Virtual Aggregation Research Articles

Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China

The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

Algorithms to create realistic virtual asphalt mixtures

This paper presents a set of algorithms for creating realistic three-dimensional (3D) models of asphalt mixtures based on its composition. The process begins by measuring the shape of the aggregates in the asphalt mixture. Using these measurements, an algorithm creates 3D volumes that approximate the size and shape of the aggregates. These volumes are then added to a virtual mixer in the same proportions as the aggregates in the real mixture and are pressed together until the air void content of the 3D model matches that of the real asphalt. This results in realistic models of aggregate skeletons in the asphalt mixture. Next, sections are taken from the aggregate skeletons and a mortar mixture, made up of bitumen, filler and fine aggregates, is applied to the sections. Geometrical rules are used to determine which areas of the sections are covered in mortar and which are air voids. The validation of virtual aggregates and air voids has demonstrated good correlations with actual ones. The resulting models are highly realistic and can be used to predict the mechanical or physical properties of asphalt mixtures in future studies.

Research on a Resource Modeling and Power Prediction Method Based on Virtual Aggregation

Distributed resources at a grid’s end cannot upload operational power data to local centers due to data transmission and privacy issues. This leaves the centers with incomplete information, thus impacting decision making. This paper presents a virtual aggregation-based model for such scenarios. We define four virtual aggregate types based on resource response characteristics. Using characteristic coefficients, we identify these aggregates’ categories and proportions from bus power. To address blind source separation in single-channel power signals, we apply the Ensemble Empirical Mode Decomposition-Fast Independent Component Analysis (EEMD-FastICA) method. This helps extract and analyze bus power, thereby deriving power curves for different aggregates. Moreover, we use a graph convolutional network to explore how factors like date, time, weather, and pricing intertwine with aggregate power. We develop a predictive model with an advanced SpatioTemporal Graph Convolutional Network (STGCN), thus facilitating proactive power forecasting for virtual aggregates. Case studies show our method’s efficacy in extracting power curves under limited information, with the STGCN ensuring accurate, forward-looking predictions.

Characterizing force-chain network in aggregate blend using discrete element method and complex network theory

The force chain network in aggregate blend indicates load transfer path and is the main carrier to resist external loads. The objective of this study is to analyze the force chain network characteristics of aggregate blend on the basis of complex network theory. The virtual aggregate blend models with three stone matrix asphalt (SMA) gradations and three dense asphalt concrete (AC) gradations were constructed using discrete element method (DEM). The force chain network topology structure of each aggregate blend was established according to the aggregate contact parameters extracted from DEM software and the introduced complex network theory. The characteristics of each force chain network were analyzed by the various network evaluation indicators. The results showed that there are two or more contacts between most particle pairs. The average coordination number and the average contact number of aggregate blend are not equal. The degree distribution of each gradation shows a good power-law distribution. The force chain network of aggregate blend is a scale-free network. Big particles have greater average strength and smaller average weight distribution difference compared with small particles. The reference lower limit has better clustering performance for SMA-16, and the reference upper limit has better clustering performance for AC-25. The average path length of SMA-16 is longer than that of AC-25, indicating that SMA-16 has better load transfer ability and skeleton properties.

Optimized operation of distributed energy resources: The opportunities of value stacking for Power-to-Gas aggregated with PV

This analysis investigates the business case of a virtually aggregated unit with PV and Power-to-Gas, outlining the added value of enhanced operation modes for the deepened market integration of distributed energy resources in an aggregated form. Based on empirical generation and market data, the presented analysis outlines the added benefit of the so-called value stacking that considers the exploitation of short-term arbitrage opportunities, the provision of secondary and tertiary frequency reserve, and active imbalance management to balance forecast errors. A multi-stage and multi-period optimization approach is presented to generate an aggregated bidding strategy on multiple energy and ancillary service markets. On a case study basis with hourly resolution, annual energy flows and financial outcomes are derived for the modelled plant. Overall, nine different operating modes with different levels of market integration of the aggregated unit are analysed. While static baseline operation results barely profitable, proper integration into energy markets raises the annual cash flow from operating activities to around 60 k€ per aggregated MW. This six-fold increase is accompanied by a much more price-specific dispatch with the equivalent full-load hours of controllable output effectively dropping to about one-third. Integrating the aggregated unit further into balancing markets and performing active imbalance management leverages the freed-up capacity margin and further increases the operational results up to 150 k€ per MW. The provided empirical insights from the case study are beneficial for both practitioners in the energy sector that want to evaluate the potential value of virtual aggregation with enhanced operation and policymakers that consider further regulatory amendments to open markets and enable further integration of new energy sources.

Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens

Even though Industrial Kitchens (IKs) are among the highest energy intensity spaces, very little work has been done to forecast their consumption. This work explores the possibility of increasing the accuracy of the consumption forecast in an IK by forecasting disaggregated appliance consumption and comparing these results with the forecast of the total consumption of these appliances (Virtual Aggregate—VA). To do so, three different methods are used: the statistical method (Prophet), classic Machine Learning (ML) method such as random forest (RF), and deep learning (DL) method, namely long short-term memory (LSTM). This work uses individual appliance electricity consumption data collected from a Portuguese restaurant over a period of four consecutive weeks. The obtained results suggest that Prophet and RF are the more viable options. The former achieved the best performance in aggregated data, whereas the latter showed better forecasting results for most of the individual loads. Regarding the performance of the VA against the sum of individual appliance forecasts, all models perform better in the former. However, the very small difference across the results shows that this is a viable alternative to forecast aggregated consumption when only individual appliance consumption data are available.

Evaluation of asphalt mixture micromechanical behavior evolution in the failure process based on Discrete Element Method

Asphalt mixture was a granular material, and traditional mechanical testing methods were difficult to reveal the failure mechanism. To evaluate the micromechanical behavior of asphalt mixture in the failure process, the DEM (Discrete Element Method) model of the uniaxial compression test was established. The virtual aggregate particle database with real morphology properties was constructed, and the linear parallel bond model was used to simulate the adhesion of asphalt. In the DEM model of the asphalt mixture failure process, the movement of asphalt mortar and coarse aggregate was evaluated, and the Discrete Fracture Network (DFN) was proposed to represent the microcrack. Finally, the three-dimensional spherical coordinate histogram was used to analyze the evolution of magnitude and direction of the coarse aggregate skeleton contact force. It found that the movement of coarse aggregate was less than that of asphalt mortar in the process of the uniaxial compression test, and the coarse aggregate movement was affected by its particle size and location. The result also found that although the adhesive and cohesion failure could occur at the same time, the adhesive failure was less than cohesion failure in the early stage of the uniaxial compression test, and the aggregate with larger particle size could reduce the generation of adhesive failure. Compared with the magnitude of coarse aggregate skeleton contact force, the change of direction was the main reason for a decrease in stress when the asphalt mixture specimen exceeded the peak strength.

3D mesostructure generation of fully-graded concrete based on hierarchical point cloud and aggregate coarsening

• A novel method for generating fully-graded concrete mesostructures is presented. • Aggregate coarsening is performed to improve the achievable volume fraction. • 3D mesostructures of practical fully-graded dam concrete are generated. • The damage and cracking behavior of fully-graded concrete in tension is studied. The fully-graded concrete (FGC) is widely used in hydraulic mass concrete structures. An exquisite mesoscale model for FGC is essential for accurately simulating its complex damage and cracking behavior. However, due to the presence of massive aggregates and the requirement of high aggregate volume fraction, it is still challenging to generate the three-dimensional (3D) mesostructure of FGC close to reality. A novel and efficient method, termed the hierarchical point cloud method, is proposed for generating the 3D FGC mesostructures. Innovatively, a hierarchical four-level point cloud with a specific spatial structure is defined within the designated domain and serves as the basis for efficiently placing the four-graded aggregates in FGC. The aggregate coarsening is adopted as a supplementary way to achieve the target aggregate packing density in high volume fraction case. In addition, the four virtual aggregate storage piles containing sufficient aggregates within different grading segments are pre-established for providing the required four-graded aggregates. The method efficiency is evaluated by comparison with two traditional methods. The standard samples of mesostructure with an aggregate volume fraction of up to 63.63% are generated for the practical FGC used in Jinping-I arch dam. The numerical tension tests are further carried out for gaining insights into the damage and cracking behavior of FGC. The results show that the arrangement of relative large aggregates, especially the extra-large aggregates, dominates the localization of damage and the final crack distribution.

Energy storage with Power-to-Power systems relying on photovoltaic and hydrogen: modelling the operation with secondary reserve provision

Energy storage is increasingly required in order to cope with the fluctuations of renewable energy sources, especially in power generation. In many countries, the electric market is undergoing regulatory transformations that aim at increasing the type and number of technologies that can provide grid services, either alone or as virtual aggregates. In this context, this work investigates the economic profitability of Secondary Frequency Reserve (SFR) provision to the electric grid through integrated generation-and-storage systems. In particular, the case of Italy is assessed, where recent projects opened the Ancillary Service Market (ASM) to relevant generation units or aggregates of units, including programmable and non-programmable renewable sources, consumption units, and energy storage systems. The operation of a hydrogen-based Power-to-Power (P2P) system, designed to supply an electric load with locally generated electricity from a solar PV field, is studied when acting on both the Day-Ahead Market (DAM) and the ASM. A mixed-integer linear optimization problem is set up that minimizes the annual average cost of electricity to the load. The analysis considers a broad range of options in terms of electricity prices on the DAM and price-quantity combinations of accepted offers on the ASM. Results show that the economic advantages deriving from the utilization of P2P systems increase with more fluctuating prices. The provision of SFR services with an optimal bidding strategy and asymmetric bids allows one to reduce the average cost of electricity up to about 15 % with respect to the operation on the DAM only.

A Distributed Collaborative Entrance Defense Framework Against DDoS Attacks on Satellite Internet

Satellite Internet (SI) dramatically expanded the ground-based Internet, and it is also the future direction of 6G. However, due to limited computing power and bandwidth resources, Distributed Denial-of-Service (DDoS) attacks can cause more severe damage to SI, and even paralysis of the entire network. Current DDoS defense mechanisms are built on abundant computing power and bandwidth resources, making applying in the SI scenario challenging. Aiming at protecting SI from DDoS attacks, a blockchain-based distributed collaborative entrance defense (DCED) framework is proposed, in which network traffic characteristics can be recorded and aggregated at the entrances of SI. The proposed framework consists of a distributed detection digesting procedure, a digest virtual aggregation procedure, and an entrance control strategy. The former procedure detects and extracts multidimensional characteristics of DDoS attacks and pushes them onto the blockchain. The latter procedure collects block data and aggregates attack features using the MapReduce algorithm and then compares them with baseline and gives an alert. The strategy completes the filtering and interception of traffic. Experiments use the IXIA platform to generate malicious traffic, and results show that the framework can accurately identify attack traffic within 1500 ms, reaching an area of 0.99 under the receiver operating characteristic curve. The proposed framework is more effective than other similar DDoS methods, protecting the precious SI bandwidth resources.

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

Evaluation of coarse aggregate movement and contact unbalanced force during asphalt mixture compaction process based on discrete element method

To evaluate the stability of asphalt mixture aggregate network and the mechanism of coarse aggregate movement of the asphalt mixture during the compaction process, the Marshall impact compaction (MIC) process of asphalt mixture was simulated through Discrete Element Method (DEM) and the new index of the coarse aggregate particles contact unbalance force was proposed. Firstly, the mechanical parameters of the asphalt mortar at the compaction temperature were calculated based on the time–temperature superposition principle and the Dynamic Mechanical Analysis (DMA) tests. Secondly, the aggregate morphology properties were collected with a 3D blue-ray scanner, and the virtual aggregate database and the DEM model of asphalt mixture during MIC process with aggregate morphology properties were constructed. Thirdly, the volumetrics evolution information of the asphalt mixture specimen was obtained, and the coarse aggregates movement and contact unbalanced force were evaluated. The results indicated that the vertical displacement of coarse aggregate particles of asphalt mixture was larger than horizontal displacement in the MIC process. The vertical displacement increased with the increment of the aggregate particles size, the reason was that the contact number and the vertical unbalance force increased with the increment of the aggregate particles size. The horizontal displacement and rotation angle of coarse aggregate shown similar trend during MIC process, both of them increased greatly during the initial compaction process and tended to remain stable after that, and the horizontal displacement and rotation angle increased with the decrement of the aggregate particles size. It also found that the particles sizes have an impact on the unbalance force distribution of coarse aggregate, and the unbalance force of coarse aggregate decreased with the increment of blows which indicated that the asphalt mixture tends to become stable with the effects of compaction. And the result also indicated that the aggregate location has an impact on the aggregate movement displacement and aggregate skeleton stability.

A Methodology for Exploiting Smart Prosumers’ Flexibility in a Bottom-Up Aggregation Process

The electrical power system is evolving in a way that requires new measures for ensuring its secure and reliable operation. Demand-side aggregation represents one of the more interesting ways to provide ancillary services by the coordinated management of a multitude of different distributed resources. In this framework, aggregators play the main role in ensuring the effectiveness of the coordinated action of the distributed resources, usually becoming mediators in the relation between distribution system operators and smart prosumers. The research project DEMAND recently introduced a new concept in demand-side aggregation by proposing a scheme without a central aggregator where prosumers can share and combine their flexibility with a collaboration–competition mechanism in a platform called Virtual Aggregation Environment (VAE). This paper, after a brief introduction to the DEMAND project, presents the algorithm for the day-ahead estimation of prosumers’ flexibility and the cooperative–competitive algorithm for the bottom-up aggregation. The first algorithm evaluates various couples of power variation and desired remuneration to be sent to the VAE for further elaborations and, for showing its potentiality, is applied to two different case studies: a passive user with only controllable loads and prosumers with controllable loads, photovoltaics and a storage system. The aggregation algorithm is instead discussed in detail, and its performance is evaluated for different population sizes.

Use of a physics toolbox to simulate aggregate packings

In this paper, an impulse-based Discrete Element numerical Method (iDEM) included in a physics toolbox, has been used to compact virtual aggregates. Firstly, geometrical properties, such as area, aspect ratio, perimeter, minor and major feret, circularity and roundness, of eleven types of coarse aggregates were measured. Then, a mass of each of these aggregates was compacted under vibration. The aggregate packings’ properties, such as aggregate segregation and orientation, porosity, pore -diameter, -tortuosity, -connectivity, -aspect ratio, -circularity, and -vertical distribution, were measured from Computed Tomography scans. Secondly, the aggregates were simulated using a Perlin noise in spherical primitives, which adjusted their geometry until they achieved realistic morphologies and gradations. iDEM detects contacts between complex shapes, including concavities, and computes the interaction between large amounts of complex objects. Results show that the properties from the packing experiments and simulations are highly comparable. This paper demonstrates the capacity of the physics toolbox to simulate granular materials effectively.

Calculation Method for Grading Size and Grading Bounding Box of Virtual Aggregate Based on DEM

The grading size, which can be defined as the side length of the smallest square hole through which an aggregate with irregular shape can directly pass, is an important morphology parameter and can be used to calculate the gradation of mixture material. The grading bounding box, which can be defined as the circumscribed cuboid with central axis length being the grading size, is an important visual tool for observing the size and direction of the aggregate. Virtual test to calculate the grading size of a virtual aggregate is environmentally friendly and efficient, but the result provided by current research is imprecise and the grading bounding box is also rarely mentioned. In this paper, the multilevel complete projection algorithm is proposed to precisely calculate the grading size of a virtual aggregate. The whole process of the algorithm can be expressed by formula after the operation of sphere discretization by converting the virtual aggregate shell into the discrete aggregate. Then, the discrete aggregate is projected onto a complete series of the plane to form several 2D figures, and then, each 2D figure is projected onto a complete series of the orthogonal axis to form orthogonal segments. The grading size can finally be obtained by comparing the length of the above orthogonal segments based on the key central axis length principle. The influencing factors of computational accuracy and efficiency are considered in the algorithm. Finally, the grading bounding box can be built by using the Rodrigues transformation according to the information obtained from the above algorithm.

The spherical primitive and perlin noise method to recreate realistic aggregate shapes

An algorithm to re-create virtual aggregates with realistic shapes is presented in this paper. The algorithm has been implemented in the Unity 3D platform. The idea is to re-create realistically the virtual coarse and crushed aggregates that are normally used as a material for the construction of roads. This method consists of two major procedures: (i) to combine a spherical density function with a noise matrix based on the Perlin noise to obtain shapes of appropriate angularity and, (ii) deform the shapes until their minor ferret, aspect ratio and, thickness are equivalent to those wanted. The efficiency of the algorithm has been tested by reproducing nine types of aggregates from different sources. The results obtained indicate that the method proposed can be used to realistically re-create in 3D coarse aggregates.Graphic abstract

In-silico manufacturing of asphalt concrete

In this paper we present an algorithm to produce realistic virtual asphalt concretes. The inputs are the geometry of aggregates, and the composition of the asphalt concrete. The outputs are the air voids content and, a 3-dimensional representation of the aggregate skeleton. The air voids that are predicted by the algorithm have been compared to those measured from asphalt made in the laboratory. The key parameters that influence the compaction of an asphalt concrete are the shape of the aggregates, number of aggregates and, volume of mastic, i.e. bitumen, filler, and aggregates smaller than 2 mm. An equation based on a Weibull function, with independent variables the number of aggregates and force required to compact the virtual aggregates, has been developed. This equation can predict the force that the algorithm needs to compact the virtual aggregates to a level equivalent to that reached by a laboratory compactor.

Generation of virtual asphalt concrete in a physics engine

The precise, quick and efficient simulation of asphalt concrete is an important step to select asphalt mixtures for a range of applications. A method to create virtual aggregates based on aggregates' topological properties and aggregate skeletons like those of asphalt concrete using a physics engine is proposed. It can produce hundreds of virtual aggregates with shape properties equivalent to these of real ones in a matter of seconds, pack them to a level of compaction like that of the Marshall compactor, and measure properties such as the expected air voids content and number of contacts between the aggregates. In the paper, only aggregates bigger than 2 mm have been considered due to computing efficiency. Besides, it was found that the main factors influencing asphalt compaction are the number of aggregates in the mixture and the amount of dust, or aggregates smaller than 2 mm, which correspond to the parts of the solid skeleton that have not been simulated.

Effectual Computing Enhanced Aggregator Node B for 5G Vehicular Milieu

In the existing environment, 30% of the base station has to handle the 70% of the network load. For a fact, the installation of additional base radio stations increases the deployment costs. The Computing Improved Aggregator Node B (CIANB) has been introduced for the 5G vehicular environment to fix this issue. The CIANB is the base radio station which consists of virtual cloud, storage space, and Aggregator Node B (ANB) pool. This paper discusses the forthcoming infrastructure, such as CIANB, which is progressing towards its innovation in the 5G vehicular environment. The CIANB and virtual ANB instance have been introduced in this paper to reduce congestion and to maximize the utilization of services without interruption. The evaluation metrics justify that the forthcoming infrastructure, CIANB, and virtual ANB greatly enhances the efficiency of the 5G vehicular environment.