Dynamic Grid Research Articles

Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method—Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models—exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)—are developed, each reflecting a different price elasticity of demand. These models are integrated with a flexible elasticity matrix to assess the dynamic consumer response to fluctuating electricity prices. The study evaluates four operational scenarios, focusing on grid participation, DER utilization, and the impact of real-time pricing (RTP), time of use (TOU), and critical peak pricing strategies. Quantitative results demonstrate the significant cost-saving potential of integrating DRPs with microgrid operations. In the optimal scenario, the GRSO achieved a minimum generation cost of 882¥ for the base load profile. Further, when critical peak pricing (CPP) was applied, the generation cost was reduced to 746¥, representing a 15.4% reduction. For a scenario where the grid’s participation was limited, the logarithmic-based demand response model decreased the generation cost to 817¥, while full grid interaction led to higher cost reductions. Additionally, our results show a significant reduction in peak load, with load factor improvements of up to 87.7% across the studied demand profiles. Furthermore, limiting the grid’s upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in terms of both execution time and convergence, making it a viable solution for real-time microgrid optimization. In conclusion, the proposed GRSO-based framework provides an efficient approach for microgrid cost minimization, achieving up to a 15.4% reduction in operational costs and notable environmental benefits by reducing emissions. This study highlights the importance of dynamic demand response strategies and grid participation for sustainable and cost-effective microgrid management.

Optimizing Power Quality in EV Chargers Using Advanced Quadrature Signal Generators and AI‐Driven Adaptive Filtering

ABSTRACTAs electric vehicle (EV) adoption grows, vehicle‐to‐grid (V2G) technology enables bidirectional power flow in grid‐interactive EV chargers. However, maintaining consistent power quality under nonideal grid conditions remains a challenge. Traditional PI controllers struggle to reduce total harmonic distortion (THD) and adjust to dynamic grid variations. This study explores machine learning techniques, including decision trees, artificial neural networks (ANN), and linear regression, as alternatives to conventional PI controllers. Decision trees emerge as the most advantageous due to their simplicity, interpretability, and ability to handle complex, nonlinear relationships with minimal data preprocessing. While ANN captures intricate patterns, it demands more computational resources and lacks transparency. Linear regression, though efficient, struggles with complex grid behaviors. The decision tree approach allows real‐time adaptive control, improving THD reduction and grid stability. Additionally, a CNISOGI filter is implemented to enhance harmonic attenuation and DC‐offset rejection. The system's effectiveness is validated through Matlab/Simulink simulations and a 1.1 kW hardware prototype. The results show that integrating decision tree‐based controllers with advanced filtering techniques can significantly enhance power quality, grid stability, and operational efficiency in future smart grids.

Novel Cartesian Grid-Stitching Algorithm Suitable for Dynamic-Boundary Euler Equation Computations

The aerodynamic characteristics of flowfields involving dynamic boundaries are complicated, imposing rigorous demands on grid generation and solution accuracy for numerical computations. Existing grid methodologies, such as overlapping grids and dynamic unstructured grids, struggle to ensure computational efficiency and solution accuracy simultaneously. Consequently, this paper presents a novel Cartesian grid-stitching algorithm designed to tackle the complexities associated with dynamic boundary problems. Firstly, a boundary normal projection approach is introduced for grid stitching, constructing a Cartesian-like grid capable of automated reconfiguration. The body-fitted portion dynamically selects nodes from the Cartesian grid for reconstruction, ensuring no grid distortion and eliminating the need for frequent deletion or addition of grid cells. Therefore, it demonstrates high adaptability to complex configurations and readily accommodates aerodynamic challenges involving moving boundaries with large-scale motion. Secondly, at the grid-stitching interfaces, the finite difference method for an unstructured grid is employed for spatial variable discretization, eliminating interpolation errors typically introduced. It enables interpolation-free information interaction between grids, achieving consistent discretization accuracy across the entire computational domain. Furthermore, three sets of numerical tests are conducted, with simulating results showing good agreement with those in the literature. This algorithm has proven effective in accurately capturing flow features involving shockwave interactions.

Study on the Behavior of Cement Slurry Prepared by Vibration Agitation and Numerical Simulation of Flow Field

Vibration agitation, a mechanically enhanced material mixing technique, is appropriate for preparing concrete- and other cement-based materials as it helps to eliminate the agglomeration effect between particles, improving mixing efficiency and material properties and saving resources. Applying vibration agitation to the large-scale industry is challenging primarily due to the difficulty in elucidating the microscopic mixing mechanism. To this end, the Euler multiphase flow model and ALE dynamic grid technology were combined to create the flow field simulation model of the cement slurry vibration mixing process in this paper. The physical parameters and mixing characteristics of various structure agitators under various vibration mixing frequencies were investigated as well. The findings demonstrated that vibration agitation significantly improved the mixing effect and the mixing efficiency and homogeneity were positively correlated with the excitation frequency. The slurry had a larger average velocity and turbulent kinetic energy, along with a reduced frictional viscous moment on the agitator. The vibration altered the system’s pressure field and fluid movement direction. Furthermore, the improvement of the flow field parameters for the horizontal shaft mixer was highly sensitive in the low-frequency range, whereas for vertical shaft agitators, it was at higher excitation frequencies that more significant gains were achieved.

Numerical Simulation on Self-Propulsion Characteristics of Bionic Flexible Foil Considering Ground Wall Effect.

In order to figure out the wall effect on the propulsive property of an auto-propelled foil, the commercial open-source code ANSYS Fluent was employed to numerically evaluate the fluid dynamics of flexible foil under various wall distances. A virtual model of NACA0015 foil undergoing travelling wavy motion was adopted, and the research object included 2D and 3D models. To capture the foil's moving boundary, the dynamic grid technique coupled with the overlapping grid was utilized to realize the foil's positive deformation and passive forward motion. The ground wall effect on fluid dynamics (thrust force, lift force and propulsive efficiency) and the flow structures of travelling wavy foil were analyzed. The numerical results show that the existence of the ground wall is beneficial for the propulsive property of foil. Specifically, the existence of the wall can improve the forward speed and efficiency of foil, with a maximum increase of 13% in moving velocity and a 10.5% increase in propulsive efficiency. The conclusions acquired in the current study are of great significance for the design of bionic UUV.

AI-powered Smart Grids: Energy Optimization

This paper aims at explaining how AI can augment smart grids toward improved energy management. Mainly, the research is concerned with using artificial intelligence together in order to optimize energy distribution, minimize the cost of running organizations and finally establish means to harness sustainable energy resources. Four AI algorithms are utilized in this study: During the smart grid, load forecasting, fault detection, and energy optimization, ANN, SVM, GA, and RL approaches can be used. Based on the experimental findings, it is shown that the ANN achieved a 92% forecast precision; on the other hand, the utility of both the SVM and GA algorithms in energy optimization ranged from 18–23%. The RL algorithm returned the best result and cut wastage by 32% and enhanced load balance efficiency. These results demonstrate that AI strategy performs better than the conventional energy management systems, particularly for accommodating renewable generation and dynamic grid loads. By optimising the utilisation of the grid, and utilising artificial intelligence, energy losses are kept to an absolute minimum, thus supplementing sustainable efforts. The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures. In future work, issues of data security and scalability will be targeted in order to enhance the application of AI in smart grids.

Generalization of the Coleman-Mandula theorem and the ring paradigm

Abstract One of the biggest open problems in cosmology is to explain the accelerated expansion of the Universe in the last 5 billion years. There were attempts to solve it classically, but one could also use quantum gravity. We introduce a new approach to the quantization of gravity, called the ring paradigm, according to which graviton is a phonon on a dynamical grid. We obtain, after the application to cosmology, a new model of dark energy, inside which we solve the old problem of the cosmological constant. The only way how to conceptually verify the ring paradigm is to show that there exists a certain generalization of the Coleman-Mandula theorem.

Vehicle-to-Grid Power Transfer Method for Electric Vehicles using off-board charger

This article explores a power transfer technique from vehicle to grid (V2G) via the construction of an off-board charger for electric cars (EVs). The charger accommodates several charging modes, such as grid-to-vehicle (G2V), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G), facilitating efficient and adaptable energy management. In G2V mode, the charger utilizes grid power to recharge electric vehicle batteries, whilst V2V mode enables direct energy transfer between electric vehicles, circumventing the grid. The novel integration of G2V and V2V modes enables the concurrent use of grid electricity and energy from other electric vehicles, therefore diminishing grid reliance and enhancing power efficiency. The system has a three-phase pulse width modulation (PWM) rectifier that sustains a constant DC link voltage and attains a unity power factor on the grid side, therefore adhering to the IEEE 519 standard for total harmonic distortion (THD). Furthermore, a half-bridge bidirectional DC/DC converter guarantees consistent charging and discharging currents, hence improving the reliability and efficiency of the charging process. This holistic strategy enhances dynamic energy flow and grid stability while providing possible economic advantages to electric vehicle owners and operators via the integration of renewable energy sources and sophisticated management algorithms for improved energy use and storage.

Coordinated DSO-VPP operation framework with energy and reserve integrated from shared energy storage: A mixed game method

Virtual power plants (VPPs) contribute to the flexibility and economy of distributed system by leveraging integrated distributed renewable resources, optimizing energy production and consumption patterns, and facilitating dynamic grid management. However, challenges arise when multi-VPPs coordinated operate, including complex spatial and temporal correlation characteristics and conflicting interests in multi-agent decision-making. Shared energy storage (SES), as a product of the sharing economy, can be more flexible to help VPPs consume power generation from distributed renewable resources. Hence, focusing on the complementary problems and conflicts of interest between VPPs and improving the utilization of distributed renewable resources, this paper proposes an energy-reserve coordinated optimization model led by the distribution system operator (DSO) and involves the participation of both multi-VPPs and SES. Firstly, a Stackelberg-cooperative mixed game (SC-mixed game) framework is proposed for the DSO-VPP system, which leverages the DSO as the leader. The transactional electricity price between DSO and VPPs is optimized via the Stackelberg game, and the operation strategies for multi-VPPs can be calculated by the Cooperative game. Besides, an energy-reserve model is proposed for SES, which considers six reserve modes for further exploring the reserve potential of SES and guarantee the reserve provision ability. Additionally, a tailored alternating direction method of multipliers (ADMM), integrating a bisection method, is proposed to solve the SC-mixed game model efficiently. Finally, several case studies are conducted to validate the effectiveness of the proposed model.

Power Quality Enhancement in Grid-Connected Solar PV Systems Using Adaptive Control and Multilevel Inverter Topologies Under Dynamic Grid Conditions

This paper introduces a control strategy that uses an integrator-based positive sequence estimator to improve the power quality of a grid-connected double-stage solar PV system. The control extracts positive sequence components from distorted and unbalanced grid voltages to calculate unit templates and grid reference currents during grid anomalies like distortion, unbalance, sag/swell, and DC offset. A feedforward term is introduced to the photovoltaic array to ensure rapid transient response, and the DC-link voltage is adaptively regulated to reduce the voltage source converter (VSC) losses during weak grid conditions. The paper compares the performance of two-level and three-level inverters in this system configuration, highlighting the differences in power quality improvement, harmonic reduction, and system efficiency under various grid disturbances and intermittent solar conditions. Simulation results, performed in MATLAB, validate the superior performance of the three-level inverter in terms of lower total harmonic distortion (THD) in grid currents, in compliance with IEEE standard 519, while demonstrating the satisfactory response of both inverter types during challenging grid conditions like intermittent solar irradiation, DC offset, voltage distortions, unbalance, and voltage sag/swell.

Slope stability and failure dynamics of rainfall-induced landslide: Algorithm and applications

Rainfall-induced landslides severely threaten to engineering facilities and the safety of people’s lives. However, accurately capturing the complex interactions between hydrology and geomaterials remains challenging, so further research on the mechanism and failure dynamics of rainfall-induced landslides can provide effective support for engineering design and disaster prevention. Due to the insensitivity to mesh distortion, the material point method (MPM) can effectively simulate the large deformation process of geomaterials, but it still faces limitations in computational efficiency and in handling large-scale simulations. In this study, based on the GPU-accelerated MPM software CoSim-MPM, the dynamic grid algorithm (DGA) is developed to overcome the limitations of calculation scale. Then the rainfall hydrological process in slope unit and evolution of mechanical behaviors of the geomaterials are coupled to analyze the influence of rainfall on slope stability. The proposed method can simulate the hydrological process of surface runoff and subsurface infiltration in slope unit, and consider the interaction mechanism between hydrology and geotechnics. Taking a rainfall-induced landslide as an example, which is at a highway dumpsite in Yunnan, China, its stability under natural conditions and failure process under heavy rainfall are simulated. The results indicate that the slope remains stable under natural conditions but suffers failure and large deformation under heavy rainfall. The numerical results are corresponding well with field investigations, which demonstrates that the provided method can well be used to simulate the whole process of rainfall infiltration, moisture migration, stability, deformation and failure of slopes.

Systematic Review of Real-Time Data Analytics in the Energy Sector: Advancing Efficiency and Sustainability

This study systematically reviews the role of real-time analytics in enhancing efficiency and sustainability in the energy sector, focusing on its applications, challenges, and future opportunities. Leveraging advanced technologies like IoT, big data, AI, and machine learning, real-time analytics enables instantaneous data processing and actionable insights, transforming energy management. Key applications include predictive maintenance, dynamic grid optimization, and renewable energy integration, which collectively enhance operational efficiency and reduce energy waste. For example, real-time monitoring reduces transmission losses, balances energy supply and demand, and ensures the stability of renewable systems like solar and wind energy. Real-time analytics also supports sustainability goals by minimizing carbon emissions, optimizing fossil fuel efficiency, and empowering consumers through smart meters to adopt energy-efficient behaviors. However, the review identifies significant challenges, including high implementation costs, data security risks, and a lack of skilled professionals, which hinder widespread adoption, particularly in developing regions. The review emphasizes the need for investments, workforce training, and secure data systems to address these barriers. Emerging technologies such as blockchain, edge AI, and quantum computing present promising opportunities to overcome current limitations. Blockchain facilitates secure, decentralized energy transactions, while edge AI enhances scalability and reduces latency in distributed systems. Quantum computing, with its unparalleled processing power, could revolutionize energy.

Study of Pathfinding Approach Based on A* With Adaptive Occupancy Grid

This paper presents the results of a study on the A* search algorithm applied to a two-dimensional map with obstacles. Since, in typical cases, A* is implemented on a map divided into cells of equal size, a scientific interest has lies in investigating the efficiency of this algorithm on a map with dynamically variable cell size. Such a map representation increases the ‘resolution’ of constructing a better trajectory near obstacles. For this purpose, the paper proposes an approach to representing the search space as a dynamic adaptive grid using a QuadTree structure. Additionally, a modification of the A* algorithm has been proposed and investigated, which involves selecting the best cell in the neighborhood of the agent’s current position and performing pathfinding from a starting point to a goal. The paper considers maps of various sizes and complexities for numerical experiments and compares the classical and modified A* algorithms. It has been shown that the proposed modification of the A* algorithm demonstrates better computational properties than the classical version of the algorithm on an adaptive grid.

Optimizing grid-dependent and islanded network operations through synergic active-reactive power integration

DG integration improves distribution network performance and also enables microgrid (MG) formation for islanded operation. However, existing studies assume an isolated operation approach for islanded mode, which is unrealistic and increases overall system costs while adding complexity to the grid. This paper presents a dual-stage methodology for the efficient operation of distributed generation (DG) and shunt capacitor banks (SCBs) in radial distribution networks (RDNs) for both grid-integrated and islanded modes. The first stage proposes an improved Jaya algorithm (IJaya) to optimize DG and SCB allocation during grid-connected operation, aiming to reduce power loss and improve voltage profiles. IJaya employs a dynamic weight-based grid search to address premature convergence. The second stage formulates a multiobjective function for the islanded operation to minimize power loss and underutilization of DG-SCB capacity. Case studies on IEEE 33- and 69-bus RDNs show IJaya reduces power loss by 38.84 % and improves voltage by 3.26 % in grid-connected networks compared to existing methods. The proposed analytical approach for islanded operation tunes the source power factor, reducing DG-SCB underutilization by up to 15.83 % for power factors between 0.8 and 0.93. The results demonstrate that operating distributed resources near optimal capacities meets a larger portion of the island network's energy needs.

Dynamic adaptive and fully unstructured tetrahedral gridding: Application to CO2 sequestration with consideration of full fluid compressibility

Numerical simulations of complex subsurface flow problems and geomechanics will advance enormously by dynamic adaptive gridding. Only a small part of a large domain where sharp changes occur may require fine gridding. In this work, we introduce a methodology to carry out dynamic adaptive gridding for large-scale flow problems. The algorithm allows 2D and 3D unstructured gridding with consideration of full fluid compressibility in single-phase, and two-phase compositional flow. We divide a triangular element into four and a tetrahedron element into eight which creates hanging nodes at each level of refinement. The handing nodes are eliminated by splitting extra elements. As a result, a transition region exists between the fine and coarse grid regions of the domain. We have applied the method to CO2 sequestration in subsurface aquifers. The conditions are selected such that gravity fingers develop from density increase by the dissolution of CO2 in the aqueous phase. The selected examples include large domains where neither systematic studies nor dynamic adaptive gridding have been reported in the past. Results from comparison with uniform gridding reveal a speedup of up to three orders of magnitude in 3D.

Single- and Multinozzle Plume Impingement to Detumble Space Debris

The management of space debris is increasingly crucial for ensuring the safety of space missions by mitigating collision risks. Most proposed debris management systems involve the capture and deorbiting of debris. However, it is also essential to eliminate any residual rotation and nutation of the debris to perform capture without accidents. The current work examines the effectiveness of thruster plume impingement for despinning space debris. A proof-of-concept study of plume impingement dynamics is conducted on a rectangular prism target using single- and multiple-nozzle arrangements for different target offsets and orientations. The three-dimensional simulation is carried out using the open-source Direct Simulation Monte Carlo code SPARTA. Staging and dynamic grid adaptation techniques are incorporated to obtain high-resolution results for the multiscale problem. It is observed that the plume–plume interaction in the multinozzle configuration leads to the beneficial formation of a relatively narrow beam of flow interacting with the target; that is, a multinozzle plume exerts a more concentrated directed force, which imparts greater countertorque across a broader spectrum of target orientations than a single-nozzle plume.

A short-term vehicle speed prediction approach considering dynamic traffic scene

Vehicle speed serves as a crucial indicator in traffic flow efficiency and safety evaluation. Previous researches typically utilize the time-series data of target vehicle speed or the relative speed and distance between the target and leading vehicles as speed prediction model input, which are suitable for stable and single-vehicle scenes. However, the surrounding traffic scenes dynamically changes during the vehicle’s driving process, the driver needs to allocate attention to the key scene vehicles in the surrounding traffic scenes and adjust the current speed based on these vehicles’ behavior. Therefore, this paper proposes a short-time vehicle speed prediction model considering the dynamic traffic scenes. A dynamic grid method is adopted to divide the target vehicle’s surrounding areas into left front, front, and right front regions. And key scene vehicles are identified based on each region’s state. Then the relative distance, speed, and acceleration data between the target vehicle and key scene vehicles are selected as model inputs. Vehicle trajectory data from the South-North No. 1 Expressway in Hefei and Ranger optimized Temporal Convolutional Network (TCN) are utilized to train the speed prediction model. Experiment results indicated that under 1 s, 2 s, and 3 s prediction lengths, the model’s average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.430 and 0.271 respectively. Compared to the speed prediction model considering the static traffic scenes, the average RMSE and MAE for the three prediction lengths have been reduced by 49.15 % and 56.02 % respectively.

Dynamic characteristics analysis of shock absorber based on fluid simulation

Pneumatic spring dampers are extensively utilized within hydraulic systems, and the flow characteristics of the internal oil play a crucial role in determining noise and vibration levels. To validate the mechanical structure's reliability, the shock absorber was simplified and the fluid domain was extracted using a CFD method. The velocity field and pressure field under different conditions were then simulated and analyzed. A finite element model of the flow field was established, and its accuracy was verified by comparing simulation results of gas side pressure with experimental results. According to the working principle of the oil-gas spring, dynamic active surfaces in contact with the main piston and dynamic driven surfaces in contact with the floating piston were defined, determining moving conditions for dynamic grids. The results indicate that as the diameter of flow channels increases, there is a decrease in average pressure drop within the oil chamber (i.e., pressure loss within the flow field). Additionally, as bend angle increases, average pressure drop decreases. However, optimization effects become less significant beyond a certain bend angle.

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

Unsteady numerical simulations of aircraft icing based on the dynamic grid method

In this study, an unsteady numerical simulation method based on dynamic grids was established to solve unsteady aircraft icing processes. The dual-time step method was used to achieve the time advancement of the transient airflow and droplets fields. The droplets trajectory was calculated using the Euler method, which incorporates a supercooled large droplets model. The transient evolution of the water film and ice layer on the substrate surface was considered in the icing thermodynamic model. The accuracy of the unsteady method for ice-shape prediction was verified via comparison and analysis of numerical examples. Additionally, the rime and glaze ice formation processes were explored, and the coupling effects of ice horn growth and the air convective heat transfer coefficient, water collection coefficient, and water film flow on the substrate surface were studied. The ice shapes calculated by unsteady and quasi-steady methods were compared. The results indicated that the latter predicts a larger icing range than the former. Moreover, there may be more obvious distinctions between these two methods in predicting glaze ice.