To achieve accurate load awareness and efficient device control, this study proposes a novel non-intrusive load decomposition （NILD） method by combining multi-modal feature selection techniques with an improved time convolutional network to enhance identification accuracy and efficiency. In this method, the load multi-scale maximum overlapping discrete wavelet transform results are introduced as input features, and the feature is combined with the mRMR algorithm to ensure the effective integration of multi-modal features. At the same time, the adaptive receptive field attention mechanism （RFAM） is added to the TCN method,improving the ability to extract features at different time scales. Finally, the algorithm is validated on datasets such as PLAID. The results show that the proposed method can achieve more accurate and efficient load decomposition with fewer features,which is higher than the regression accuracy of the traditional BiLSTM model of 5.31%,and the detection rate of new energy household equipment such as photovoltaic power has also reached over 96.6%.

The cascaded H-bridge topology exhibits distinct advantages in the field of photovoltaic （PV） power generation with its modular design, smaller filtering inductance, and simplified layout. However, the three-phase cascaded H-bridge inevitably inherits the inherent issue of the single-phase full-bridge inverter, namely, double-line frequency voltage ripple on the DC-link. This issue leads to an output voltage deviation of photovoltaic array from its maximum power point, thereby reducing the power generation efficiency of system. To address this issue, this paper presents an innovative control method based on adaptive third-harmonic injection, which effectively avoids over-modulation risks under varying power factor angles. The method can calculate the optimal third-harmonic injection based on the real-time operating conditions of the system, thereby achieving optimal suppression of DC-link voltage ripple. Finally, a full-scale experimental platform is constructed, and experimental results confirm the effectiveness and feasibility of the proposed method.

To enhance the consumption capacity of distributed photovoltaic （PV） and improve the power supply reliability of rural areas, this paper proposes a hierarchical coordinated planning method of PV and energy storage system for rural microgrids. Firstly, the medium and long-term load forecasting model based on the DenseNet-Autoformer network is proposed, which uses the autocorrelation mechanism to extract the deep time-series features, and comprehensively considers factors such as meteorology and economy on long-term monthly electricity consumption; Secondly, the Wasserstein generative adversarial network-gradient penalty method is used to expand the PV power samples. On this basis, the typical source and load scenarios are obtained via Gaussian mixture clustering; Finally, taking into account the electricity self-sufficiency and the off-grid reliability, the PV and storage configuration plan of the distribution transformer area is optimized, and the power supply reliability under extreme scenarios is quantified based on chance constraints. Meanwhile, considering the overall energy self-sustained ability of the 10 kV feeder, the feeder-level PV and storage configuration plan is optimized. The simulation results show that the proposed method can enhance the energy self-sustained ability and PV consumption capacity of rural microgrids and improve the power supply reliability in extreme scenarios.

A dual-winding induction machine based inverting system is proposed in this paper. The dual winding induction machine with two electrical interfaces is utilized as an energy conversion hub between the renewable energy source and the grid. It enhances the motor characteristics of the power system while completing the renewable energy integration. In order to make it valuable for application, this paper focuses on its control strategy. This paper sets out the analysis of the operating characteristics of the dual-winding induction machine. The analysis is used to design a set of multi-condition control strategies for different operating conditions. This strategy is intended to meet the control requirements of startup, integration into the grid, grid-connected operation and off-grid operation. The experimental results verify the effectiveness of the proposed control strategy.

In this paper, the optimization strategy of reserve capacity and cost sharing mechanism are studied considering the source-load uncertainty. The probability distribution model of wind and PV forecast deviation is improved based on Gauss-Laplace distribution, and an improved Latin hypercube sampling method is proposed based on Box-Muller transformation to promote the efficiency and accuracy. And then a reserve capacity optimization model is constructed to minimize the cost considering the constraints of condition risk. Finally, a reserve cost sharing mechanism is established with the objective of achieving Maxmin fairness in the allocation utility value and the constraint interval of cost allocation as the constraint condition. The IEEE-30 standard test cases show that the improved Latin hypercube sampling method can effectively improve the sampling efficiency and reduce the computation time, and the benefit sharing is more reasonable based on the proposed strategy, which can more effectively motivate the demand side to participate in the standby market.

To mitigate the adverse effects of wind power output fluctuations, low-frequency oscillations, and multi-controller coupling in power systems, this paper proposes a coordinated optimization strategy for multi-controller parameters under diverse operating conditions. Firstly, a Simulink model of the wind-PV-thermal bundled power transmission system and its controllers is established. Typical wind power output scenarios are generated using the Voronoi partition sampling method. Subsequently, the objective function for the optimization problem is formulated based on probabilistic methods, and critical controller parameters for optimization are identified via random forest regression analysis. Finally, an improved hybrid dung beetle optimizer is employed to coordinately optimize these key controller parameters across multiple operating scenarios. Simulation results on the IEEE 4-machine 2-area system and the 16-machine 5-area system demonstrate that optimizing the extracted controller parameters effectively enhances system damping. This significantly suppresses fluctuations in active power, power angles, and other system variables caused by low-frequency oscillations, thereby validating the efficacy of the proposed probabilistic optimization approach.

To explore a new mechanism of frequency response with multi-resource coordination, a day-ahead and intra-day decentralized mutual-aid method of energy and auxiliary services is proposed. Firstly, based on the mutual-aid of energy resources, the framework of multivariate adjusting resources is established including day-ahead and intra-day energy, inertia and primary frequency regulation （PFR）. Secondly, the shared alternating direction method of multiplier（ADMM） is adopted to realize efficient solutions of the model and protect the privacy of mutual-aid subjects, in which the alternating optimization procedure （AOP） is established to address the non-convexity. Finally, a simulation example is given to verify that the proposed method can effectively motivate multiple regulatory resource suppliers to provide inertia and PFR services, which provides a reference for promoting mutual-aid of frequency regulation resources.

An integrated energy system coupled with solar energy and biogas is designed in this study, and a joint optimization method of capacity and operation is proposed. Firstly, a thermodynamic model of biogas production is introduced, and a capacity-operation two-layer optimization model is constructed. The upper layer optimizes the size of the biogas digester and the capacity of energy conversion and energy storage equipment to minimize the annual total cost. The lower layer takes the upper-layer results as constraints to optimize the operation plan of energy conversion and energy storage equipment, minimizing the operation cost. Secondly, by integrating the nonlinear programming method into the genetic algorithm, the model is solved to determine the optimal capacity configuration and operation scheme of the system. Finally, five simulation scenarios are used to verify the effectiveness of the proposed system and the joint optimization method. The results show that the integrated energy system optimized by the proposed method has the best economic performance, and the energy consumption scheme is efficient and reasonable.

To address the low efficiency and high pollution associated with fossil-energy-dominated energy use in rural industrial parks, a park-level integrated energy system （PIES） model is established based on the source-load characteristics of rural parks. To promote renewable energy accommodation and biomass utilization, a power-to-ammonia system （P2A） and a biomass anaerobic fermentation gas production system （AF） are introduced into the PIES and modeled in detail. Considering the influence of anaerobic fermentation temperature on gas production rate, the waste heat from the P2A system is delivered to the AF system to construct a P2A-AF coupled system, thereby improving system energy efficiency and economy. On this basis, detailed carbon emission modeling is carried out for the main energy-use processes of the PIES, and carbon emission costs are incorporated to establish an optimal dispatch model with the minimum total cost as the objective. The simulation results show that the proposed system can improve energy economy by 31.56%, increase the local accommodation rate of renewable energy by 34.92%, and reduce total carbon emissions by 3.58%.

This paper takes the IEA 15 MW wind turbine blade as the research object, establishes the dynamic model of the aeroelastic system for the blades, analyzes the aeroelastic response during flutter at high tip speed ratio, and conducts in-depth research on the multi-degree-of-freedom coupling characteristics therein. The results show that at high tip speed ratio, the long and flexible blade of this wind turbine undergoes a coupled bending-torsion flutter of pitch-torsion. The vibrations in the three degrees of freedom all contain the natural frequencies of pitch and torsion. However, the maintenance of flutter is sustained by the aerodynamic power work in the pitch and torsion degrees of freedom. There is an obvious coupling phenomenon in the development of the instantaneous aerodynamic power of pitch and torsion. In the direction of yaw, there is basically no energy contribution. There is only weak aerodynamic power throughout the process, and it only undergoes forced vibrations due to the excitation of pitch and torsion because of the coupling characteristics of the blade structure.