Search Results (620)

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article

The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article

With the support of the dual-carbon strategy and related policies, China’s offshore wind power has experienced rapid development. However, constrained by the inherent intermittency and volatility of wind power, large-scale expansion poses significant challenges to grid integration and exacerbates government fiscal burdens. To address these critical issues, this paper proposes a multi-market coupling trading model integrating energy storage-equipped offshore wind power into electricity–carbon–green certificate markets for large-scale grid networks. Firstly, a day-ahead electricity market optimization model that incorporates energy storage is established to maximize power revenue by coordinating offshore wind power generation, thermal power dispatch, and energy storage charging/discharging strategies. Subsequently, carbon market and green certificate market optimization models are developed to quantify Chinese Certified Emission Reduction (CCER) volume, carbon quotas, carbon emissions, market revenues, green certificate quantities, pricing mechanisms, and associated economic benefits. To validate the model’s effectiveness, a gradient ascent-optimized game-theoretic model and a double auction mechanism are introduced as benchmark comparisons. The simulation results demonstrate that the proposed model increases market revenues by 17.13% and 36.18%, respectively, compared to the two benchmark models. It not only improves wind power penetration and comprehensive profitability but also effectively alleviates government subsidy pressures through coordinated carbon–green certificate trading mechanisms. Full article

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. Full article

After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems. Full article

Against the backdrop of the increasing penetration rate of new energy year by year, power systems face a continuously growing demand for flexibility. Under the structure of such a new power system, it is essential not only to introduce diverse flexible power sources but also to explore the flexible regulation capabilities of existing conventional power sources. To fully utilize the flexibility of thermal power units (TPUs), this study proposes a real-time optimal scheduling strategy for a wind–thermal energy-storage integrated system with an adaptive time division and variable objectives. Based on the evaluation results of the real-time flexible supply–demand relationship within a regional power grid, the operation modes of TPUs are categorized into three types: economic mode, peak shaving mode, and coordination mode. For each operation mode, corresponding optimization objectives are defined, and an energy storage control strategy is developed to assist in the peak shaving of TPUs. While effectively harnessing the flexibility of TPUs, the proposed method reduces both the frequency and capacity of TPUs entering deep peak shaving. Using data from a province in Northwest China as a case study, simulation calculations and analyses demonstrate that the proposed method increases renewable energy consumption by 314.37 MWh while decreasing system economic benefits by CNY 129,000. Compared with traditional scheduling methods for TPUs to accommodate renewable energy, the system benefit increases by CNY 297,000, and an additional 13.53 MWh of peak wind power is accommodated. These results confirm that the proposed scheduling strategy can significantly enhance the system’s ability to integrate new energy while maintaining its economic efficiency. Full article

This study analyzed the frequency control challenges within the West Africa Power Pool Interconnected Transmission System (WAPPITS) as it plans to incorporate variable renewable energy (VRE) resources, such as wind and solar energy. Concerns center on the ability of WAPPITS primary frequency control reserves to adapt to high VRE penetration given the synchronization and frequency control problems experienced by the three separate synchronous blocks of WAPPITS. Optimizing solutions requires a better understanding of WAPPITS’ current frequency control approach. This study used questionnaires to understand operators’ practical experience with frequency control and compared these observations to field tests at power plants and frequency response metrics during system events. Eight (8) of ten (10) Transmission System Operators (TSOs) indicated that primary frequency control service was implemented in the TSO, but nine (9) of ten TSOs indicated that the reserves provided were inadequate to meet system needs. Five (5) of ten (10) respondents answered “yes” to the provision of secondary frequency control service, while only one (1) indicated that secondary reserves were adequate. Three (3) TSOs indicated they have AGC (Automatic Generation Control) installed in the control room, but none have implemented it for secondary frequency control. The results indicate a significant deficiency in primary control reserves, resulting in a reliance on under-frequency load shedding for primary frequency control. Additionally, the absence of an AGC system for secondary frequency regulation required manual intervention to restore frequency after events. To ensure the effectiveness of battery energy storage systems (BESSs) and the reliable operation of the WAPPITS with a higher penetration of inverter-based VRE, this paper recommends (a) implementing and enforcing basic primary frequency control structures through regional regulation and (b) establishing an ancillary services market to mobilize secondary frequency control resources. Full article

The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such as (1) the spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) the cost dependencies of the different components and system characteristics. When conducting such analyses at the regional or national scale, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, identifying the lack of national-scale product cost models for HPPs as the main gap and establishing a replicable methodology for hybrid plant mapping. A simplified example is shown using this methodology for a country-level analysis. Full article

Amid the global transition toward sustainable energy, regional power grids with high wind power penetration are increasingly emerging. The implementation of frequency control is critically essential for enhancing the frequency support capability of grid-connected devices. However, existing studies indicate this may induce ULFOs (ultra-low frequency oscillations). Current research on ULFOs have been predominantly concentrated on hydro-dominated power systems, with limited exploration into systems where thermal power serves as synchronous sources—let alone elucidation of the underlying mechanisms. This study focuses on regional power grids where wind and thermal power generation coexist. Eigenvalue analysis reveals that frequency regulation control of doubly-fed induction generators (DFIGs) can trigger ULFOs. Leveraging common-mode oscillation theory, an extended system frequency response (ESFR) model incorporating DFIG frequency control is formulated and rigorously validated across a range of operational scenarios. Moreover, frequency-domain analysis uncovers the mechanism by which inertia control affects ULFO behavior, and time-domain simulations are conducted to validate the influence of DFIG control parameters on ULFOs. Full article

Transitioning to sustainable energy systems is crucial for reducing greenhouse gas (GHG) emissions, especially in remote industrial operations where diesel generators remain the dominant power source. This study examines the feasibility of integrating a redox flow battery (RFB) storage system to optimize wind energy utilization at the Raglan mining site in northern Canada, with the goal of reducing diesel dependency, enhancing grid stability, and improving energy security. To evaluate the effectiveness of this hybrid system, a MATLAB R2024b-based simulation model was developed, incorporating wind energy forecasting, load demand analysis, and economic feasibility assessments across multiple storage and wind penetration scenarios. Results indicate that deploying 12 additional E-115 wind turbines combined with a 20 MW/160 MWh redox flow battery system could lead to diesel savings of up to 63.98%, reducing CO₂ emissions by 68,000 tonnes annually. However, the study also highlights a key economic challenge: the high Levelized Cost of Storage (LCOS) of CAD (Canadian dollars) 7831/MWh, which remains a barrier to large-scale implementation. For the scenario with high diesel economy, the LCOS was found to be CAD 6110/MWh, and the corresponding LCOE was CAD 590/MWh. While RFB integration improves system reliability, its economic viability depends on key factors, including reductions in electrolyte costs, advancements in operational efficiency, and supportive policy frameworks. This study presents a comprehensive methodology for evaluating energy storage in off-grid industrial sites and identifies key challenges in scaling up renewable energy adoption for remote mining operations. Full article

Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance efficiency and accuracy. In this paper, we propose a Dynamic Significance–Correlation Weighting (D-SCW) method, which generates dynamic weight coefficients that evolve over time. This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. The results show that the D-SCW-LightGBM framework reduces the root mean squared error (RMSE) by 5.2% to 10.4% and shortens the dynamic response delay by 52% compared with the benchmark method in high renewable penetration scenarios, confirming its effectiveness in both prediction accuracy and computational efficiency. Full article

Driven by the demand for low-carbon and sustainable development, power systems are increasingly transitioning toward higher proportions of renewable energy and power-electronic interfaces, leading to a growing requirement for wind turbines to provide inertia support and frequency regulation (FR). Wind turbine kinetic energy-based FR inherently involves a trade-off between rotor speed recovery and grid stability: aggressive acceleration exacerbates the secondary frequency drop (SFD), while suppressing SFD prolongs rotor speed recovery. This study aims to resolve this dynamic coupling conflict and optimize the rotor speed recovery process by employing a segmented rotor speed recovery strategy. Firstly, a detailed wind farm-integrated frequency response model is developed. Leveraging its identified speed recovery dynamics, a five-dimensional rotor speed recovery evaluation framework is established. Subsequently, guided by this evaluation framework, a segmented rotor speed recovery control strategy is designed. Finally, three validation scenarios—a single wind turbine, 10% wind power penetration, and 30% wind power penetration—are constructed to evaluate the proposed strategy. Comparative analysis demonstrates that the proposed segmented rotor speed recovery strategy reduces aerodynamic power recovery time by 28.5% and power disturbance by 47.3% in an operational scenario with 30% wind power penetration, effectively achieving synergistic coordination of recovery acceleration and SFD suppression. Full article

As renewable energy penetration and extreme weather events increase, accurately predicting power system behavior is essential for reducing risks and enabling timely interventions. This study presents a physics-informed learning approach to forecast transient voltage angles in power systems with integrated wind energy under gusty wind conditions. We developed a simulation framework that generates wind power profiles with significant gust-induced variations over a one-minute period. We evaluated the effectiveness of physics-informed neural networks (PINNs) by integrating them with LSTM (long short-term memory) and GRU (gated recurrent unit) architectures and compared their performance to standard LSTM and GRU models trained using only mean squared error (MSE) loss. The models were tested under three wind energy penetration scenarios—20%, 40%, and 60%. Results show that the predictive accuracy of PINN-based models improves as wind penetration increases, and the best-performing model varies depending on the penetration level. Overall, this study highlights the value of physics-informed learning for dynamic prediction under extreme weather conditions and provides practical guidance for selecting appropriate models based on renewable energy integration levels. Full article

This research paper analyses the evolution of electricity price volatility in six European countries between 2015 and 2025, focusing on the relationship between the increasing penetration of renewable energy sources (RES) and short-term price fluctuations. Based on high-frequency data (at 15 min to hourly resolution) on electricity prices, solar and wind generation, and residual load, both year-on-year and structural changes in volatility are quantified. The results show a significant increase in volatility after 2021, with outliers appearing particularly during the 2022 energy crisis, most notably in countries with a high share of RES and limited system flexibility. The analysis identifies non-linear relationships between RES generation and the occurrence of negative prices, with country-specific threshold levels. Annual regression models show that the predictive power of these relationships is time-varying and influenced by externalities. The correlation matrices confirm regional differences in the impact of RES on price dynamics. The results support the design of rules for forecasting risk periods and point to the need for market mechanisms increasing flexibility, including accumulation, demand management, and cross-border integration. Full article

The increasing penetration of renewable energy sources presents significant challenges for power system stability and operation. Accurately assessing renewable energy absorption capacity is essential to ensuring grid reliability while maximizing renewable integration. This paper proposes a security-constrained sequential production simulation (SPS) framework, which incorporates grid voltage and frequency support constraints to provide a more realistic evaluation of renewable energy absorption capability. Additionally, hierarchical clustering (HC) based on dynamic time warping (DTW) and min-max linkage is employed for temporal aggregation (TA), significantly reducing computational complexity while preserving key system characteristics. A case study on the IEEE 39-bus system, integrating wind and photovoltaic generation alongside high-voltage direct current (HVDC) transmission, demonstrates the effectiveness of the proposed approach. The results show that the security-constrained SPS successfully prevents overvoltage and frequency deviations by bringing additional conventional units online. The study also highlights that increasing grid demand, both locally and through HVDC export, enhances renewable energy absorption, though adequate grid support remains crucial. Full article