To address the problem of poor voltage stability of wind-solar-energy storage DC microgrids in distributed renewable energy systems, an optimized nonlinear active disturbance rejection control strategy based on the SAC algorithm （SAC-ADRC） is proposed. Firstly, the wind-solar-energy storage system is modeled with nonlinear ADRC control. Then, the gain parameters of the nonlinear ADRC are reconstructed by using linear/nonlinear ADRC switching to improve its internal parameters which are more difficult to tune and analyze. Finally, the analysis establishes a mechanism for SAC intelligence to learn interactively with the microgrid environment, enabling the adjustment of non-linear ADRC parameters. Comparative analysis using algorithm convergence curves and simulation of various classical working conditions confirms the superiority of the SAC-ADRC control strategy in terms of interference performance. Thus, it is shown that the organic integration of nonlinear ADRC and deep reinforcement learning improves the stability of the microgrid bus voltage.

To address the issue of low-carbon operation in multi-microgrid distribution systems under conflicting interests, this paper proposes a hybrid-game based low-carbon strategy for multi-microgrid distribution system considering carbon emission responsibilities. Firstly, a carbon accounting mechanism based on carbon flow theory is employed to allocate source-side carbon emissions to the load side, thereby clarifying the carbon emission responsibilities of all parties. Simultaneously, considering the game relationships among multiple stakeholders, a hybrid game-based low-carbon operation framework for multi-microgrid distribution systems is developed. This framework incorporates both master-slave and cooperative game models to enable effective multi-stakeholder participation. In this framework, the master-slave game takes the distribution network as the leader and the multi-microgrids as followers. The distribution network aims to minimize its operation costs while accounting for carbon emissions by optimizing electricity pricing. This pricing strategy guides the demand response and operational behaviors of the multi-microgrids, leveraging their collaborative carbon reduction potential under carbon flow theory to enhance the low-carbon operational performance of the system. Moreover, using the power interaction data among microgrids obtained from the master-slave game, a cooperative game model based on Nash negotiations theory is established. This model aims to achieve balanced benefit distribution among the microgrids, promoting fairness and enhancing collaborative efficiency within the system. Finally, the proposed hybrid game model is solved using a combined of the dichotomy and the analytical target cascading approach, ensuring efficient and accurate optimization of the system. The case study analysis demonstrates the effectiveness of the hybrid game model proposed in this paper in addressing the conflicts of interest between the distribution grid and multi-microgrids, and in improving the low-carbon operation level.

To realize the intelligent interconnection and interaction between the intelligent integrated energy system （IES） and the power supply system of rail transit, a synergistic optimization strategy for the hydrogen-containing IES and the power supply system of rail transit is proposed based on the dynamic ladder-type carbon trading mechanism. This strategy can reduce the cost of energy supply and utilization, while enhancing the low-carbon energy supply of the multi-flow coupling system and the low-carbon energy consumption of the rail transit system. Firstly, a dynamic ladder-type carbon trading model is designed based on the level of renewable energy and loads in IES. Secondly, according to the electric and thermal output characteristics of gas turbine （GT） and hydrogen fuel cell （HFC）, the traditional combined heat and power （CHP） unit and hydrogen energy unit are coupled by the Kalina cycle to develop a hydrogen-containing flexible energy supply unit, which can enhance the flexibility of the energy supply. Then, a hydrogen-containing IES collaborative optimization model connecting to the power supply system of rail transit is established to achieve cross-system joint optimization of rail transit energy consumption and multiple heterogeneous energy supply units, fully exploring the low-carbon economic operation potential of the system. Simulation results show that the proposed strategy can effectively balance the low-carbon economy and flexibility of multi-energy system scheduling, which can provide references for the synergistic operation of IES and power supply system of rail transit.

To meet the inertia requirements of different voltage levels and dynamic operating conditions, this paper proposes an inertial fusion control technology of multi-voltage-level DC microgrid. Initially, a model of a multi-voltage-level DC microgrid is established, and the inertia characteristics of various levels of DC systems are analyzed. Subsequently, aiming to maximize inertia in each system level, the paper considers the involvement of battery storage and renewable energy side converters connected to the system bus. These are engaged in inertia regulation through increased output current feed-forward control and grouped adaptive control. Meanwhile, the low-voltage side use inertia control based on observation compensation with converters connected to their load side to suppress fluctuations. This approach allows converters with inertia regulation capabilities to participate in the graded fusion of inertia in the DC microgrid and designs the inertia parameters for each end. Finally, a multi-voltage-level DC microgrid hardware-in-the-loop simulation platform is constructed to validate the effectiveness of this control strategy.

This paper proposes a detection method for protection mismatch risk settings tailored for high-penetration renewable energy integration scenarios. Using a typical topology of high-penetration distributed renewable energy systems, the degradation mechanism of protection performance is analyzed in depth. Furthermore, the constraints on correct protection operation are revealed by investigating the root causes of protection misoperation and failure to trip. Based on this analysis, the mapping relationship between DG output and protection mismatch risk settings is quantitatively examined. The boundary of protection failure is further determined using a dual-loop iteration method based on the backbone particle swarm optimization （PSO） algorithm. Finally, a comprehensive identification procedure for protection mismatch risk settings is developed. The effectiveness of the proposed approach is validated through simulations using the PSCAD/EMTDC and Matlab/Simulink platforms.

It is of great significance to realize transient stability evaluation accurately and quickly for stable and safe operation of power system. Aiming at the problem of low prediction accuracy in the current transient stability evaluation of grid-connected photovoltaic energy storage systems, a transient stability evaluation method based on integrated deep Random forest algorithm （gcForest-AdaBoost） is proposed by combining deep learning and ensemble learning techniques. Firstly, the initial input features are constructed according to the steady-state components of the grid-connected photovoltaic energy storage system during operation. Secondly, in order to reduce the overfit of the model and ensure that the model still has strong learning ability after the number of cascades increases, a transient stability evaluation method based on gcForest-AdaBoost is proposed by combining deep random forest algorithm and AdaBoost algorithm. Thirdly, the gcForest-AdaBoost model is trained with input feature set, and the transient stability evaluation model of grid-connected PV energy storage system is established. Finally, the IEEE 39-node system is connected to the photovoltaic energy storage unit to build a case system for simulation analysis and data acquisition, and the evaluation results are obtained. Numerical examples show that the proposed model can effectively analyze the transient stability of grid-connected photovoltaic energy storage systems, and it is found that the proposed model has good robustness and generalization ability.

Concerning the common-mode current problem and the magnetic saturation problem of energy storage inductor in the single-stage current-source inverter, a multi-input current-source inverter with bypass switches and multi-winding high-frequency transformers is adopted. Concerning the energy management and the loop decoupling problem of the multi-energy system, a one-cycle control and master- slave power energy management strategy based on power ratio is proposed, which aims to realize the stable operation of different power supply modes, smooth transformation of different power supply modes, and decoupling operation of the control-loop. The intensive theoretical analysis of the circuit structure, energy management control strategy, and small-signal model of the system is carried out, the control loop of the system is designed, and finally a 2.5 kVA prototype of single-stage current-source inverter with photovoltaic-wind dual-input is developed to validate the adopted circuit topology and energy management control strategy.

Under the background of global climate change, extreme weather events such as extreme high temperature, cold and freezing temperature, and strong wind and heavy rain occur frequently, which seriously threaten the operational reliability of integrated energy system （IES）. Based on this, we firstly focus on the reliability assessment methods of integrated energy system under extreme weather, and analyze the impacts of three types of extreme weather, namely, extreme high temperature, severe cold and freezing temperature, and strong wind and rainstorm, on the energy production equipment （including power generation, heat supply, and energy storage equipment） and the energy transmission network in various aspects. Secondly, we comprehensively review the integrated energy system reliability assessment methods under these extreme weather conditions, and explore the shortcomings of the existing methods in terms of meteorological data processing, system coupling modeling, multi-method fusion, toughness quantification, and multi-factor considerations. Finally, possible future research directions are proposed for the operational reliability assessment of integrated energy systems under extreme meteorological conditions, aiming to provide certain theoretical and practical references for improving the accuracy and effectiveness of IES reliability assessment under extreme meteorological conditions, so as to guarantee the safety and stability of energy supply.

In order to study the high dimensional unsteady flow characteristics in the flow field of supercritical carbon dioxide centrifugal compressor reversed as turbine, the research object is centrifugal compressor reversed as turbine, the CFX flow field analysis software was utilized for numerical simulation, the dynamic mode decomposition（DMD） was performed on the simulated results, and the first four order modes and their corresponding spatio-temporal information are obtained. The analysis results show that the DMD method can decompose the unsteady flow field into modes with distinct energy levels and frequencies, including the basic mode with the highest energy contribution （0 Hz）, the dynamic and static interference mode with the second-highest energy （at the blade passing frequency）, and the high order harmonic characteristics of the dynamic and static interference modes with lower energy contributions（at multiples of the blade passing frequency）; The main characteristics of the basic mode are caused by the geometric parameters of the impeller, the main characteristics of the dynamic and static interference modes are caused by the dynamic and static interference between the impeller and the guide vane, and the high order harmonic characteristics of the dynamic and static interference modes show the subtle flow characteristics in the flow field; Under low-flow conditions, the first order mode exhibits a relative liquid flow angle smaller than the inlet blade angle, leading to flow separation on the pressure side of the blade inlet; Under high-flow conditions, the relative liquid flow angle of the first-order mode exceeds the inlet blade angle, resulting in flow impingement on the pressure side and flow separation on the suction side of the blade inlet. The DMD method can effectively decouple the unsteady flow field within the impeller and extract transient flow characteristics.

In view of the characteristics of multi-regional correlation of full-network power flow in the new-type power system, an optimization method for coordinated electricity-carbon operation and a new full-network multi-regional cooperative optimization method for dynamically matching coordinated electricity-carbon operation are proposed. Firstly, according to the characteristics of cascade multi-source load-storage units in sub-regions of the new-type power system, the differences in operational regulation efficiency of various load-storage units within the region are analyzed, and a gradient cooperative optimization method for load-storage unit indicators is proposed. Then, aiming at the diversified characteristics of electricity-carbon regulation of load-storage units under different operating modes, a fine-tuning optimization method for load-storage unit control parameters based on complete state space and operational mode feedback is studied, and a 'multi-level multi-core' convolutional neural network model is established. Finally, a simulation model is constructed for validation. Simulation results show that the proposed multi-level indicator cooperative electricity-carbon power flow optimization control strategy can effectively enhance the electricity-carbon coordination capability and electricity-carbon regulation level of the new-type power system.