Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment

Forested areas are extremely vulnerable to disasters leading to environmental destruction.Forest Fire is one among them which requires immediate attention.There are lot of works done by authors where Wireless Sensors and IoT have been used for forest fire monitoring.So,towards monitoring the forest fire and managing the energy efficiently in IoT,Energy Efficient Routing Protocol for Low power lossy networks(E-RPL)was developed.There were challenges about the scalability of the network resulting in a large end-to-end delay and less packet delivery which led to the development of Aggregator-based Energy Efficient RPL with Data Compression(CAAERPL).Though CAA-ERPL proved effective in terms of reduced packet delivery,less energy consumption,and increased packet delivery ratio for varying number of nodes,there is still challenge in the selection of aggregator which is based purely on probability percentage of nodes.There has been research work where fuzzy logic been employed for Mobile Ad-hoc Routing,RPL routing and cluster head selection in Wireless Sensor.There has been no work where fuzzy logic is employed for aggregator selection in Energy Efficient RPL.So accordingly,we here have proposed Fuzzy Based Aggregator selection in Energy-efficient RPL for region thereby forming DODAG for communicating to Fog/Edge.We here have developed fuzzy inference rules for selecting the aggregator based on strength which takes residual power,Node degree,and Expected Transmission Count(ETX)as input metrics.The Fuzzy Aggregator Energy Efficient RPL(FA-ERPL)based on fuzzy inference rules were analysed against E-RPL in terms of scalability(First and Half Node die),Energy Consumption,and aggregator node energy deviation.From the analysis,it was found that FA-ERPL performed better than E-RPL.These were simulated using MATLAB and results.

How Load Aggregators Avoid Risks in Spot Electricity Market:In the Framework of Power Consumption Right Option Contracts

There is uncertainty in the electricity price of spot electricity market,which makes load aggregators undertake price risks for their agent users.In order to allow load aggregators to reduce the spot market price risk,scholars have proposed many solutions,such as improving the declaration decision-making model,signing power mutual insurance contracts,and adding energy storage and mobilizing demand-side resources to respond.In terms of demand side,calling flexible demand-side resources can be considered as a key solution.The user’s power consumption rights(PCRs)are core contents of the demand-side resources.However,there have been few studies on the pricing of PCR contracts and transaction decisions to solve the problem of price forecast deviation and to manage the uncertainty of spot market prices.In addition,in traditional PCR contracts,PCRs are mostly priced using a single price mechanism,that is,the power user is compensated for part of the electricity that was interrupted or reduced in power supply.However,some power users might engage in speculative behaviours under this mechanism.Further,for load aggregators,their price risk avoidance ability has not substantially improved.As a financial derivative,options can solve the above problems.In this article,firstly,the option method is used to build an option pricing optimization model for power consumption right contracts that can calculate the optimal option premium and strike price of option contracts of power consumption rights.Secondly,from the perspective of power users and load aggregators,a simulation model of power consumption right transaction decision-making is constructed.The results of calculation examples show that(1)Under the model in this article,the pricing of option contracts for power consumption rights with better risk aversion capabilities than traditional compensation contracts can be obtained.(2)The decision to sell or purchase the power consumption rights will converge at respective highvalue periods,and option contracts will expedite the process.(3)Option contracts can significantly reduce the loss caused by the uncertainty of spot electricity prices for load aggregators without reducing users’willingness to sell power consumption rights.

Micro-Grid Planning with Aggregator’s Role in the Renewable Inclusive Prosumer Market

The paper proposes a model for a micro-grid architecture incorporating the role of aggregators and renewable sources on the prosumer side, working together to optimize configurations and operations. The final model takes the form of a mixed-integer linear programming model. This model is solved using the CPLEX solver via GAMS by having a consistent data set.

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggrega-tor considering uncertainties.The aggregator,which integrates power and capacity of small-scale prosumers and flex-ible community-owned devices,trades electric energy in the day-ahead(DAM)and real-time energy markets(RTM),and trades reserve capacity and deployment in the reserve capacity(RCM)and reserve deployment markets(RDM).The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules,including minimum offer/bid size and minimum delivery duration.A combination approach of stochastic program-ming(SP)and robust optimization(RO)is used to model different kinds of uncertainties,including those of market price,power/demand and reserve deployment.The risk management of the aggregator is considered through con-ditional value at risk(CVaR)and fluctuation intervals of the uncertain parameters.Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets,reserve regulations,and risk preferences.

Three-stage day-ahead scheduling strategy for regional thermostatically controlled load aggregators

Thermostatically controlled loads(TCLs)are regarded as having potential to participate in power grid regulation.This paper proposes a scheduling strategy with three-stage optimization for regional aggregators jointly participating in day-ahead scheduling to support demand response.The first stage is on the profit of aggregators and peak load of the grid.The line loss and voltage deviation of regulation are considered to ensure stable operation of the power grid at the second stage,which guarantees the fairness of the regulation and the comfort of users.A single tempera-ture adjustment strategy is used to control TCLs to maximize the response potential in the third stage.Finally,digital simulation based on the IEEE 33-bus distribution network system proves that the proposed three-stage scheduling strategy can keep the voltage deviation within±5%in different situations.In addition,the Gini coefficient of distribu-tion increases by 20%and the predicted percentage of dissatisfied is 48%lower than those without distribution.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.

Aggregator Service for PV and Battery Energy Storage Systems of Residential Building

Distributed energy resources(DERs),including photovoltaic(PV)systems,small wind turbines,and energy storage systems(ESSs)are being increasingly installed in many residential units and the industry sector at large.DER installations in apartment buildings,however,pose a more complex issue particularly in the context of property ownership and the distribution of DR benefits.In this paper,a novel aggregator service is proposed to provide centralized management services for residents and DER asset owners in apartment buildings.The proposed service consists of a business model for billing and benefits distribution,and a model predictive control(MPC)control algorithm for managing and optimizing DER operations.Both physical and communication structures are proposed to ensure the implementation of such aggregator services for buildings.Three billing tariffs,i.e.,flat rate,time-of-use(TOU),and real time pricing(RTP)are compared by way of case studies.The results indicate that the proposed aggregator service is compatible with the business model.It is shown to offer good performance in load shifting,bill savings,and energy trading of DERs.Overall,the aggregator service is expected to provide benefits in reducing the pay back periods of the investment.

Risk assessment of microgrid aggregators considering demand response and uncertain renewable energy sources

In power market environment,the growing importance of demand response(DR)and renewable energy source(RES)attracts more for-profit DR and RES aggregators to compete with each other to maximize their profit.Meanwhile,the intermittent natures of these alternative sources along with the competition add to the probable financial risk of the aggregators.The objective of the paper is to highlight this financial risk of aggregators in such uncertain environment while estimating DR magnitude and power generated by RES.This work develops DR modeling incorporating the effect of estimating power at different confidence levels and uncertain participation of customers.In this paper,two well-known risk assessment techniques,value at risk and conditional value at risk,are applied to predict the power from RES and DR programs at a particular level of risk in different scenarios generated by Monte Carlo method.To establish the linkage between financial risk taking ability of individuals,the aggregators are classified into risk neutral aggregator,risk averse aggregator and risk taking aggregator.The paper uses data from Indian Energy Exchange to produce realistic results and refers certain policies of Indian Energy Exchange to frame mathematical expressions for benefit function considering uncertainties for each type of three aggregators.Extensive results show the importance of assessing the risks involved with two unpredictable variables and possible impacts on technical and financial attributes of the microgrid energy market.

Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging

The increase in global electricity consumption has made energy efficiency a priority for governments.Consequently,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)into energy markets.This study presents an assessment of various strategies for EV aggregators.In this analysis,the smart charging methodology proposed in a previous study is considered.The smart charging technique employs charging power rate modulation and considers user preferences.To adopt several strategies,this study simulates the effect of these actions in a case study of a distribution system from the city of Quito,Ecuador.Different actions are simulated,and the EV aggregator costs and technical conditions are evaluated.

Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

Demand response(DR)has received much attention for its ability to balance the changing power supply and demand with flexibility.DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets.In this work,a DR operation framework is presented to enable local management of customers to participate in electricity market.A novel optimization model is proposed for the DR aggregator with multiple objectives.On one hand,it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit.On the other hand,the customers’welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control.The consumer psychology is introduced to model the consumers’behavior during contract signing.Several simulation studies are performed to demonstrate the feasibility of the proposed model.The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers’welfare is considered.

Integration of Utility Distributed Energy Resource Management System and Aggregators for Evolving Distribution System Operators

With the rapid integration of distributed energy resources(DERs),distribution utilities are faced with new and unprecedented issues.New challenges introduced by high penetra-tion of DERs range from poor observability to overload and reverse power flow problems,under-/over-voltages,maloperation of legacy protection systems,and requirements for new planning procedures.Distribution utility personnel are not adequately trained,and legacy control centers are not properly equipped to cope with these issues.Fortunately,distribution energy resource management systems(DERMSs)are emerging software technologies aimed to provide distribution system operators(DSOs)with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources.However,as DERMS technology is still emerging,its definition is vague and can refer to very different levels of software hierarchies,spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems(called utility DERMS).Although they are all frequently simply called DERIMS,these software technologies have different sets of tools and aim to provide different services to different stakeholders.This paper explores how these different software technologies can complement each other,and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.

Impact of Electric Vehicle Aggregator with Communication Time Delay on Stability Regions and Stability Delay Margins in Load Frequency Control System

This paper investigates the impact of electric vehicle(EV)aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control(LFC)system.Primarily,a graphical method characterizing stability boundary locus is implemented.For a given time delay,the method computes all the stabilizing proportional-integral(PI)controller gains,which constitutes a stability region in the parameter space of PI controller.Secondly,in order to complement the stability regions,a frequency-domain exact method is used to calculate stability delay margins for various values of PI controller gains.The qualitative impact of EV aggregator on both stability regions and stability delay margins is thoroughly analyzed and the results are authenticated by time-domain simulations and quasi-polynomial mapping-based root finder(QPmR)algorithm.

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

Small molecules to target tau amyloid aggregation

Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.

Mechanical Behavior Based on Aggregates Microstructure of Ultra-high Performance Concrete

We developed ultra-high performance concrete(UHPC)incorporating mullite sand and brown corundum sand(BCS),and the quartz sand UHPC was utilized to prepare for comparison.The properties of compressive strength,elastic modulus,ultrasonic pulse velocity,flexural strength,and toughness were investigated.Scanning electron microscopy and nanoindentation were also conducted to reveal the underlying mechanisms affecting macroscopic performance.Due to the superior interface bonding properties between mullite sand and matrix,the compressive strength and flexural toughness of UHPC have been significantly improved.Mullite sand and BCS aggregates have higher stiffness than quartz sand,contributing to the excellent elastic modulus exhibited by UHPC.The stiffness and volume of aggregates have a more significant impact on the elastic modulus of UHPC than interface performance,and the latter contributes more to the strength of UHPC.This study will provide a reference for developing UHPC with superior elastic modulus for structural engineering.

Case Studies of the Microphysical and Kinematic Structure of Summer Mesoscale Precipitation Clouds over the Eastern Tibetan Plateau

Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polarization radar.The time-height series of radar physical variables and mesoscale horizontal divergence δderived by quasi-vertical profiles(QVPs)indicated that the dendritic growth layer(DGL,-20°C to-10°C)was ubiquitous,with large-value zones of K_(DP)(specific differential phase),Z_(DR)(differential reflectivity),or both,and corresponded to various dynamic fields(ascent or descent).Ascents in the DGL of cloud systems with vigorous vertical development were coincident with large-value zones of Z_(DR),signifying ice crystals with a large axis ratio,but with no obvious large values of K_(DP),which differs from previous findings.It is speculated that ascent in the DGL promoted ice crystals to undergo further growth before sinking.If there was descent in the DGL,a high echo top corresponded to large values of K_(DP),denoting a large number concentration of ice crystals;but with the echo top descending,small values of K_(DP) formed.This is similar to previous results and reveals that a high echo top is conducive to the generation of ice crystals.When ice particles fall to low levels(-10℃ to 0℃),they grow through riming,aggregation,or deposition,and may not be related to the kinematic structure.It is important to note that this study was only based on a limited number of cases and that further research is therefore needed.