil quality.

This study establishes a model of a solid oxide fuel cell (SOFC) power generation system fueled by hydrogen-enriched natural gas using Cycle-Tempo software. It provides a detailed analysis of the impacts of key operating conditions-such as single-cell voltage and operating temperature-and critical parameters-including hydrogen blending ratio and anode fuel utilization-on the performance of the SOFC system. The results indicate that increasing the hydrogen blending ratio, inlet temperature, single-cell voltage, and anode fuel utilization can enhance power generation. Notably, a higher hydrogen blending ratio significantly reduces greenhouse gas emissions, particularly CO₂. Under the conditions of a gas flow rate of 0.043 7 kg/s, single-cell voltage of 0.75 V, fuel utilization rate of 80%, fuel cell inlet temperature of 700 ℃, and hydrogen blending ratio of 60%, the system achieves a power output of 1 220.33 kW with an efficiency of 52.67%, while reducing carbon emissions by 27.86%.

To address the limitations of traditional photovoltaic (PV) array modeling methods in terms of computational accuracy and portability, this study proposes a novel PV array modeling approach based on the Implicit Double Diode Model (IDDM) and the Reverse Bias Model (RBM). The proposed method first configures environmental parameters according to the actual operating conditions of the PV array. Subsequently, it calculates the current and voltage characteristics of PV cells under various operating states based on the theoretical frameworks of IDDM and RBM. Following this, the method determines the current and voltage characteristics of PV array substrings and finally outputs the current-voltage (I-V) characteristics of the PV array. Experimental results demonstrate that the proposed method achieves high simulation accuracy, with a theoretical-to-actual current sequence error of only 0.015 A. Additionally, the model effectively simulates the I-V characteristics of PV arrays under scenarios such as partial shading, short circuits, and degradation of equivalent series and parallel resistances. This modeling approach provides a robust theoretical foundation and data support for PV power plant fault diagnosis and analysis.