Parallel gas-electric hybrid systems have broad application prospects in low-carbon ships due to their few emissions and dynamic performance. However, uncertain delays in multiple actuators during mode transition can cause violent fluctuations in the shaft speed along the power drive. In this paper, a cascaded internal mode control (IMC) consisting of filters with explicit nominal delay is proposed to improve speed tracking performance and eliminate the effect of delay. A dynamic model of the marine driveline is developed, and the cascade IMC is designed based on the driveline mechanism with the clutch serving as the separating component. The cascade IMC consists of an anti-saturation compensator, a two-stage tracking controller, and a two-stage anti-interference controller. Finally, the small-gain theorem is derived to ensure robust stability conditions, taking the upper bound of the uncertain delays into consideration. The results of simulation and dynamometer test show that the cascaded IMC has excellent robustness in handling uncertain delays, significantly reduces shaft jerk, and ensures smooth mode transition.

High power density is critical to high-performance electrical machines in aviation, and the rotor structures of the machines play an essential role. This paper focuses on the permanent magnet (PM)-shifting rotor to enhance the power density and PM utilization ratio of PM synchronous machines. Aimed at the rated operation point with a power of 150 kW and a speed of 12 000 r/min, global optimizations using the non-dominated sorting genetic algorithm II (NSGA-II) are applied individually to the surface-mounted, surface-inserted, and surface-inserted-shifted PM machines. Then, the three optimal machines are comprehensively evaluated and compared. Additionly, a comparative investigation is conducted on the surface-inserted-shifted PM machines with different structures and different PM shifting angles. The results reveal that surface-inserted-shifted PM machines achieve a 2.1% increase in power density, a 17.6% reduction in PM usage, and an 11.6% decrease in torque ripple compared to conventional surface-mounted PM machines, indicating strong potential for aircraft applications.

Due to the much faster response time of permanent magnet synchronous motor (PMSM) compared to mechanical dynamics, a tracking differentiator (TD)-based dual-time-scale sliding mode control (SMC) method is proposed. First, the mathematical model is established in a two-phase synchronous rotating orthogonal reference coordinate system, and the fast and slow subsystems are then derived based on the quasi-steady-state theory. To address the conflict between reaching velocity and chattering phenomenon existing in the traditional exponential reaching law, a novel reaching law is introduced, allowing for a comparison and analysis of the reaching-time and switching-band. Next, the SMC laws are separately designed within a dual-time scale, thus resulting in the eventual TD-based composite non-cascade sliding mode controller. Finally, the advantages and effectiveness of the proposed methods are demonstrated through the simulation comparisons and experimental results. The results illustrate that the proposed control strategy can realize tracking without any overshoot, ensuring a fast dynamic response procedure in the servo system. The control system has the perfect dynamic performance when the PMSM operates in the reverse direction, and possesses strong robustness against the external load disturbances.

To address the challenge of effectively filtering constraints in the unit commitment problem constrained by large-scale line transmission networks, this paper reviews the operating principles of line constraints in both transient and steady states. An effective filtering method based on load similarity mining is proposed to eliminate redundant transmission constraints and reduce the complexity of the problem. Distance functions are developed to measure the similarity of historical load data according to the influence of different nodes on line flows. Based on the similarity analysis, typical power load scenarios are clustered, and effective line constraints are identified according to their operational significance. In addition, a pre-filtering strategy is applied to system states in which line statuses remain unchanged over time, thereby reducing the computational burden during the mining process. Simulations conducted on the IEEE 118 and Case2746wop systems validate the effectiveness of the proposed method, showing that the proposed method efficiently eliminates 99% of ineffective line constraints, and reduces solving time by over 80% compared to existing approaches.

Large-scale new energy bases in desert, Gobi, and arid regions are key components of new-type power systems in China. Considering factors such as construction cost and carbon emissions, the capacities of thermal power and energy storage in these bases are limited, resulting in constrained flexibility. Consequently, the scheduling and operation of these large bases face significant challenges. This paper proposes a coordinated day-ahead and real-time scheduling method for wind-thermal-storage integrated bases. In the day-ahead stage, the startup/shutdown plans and adjustable output ranges of thermal units are determined based on a rough prediction of wind power. Then, it constructs a wind power accommodation interval based on the adjustable range of thermal power output and the operational constraints of energy storage. In the real-time stage, dispatch strategies are generated using a quantile-based rule according to current wind and solar power output, eliminating the need for high-precision forecasts. It is further demonstrated that the dispatch strategies generated by the quantile rule inherently satisfy system operational constraints. The case study validates the effectiveness of the proposed method for wind-thermal-storage systems. The results demonstrate that the proposed method, which does not rely on point prediction, outperforms rolling optimization methods when the three-step prediction error exceeds 10%. Moreover, the performance of operational scheduling can be improved by enhancing the accuracy of day-ahead or intraday short-term forecasts. The proposed method provides valuable reference for the operation of large-scale new energy bases.

As one of the most promising carbon capture technologies for coal-fired power plants, oxy-fuel combustion provides a new solution for improving the flexibility of the integrated energy system (IES) and reducing carbon emissions. In this paper, a two-stage optimal dispatch strategy for the integrated energy system with oxy-fuel combustion units considering the constraints of multi-energy flexibility is proposed based on the intergration of oxy-fuel combustion technology and the optimal operation of the integrated energy system. First, a model of integrated energy system with oxy-fuel combustion (Oxy-IES) is established. Then, a matrix model of multi-energy flexibility constraints for Oxy-IES is proposed to reveal the supply and demand relationship of flexibility within the system. Finally, a two-stage optimization dispatch strategy for Oxy-IES is constructed, in which the output of each unit is optimized to minimize the daily operating cost of carbon trading in the day-ahead stage, while the rapid variable load capacity of the oxy-fuel combustion unit improves the flexibility of the system in the intraday stage. The simulation results of Oxy-IES show that the proposed strategy can improve the flexibility and economy performance of the IES while reducing carbon emissions.

As the installed capacity of wind power continues to increase, the frequency security of new power system becomes increasingly significant. To guarantee the frequency security of the system, improve the frequency regulation capability of the system, and determine an optimal wind power installed capacity, a wind power installed capacity optimization model considering dynamic frequency constraints as well as load-side inertia is proposed. First, the dynamic frequency response model with load-side inertia is derived. Then, fuzzy opportunity constraints are introduced considering the uncertainty of wind power, load, and load-side inertia. Taking into account the dynamic frequency constraints, the model incorporates multiple uncertainty fuzzy opportunity constraints, in which the uncertainty constraints are clearly converted into equivalence classes. Finally, to address the dynamic frequency-constrained nonlinear characteristic, the optimization problem is partitioned into a main problem and sub-problems for solution. The validity and feasibility of the proposed model are validated by using an improved 10-machine system.

To alleviate the adverse effect of large-scale electric vehicles (EVs) random charging, an EV hierarchical charging method considering multiple modes coordination is proposed in this paper, which avoids the large-scale charging load centralized in a certain period by coordinating diverse charging modes. Considering the load characteristics such as the output of renewable power generation, the power load curves of an area in Inner Mongolia Autonomous Region are clustered into five typical power load curves based on the K-means clustering algorithm and elbow method. According to the characteristics of EV charging and battery swapping (BS) modes, the charging station models and EV hierarchical power exchange model are established. At the upper level, the users’ requirements and charging costs are considered, and EVs are matched with the charging mode based on the particle swarm optimization algorithm. At the lower level, the electric price and charging station operation conditions are considered, and the charging schemes in the charging/BS station are optimized based on the optimization toolbox in the MATLAB software platform. Extensive case studies are conducted to validate the effectiveness of the proposed method, where a large number of EVs charge continuously with cost efficiency. However, relying solely on electricity price as the control signal for EV charging load may exacerbate the valley-to-peak disparity and instability of the local power load curve.

To tackle the challenges of high difficulty and time-consuming micro-site optimization of wind farms in complex terrains, a multi-objective optimization method for micro-site selection is proposed, assisted by proxy model. First, considering the geographical features of complex terrains with significent undulations, the ruggedness index is calculated and the ground flatness is numerically quantified, constraining the points with excessive ruggedness. Then, a mathematical model for three-dimensional windy downward wake superposition calculation of power generation is established, a three-dimensional terrain collector line topology optimization agent model is constructed, and the prediction accuracy of the proxy model is verified, demonstrating the ability to replace numerous calculations in collector line topology optimization and effectively improving the computing efficiency. Finally, taking a real complex terrain wind farm in Xinjiang Uygur Autonomous Region, China as an example, multi-objective micro-site selection of complex terrain wind farm is realized, and the results are compared with those obtained through the single-objective optimization. The simulation results show that the multi-objective discrete state transfer algorithm assisted by the proxy model can reduce the total cable laying length, decrease the construction costs, and provide more feasible layout schemes while optimizing the annual power generation.

With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers (DCs) have significantly increased. In recent years, the construction of data center integrated energy systems (DC-IES) has emerged as one of the critical trends in energy conservation and emission reduction for DCs under the global net-zero emission initiative. To support the planning and construction of low-carbon DC-IES, this paper proposes a multi-objective optimization model for capacity allocation and operational planning of DC-IES, integrating energy and economic considerations with a focus on low-carbon performance. Based on the “quality” analysis method of exergy from the second law of thermodynamics, the model proposed comprehensively accounts for the dynamic exergy efficient characteristics of energy conversion devices under varying load conditions, revealing the energy flow distribution characteristics of DC-IES under different objectives. The computational results indicate that compared with the optimization scheme assuming constant equipment efficiency, the scheme considering dynamic equipment efficiency reduces energy loss rate, economic cost, and carbon emissions by 2.6%, 1.9%, and 4.8%, respectively, demonstrating clear advantages. Moreover, compared with the economically optimal scheme, the multi-objective optimization scheme significantly reduces carbon emissions and energy loss rate of the DC-IES by 22.72% and 20.73%, respectively. Furthermore, compared to the scheme scenarios with the minimum exergy loss rate and lowest carbon emissions, the multi-objective optimization scheme reduces economic costs by 54.54% and 60.78%, respectively. Compared with the scheme relying solely on grid electricity supply, the multi-objective optimization scheme that regards the DC as an integrated energy system can reduce carbon emissions by 40.97%.

Severe geological disasters can lead to major power outages, posing serious threats to the safe and stable operation of power grids. To address the impact of geologic disasters on power grids, a power system security scheduling strategy based on disaster risk mapping is proposed. First, a disaster risk mapping strategy is constructed based on a variety of disaster impact factors, and the hazardous situation of the transmission line is obtained by overlaying this map with the geographic location of the transmission line. Then, a power system operation risk model is established to quantitatively analyze the risk of the system using composite risk indexes. Finally, a particle swarm algorithm is used to construct the regulation strategy of generators and flexibility resources using the New England power system as a case study to validate the effectiveness and feasibility of the strategy. The results show that the proposed power system security scheduling strategy can effectively reduce the comprehensive risk indicators, including the risk of power system overrun/heavy load, enhancing the security and stability of power grid operations.

Due to the complexity of equipment components in integrated energy systems, which involve multiple forms of energy such as electricity, gas, heat, and cooling, a massive amount of heterogeneous data is generated during interconnection and transmission processes, making it difficult to effectively reflect the characteristics of integrated energy systems. In light of this, basic units of integrated energy system are adopted as the research foundation in this paper. First, typical basic unit devices of integrated energy systems are carefully selected based on statistical clustering analysis, whose characteristic data are extracted to represent the basic unit features. Then, models of basic unit in integrated energy systems are established based on their energy and quality attributes. Finally, the rationality and applicability of the proposed models for various types of basic units in integrated energy systems are validated by case studies.

To address the problems in the profit model and transaction pricing of distributed energy storage providing multiple customized power services for sensitive customers, a multi-grade pricing strategy for distributed energy storage to provide various customized power services is proposed, including reactive power compensation, voltage sag control, and harmonic control. First, based on the four-quadrant operation characteristics of energy storage converter, a multi-grade evaluation indicator system of customized power services is established considering the differentiated user demands for power quality. Then, a cost-to-capacity model is developed for energy storage to provide customized power services in different power quality standards. Next, by taking economic loss of power quality into account, a user customized power utility function is established with individual rational constraint. Afterwards, considering power quality default risk and investment cost constraints, a customized power revenue model of distributed energy storage is constructed. Furthermore, a multi-grade trading framework for distributed energy storage to provide differentiated customized power services and its multi-grade pricing optimization strategy are proposed. Finally, in order to obtain the optimal additional tariff and user purchase package for premium power, the nonlinear multi-grade pricing model is transformed into a mixed integer linear programming model for optimization by using the big M method and transforming the user utility function into a constraint. The comparative analysis of the algorithm demonstrates that the proposed strategy can reduce the annual cost of customized power services for users while simultaneously enhancing the energy storage revenue.

In a composite energy storage system, coordinating the operation of different types of energy storage is an important approach to enhancing frequency regulation performance. To fully tap the potential of energy storage for frequency modulation, this paper proposes a secondary frequency modulation strategy based on a hybrid system combining battery energy storage and pumped hydro storage. To address the limitation of traditional first-order low-pass filter with fixed cutoff frequencies, it proposes a dynamic adjustment method for the filter time constant based on frequency variation, enabling flexible allocation of modulation commands in the composite storage system. During frequency modulation, it designs a dual fuzzy control strategy to coordinate battery energy storage and pumped hydro storage, taking into account the state of charge (SOC) constraint of the battery. During non-frequency modulation, it constructs the SOC self-recovery curve of the battery using the logistic function, and utilizes the remaining capacity of the pumped hydro storage to restore the battery SOC. Simulation analyses under two typical working conditions show that the proposed strategy has advantages in improving frequency modulation performance and maintaining the SOC of the battery energy storage system.

To utilize the operation data of photovoltaic(PV) panels for identifying key parameters of PV cells and diagnosing their operation status, the second-order Bézier function is combined with the adaptive war strategy optimization algorithm to obtain the optimal solutions for the five unknown parameters of silicon-based PV cell single diode topology: photogenerated current, diode reverse saturation current, diode ideal factor, series resistance and parallel resistance. The method includes theoretical and simulation analyses of four common faults, namely, shadowing, aging, short-circuit and open-circuit. By comparing the changes of the parameters in the identification results and the types of faults based on experimental validation, it is concluded that there is a certain correspondence between the five parameters of the PV single diode model and the four typical fault types. Additionly, the output behavior of PV modules in different fault types is characterized, which provides a reference for the identification of PV cell faults and the judgment of battery performance.

Surface discharge is a common type of discharge occurring in gas-insulated switchgear equipment, of which the microscopic process remains unclear. Additionly, there is a lack of theoretical correlation between the microscopic process of partial discharge due to defects and the macroscopic detection signals. First, the surface discharge process in SF₆ is simulated based on a fluid-chemical simulation model, revealing the variation patterns of charged particle concentration and surface streamer velocity. Then, taking the current pulse generated in the microscopic discharges as excitation sources, the discharge signals resulting from the surface discharges are simulated based on the finite integral method, establishing a correspondance between the microscopic partial discharge process and the detectable discharge signals. Compared with the conventional Gaussian excitation source, the time-domain waveforms of electromagnetic signals obtained from the microscopic discharge simulation more chosely matches to realistic conditions. These findings effectively supplement existing researches on the microscopic mechanisms of partial discharge signals, laying a foundation for the insulation state evaluation based on the discharge signal analysis.