With the large-scale integration of renewable energy through power electronic inverters, modern power systems are gradually transitioning to low-inertia systems. Grid-forming inverters are prone to power overshoot and frequency deviation when facing external disturbances, threatening system stability. Existing methods face two main challenges in dealing with complex disturbances: neural-network-based approaches have high computational burdens and long response times, while traditional linear algorithms lack sufficient precision in adjustment, leading to inadequate system response accuracy and stability. This paper proposes an innovative coordinated adaptive control strategy for virtual inertia and damping. The strategy utilizes a Radial Basis Function neural network for the adaptive regulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm. This approach provides refined inertia regulation while maintaining computational efficiency, optimizing the rate of change in frequency and frequency deviation. Simulation results demonstrate that the proposed control strategy significantly outperforms traditional methods in improving system performance. In the active power reference variation scenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by 66.7%, and the system recovery time is shortened. In the load variation scenario, frequency overshoot is reduced by approximately 3.6%, and the maximum frequency deviation is reduced by approximately 26.9%. In the composite disturbance scenario, the frequency peak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by 35%, and the power response improves by 23.3%. These results indicate that the proposed method offers significant advantages in enhancing system dynamic response, frequency stability, and power overshoot suppression, demonstrating its substantial potential for practical applications.

The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of “key component–wind turbine–wind farm” based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the “maximum membership degree”, which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms.

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration.

With the rapid expansion of renewable energy and the acceleration of electricity market reforms, coal-fired units are facing increasing difficulty in recovering fixed costs due to marginal cost-based bidding competition and depressed clearing prices caused by low-cost renewable integration, circumstances in which reasonable returns and investment incentives for coal-fired power plants are not guaranteed. To address this issue, this paper proposes a capacity cost compensation mechanism for coal-fired power in the electricity market environment. First, a joint clearing model for the electricity spot market considering both energy and reserve services is established, and annual market operation simulations are conducted to obtain unit output schedules, clearing prices, and annual revenues. Second, based on the long-term simulation results, the marginal clearing probability and fixed cost recovery deficit of each coal-fired unit are calculated, and a capacity compensation pricing method based on marginal clearing probability weighting is proposed to determine the system unit capacity compensation price. Subsequently, the compensated capacity is determined using the availability factor method, comprehensively reflecting each unit’s actual contribution to system capacity adequacy. Finally, case studies conducted on a modified IEEE 30-bus system validate the effectiveness of the proposed mechanism. The results demonstrate that following the implementation of the proposed mechanism, the investment payback periods of all coal-fired units are reduced to within the planned 20-year horizon, thereby ensuring the sustainable operation of coal-fired units and maintaining adequate reliability margins in the power system.

Addressing the challenges of insufficient reserve capacity allocation and wind power uncertainty-induced security and economic concerns under high wind power penetration, this paper develops an integrated energy–reserve market clearing model for regional electricity markets. Firstly, a comprehensive day-ahead market clearing mechanism is designed, encompassing market participant bidding, security-constrained unit commitment (SCUC), security-constrained economic dispatch (SCED), nodal marginal price calculation, and market settlement. Secondly, a SCUC model targeting the minimization of total system operating costs and a SCED model targeting the minimization of energy and reserve procurement costs are established, comprehensively incorporating constraints, such as power balance, unit output and ramping limits, reserve requirements, and network power flows, with nodal marginal prices calculated using the Lagrangian multiplier method. Finally, simulation verification is conducted using a modified IEEE 30-bus system as a case study. Results demonstrate that the proposed model effectively coordinates wind power integration with system reserve requirements, achieving economically optimal dispatch while ensuring grid security and stability. Thermal units obtain substantial market revenues by providing reserve ancillary services, while wind units achieve high revenues through zero marginal cost advantages, fully validating the model’s effectiveness and economic efficiency under high wind power penetration conditions. The research findings provide theoretical foundations and practical guidance for constructing electricity spot market mechanisms adapted to large-scale renewable energy integration.