Search Results (343)

With the increasing integration of renewable energy sources and power electronic converters, Grid-Forming (GFM) technologies such as Virtual Synchronous Generators (VSGs) have emerged as key enablers of future power systems. However, conventional VSG control strategies with fixed parameters often fail to maintain transient stability under dynamic grid conditions. This paper proposes a novel adaptive GFM control framework based on physics-informed reinforcement learning, targeting transient power angle stability in systems with high renewable penetration. An adaptive controller, termed the 3N-D controller, is developed to periodically update the virtual inertia and damping coefficients of VSGs based on real-time system observations, enabling anticipatory adjustments to evolving operating conditions. The controller leverages a reinforcement learning architecture embedded with physical priors, which captures the high-order differential relationships between rotor angle dynamics and control variables. This approach enhances generalization, reduces data dependency, and mitigates the risk of local optima. Comprehensive simulations on the IEEE-39 bus system with varying VSG penetration levels validate the proposed method’s effectiveness in improving system stability and control flexibility. The results demonstrate that the physics-embedded GFM strategy can significantly enhance the transient stability and adaptability of future power grids. Full article

With the continuous increase in renewable energy penetration, traditional frequency regulation strategies in power grids struggle to maintain frequency stability under high renewable-share conditions. To address the shortcomings of the current deadband settings in regional grid frequency regulation, this paper proposes an optimized deadband-configuration scheme for renewable energy power plants and evaluates its effectiveness in enhancing the frequency regulation potential of renewable units. By developing frequency response models for thermal power, wind power, photovoltaic generation, and energy storage, the impact of different deadband widths on dynamic frequency response and steady-state deviation is analyzed. Three representative output scenarios for renewable units are constructed, and under each scenario the coordinated control performance of the proposed and the existing deadband configurations is compared. Simulation studies are then conducted based on a typical high renewable penetration scenario. The results show that, compared with the existing regional-grid deadband settings, the proposed configuration more fully exploits the regulation potential of renewable units, improves overall frequency-response capability, significantly reduces frequency deviations, and shortens recovery time. This research provides both theoretical foundations and practical guidance for frequency-support provision by renewable energy power plants under high penetration conditions. Full article

Amid escalating climate challenges, China’s carbon neutrality objectives necessitate energy electrification as a pivotal strategy. As a critical load hub, East China demonstrates significant trends toward cleaner energy—marked by growing renewable energy penetration and accelerated cross-regional direct current (DC) transmission deployment. Ensuring stable and efficient grid operation requires rigorous assessment of the impacts of ultra-high voltage DC (UHVDC) integration on grid stability. This study introduces the transient multi-feed-in short circuit ratio (TMSCR), a novel metric for evaluating new DC transmission systems’ influence on grid performance. We systematically investigate UHVDC integration within the East China power grid, emphasizing strategic DC landing point placement. Using TMSCR, the effects of diverse DC incorporation methods are analyzed. Furthermore, this research examines impacts of new DC connections on local and main grids, proposing targeted mitigation measures to enhance grid resilience. This comprehensive UHVDC impact analysis addresses a critical literature gap, providing actionable insights for East China power grid planning and establishing a foundation for subsequent grid planning and DC project feasibility studies during the ‘15th Five-Year Plan’ period. Full article

With the rapid growth in the scale of high-percentage new energy generation, the structure of the new power system is changing. Influenced by the uncertainty and zero marginal cost characteristics of new energy, the security cost required by the power system under the high proportion of new energy access has increased dramatically. How to accurately measure the cost of the power system and assess the trend of the system cost changes and the impact on its end-user price has become critical. Accordingly, this paper creatively proposes a levelized cost of electricity (LCOE) prediction and end-user price deduction model for power systems with high renewable energy penetration. Firstly, the power system factor cost prediction model is constructed from the three dimensions of power-side, grid-side, and system operation cost. Secondly, a levelized cost of electricity prediction model is constructed based on the above model. Again, based on the analysis of the end-user price composition, the end-user price deduction model is proposed. Finally, the data of Gansu Province is selected for example analysis, and the results show that, in 2060, the power LCOE will be 0.064 USD/kWh, the system LCOE will be 0.103 USD/kWh, and the end-user price will rise to 0.1 USD/kWh. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

This paper presents a novel literature survey on leveraging electrolysis for grid frequency stabilization in power systems with high penetration of renewable energy sources (RESs), uniquely integrating global research findings with specific insights into the Polish energy context—a region facing acute grid challenges due to rapid RES growth and infrastructure limitations. The intermittent nature of wind and solar power exacerbates frequency fluctuations, necessitating dynamic demand-side management solutions like hydrogen production via electrolysis. By synthesizing over 30 studies, the survey reveals key results: electrolysis systems, particularly PEM and alkaline electrolyzers, can reduce frequency deviations by up to 50% through fast frequency response (FFR) and primary reserve provision, as demonstrated in simulations and real-world pilots (e.g., in France and the Netherlands); however, economic viability requires enhanced compensation schemes, with current models showing unprofitability without subsidies. Technological advancements, such as transistor-based rectifiers, improve efficiency under partial loads, while integration with RES farms mitigates overproduction issues, as evidenced by Polish cases where 44 GWh of solar energy was curtailed in March 2024. The survey contributes actionable insights for policymakers and engineers, including recommendations for deploying electrolyzers to enhance grid resilience, support hydrogen-based transportation, and facilitate Poland’s target of 50.1% RESs by 2030, thereby advancing the green energy transition amid rising instability risks like blackouts in RES-heavy systems. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

South Africa’s transition to a renewable-powered grid faces critical challenges due to the inherent variability of wind and solar generation as well as the need for economically viable and reliable dispatch strategies. This study proposes a scenario-based stochastic optimization framework that integrates machine learning forecasting and uncertainty modeling to enhance operational decision making. A hybrid Long Short-Term Memory–XGBoost model is employed to forecast wind, photovoltaic (PV) power, concentrated solar power (CSP), and electricity demand, with Monte Carlo dropout and quantile regression used for uncertainty quantification. Scenarios are generated using appropriate probability distributions and are reduced via Temporal-Aware K-Means Scenario Reduction for tractability. A two-stage stochastic program then optimizes power dispatch under uncertainty, benchmarked against Deterministic, Rule-Based, and Perfect Information models. Simulation results over 7 days using five years of real-world South African energy data show that the stochastic model strikes a favorable balance between cost and reliability. It incurs a total system cost of ZAR 1.748 billion, with 1625 MWh of load shedding and 1283 MWh of curtailment, significantly outperforming the deterministic model (ZAR 1.763 billion; 3538 MWh load shedding; 59 MWh curtailment) and the rule-based model (ZAR 1.760 billion, 1.809 MWh load shedding; 1475 MWh curtailment). The proposed stochastic framework demonstrates strong potential for improving renewable integration, reducing system penalties, and enhancing grid resilience in the face of forecast uncertainty. Full article

With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems. Full article

The large-scale integration of renewable energy and the high penetration of power electronic devices have led to a significant reduction in system inertia and short-circuit capacity. This is particularly manifested in the form of insufficient multiple renewable energy stations short-circuit ratio (MRSCR) and transient overvoltage issues following severe disturbances such as AC and DC faults, which greatly limit the power transfer capability of large renewable energy bases. To effectively mitigate these challenges, this paper proposes an optimal synchronous condenser deployment method tailored for large-scale renewable energy bases. The proposed mathematical model supports a hybrid centralized and distributed configuration of synchronous condensers with various capacities and manufacturers while considering practical engineering constraints such as short-circuit ratio, transient overvoltage, and available bays in renewable energy stations. A practical decomposition and iterative computation strategy is introduced to reduce the computational burden of transient stability simulations. Case studies based on a real-world system verify the effectiveness of the proposed method in determining the optimal configuration of synchronous condensers. The results demonstrate significant improvements in grid strength (MRSCR) and suppression of transient overvoltages, thereby enhancing the stability and transfer capability of renewable energy bases in weak-grid environments. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article

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

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration. Full article