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.

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.

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.

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.

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.

Navigating the pathways:TAR-DNA-binding-protein-43 aggregation,axonal transport,and local synthesis in amyotrophic lateral sclerosis pathology

Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).

Design and redesign journey of a drug for transthyretin amyloidosis

The misfolding and subsequent aggregation of proteins into amyloid fibrils underlie the onset of a variety of human disorders collectively known as amyloidosis.Transthyretin(TTR)is one of the>30 amyloidogenic proteins identified to date and is associated with a group of highly debilitating and life-threatening disorders called TTR amyloidosis(ATTR).ATTR comprises senile systemic amyloidosis,which is linked to wild-type(WT)TTR aggregation,and hereditary ATTR,a dominantly inherited disorder caused by the deposition of one of over 130 TTR genetic variants.Senile systemic amyloidosis is a prevalent age-related amyloidosis,affecting up to 25%of the population over 80 years of age,and is characterized by the build-up of TTR fibrils in the myocardium.Regarding hereditary ATTR,the clinical presentation is highly heterogeneous,primarily affecting the peripheral nervous system(familial amyloid polyneuropathy-FAP)or the heart(familial amyloid cardiomyopathy).In rare cases,aggregation develops in the central nervous system,giving rise to a phenotype known as familial leptomeningeal amyloidosis(Carroll et al.,2022).

Role of glutathione transferase in phase separation of FUS and TAF15 in neurons

Amyotrophic lateral sclerosis(ALS)is a progressive neurodegenerative disorder characterized by the degeneration of motor neurons in the brain and spinal cord,leading to muscle weakness,paralysis,and ultimately death(Cleveland and Rothstein,2001).Frontotemporal lobar degeneration(FTLD)is a neurodegenerative disease affecting the frontal and temporal lobes of the brain,leading to changes in behavior,personality,and language(Van Langenhove et al.,2012).Both ALS and FTLD are classified as proteinopathies in which abnormal protein aggregation and accumulation in neurons contribute to the disease pathogenesis.Fused in sarcoma(FUS)is a DNA/RNA-binding protein involved in various cellular processes,including transcriptional regulation,RNA splicing,and DNA repair.Mutations in the FUS gene have been linked to familial ALS,highlighting the importance of FUS in the disease pathogenesis(Vance et al.,2009).In ALS and FTLD,aberrant post-translational modifications(PTMs)of FUS,such as phosphorylation,acetylation,and methylation,have been implicated in the promotion of FUS aggregation and neurotoxicity(Choi et al.,2023).Therefore,understanding the regulatory mechanisms of FUS PTMs is crucial for developing targeted therapies against these diseases.

Small heat shock protein B8:from cell functions to its involvement in diseases and potential therapeutic applications

Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to proteotoxic stress response and in other processes such as inflammation,cell division,and migration.HSPB8 binds misfolded clients to prevent their aggregation by assisting protein refolding or degradation through chaperone-assisted selective autophagy.In line with this function,the pro-degradative activity of HSPB8 has been found protective in several neurodegenerative and neuromuscular diseases characterized by protein misfolding and aggregation.In cancer,HSPB8 has a dual role being capable of exerting either a pro-or an anti-tumoral activity depending on the pathways and factors expressed by the model of cancer under investigation.Moreover,HSPB8 exerts a protective function in different diseases by modulating the inflammatory response,which characterizes not only neurodegenerative diseases,but also other chronic or acute conditions affecting the nervous system,such as multiple sclerosis and intracerebellar hemorrhage.Of note,HSPB8 modulation may represent a therapeutic approach in other neurological conditions that develop as a secondary consequence of other diseases.This is the case of cognitive impairment related to diabetes mellitus,in which HSPB8 exerts a protective activity by assuring mitochondrial homeostasis.This review aims to summarize the diverse and multiple functions of HSPB8 in different pathological conditions,focusing on the beneficial effects of its modulation.Drug-based and alternative therapeutic approaches targeting HSPB8 and its regulated pathways will be discussed,emphasizing how new strategies for cell and tissue-specific delivery represent an avenue to advance in disease treatments.

Present status and future directions:Apexification

The success rate of apexification is primarily determined by multiple factors,including the material used,the size of the open apex compared to the length of the root,and the technique used in each case.The main objective of this review was to provide an update on the present management of open apex to identify factors and circumstances that may influence the success of apexification using different materials and techniques.Future research on apexification should focus on how to treat open apices with wide periapical lesions without surgery.Previously,the predictability of these parameters with non-surgical procedures was uncertain,but now,with the use of a dental operating microscope,it has become more predictable.Another reason could be that extra visits are no longer required due to major advances in the armamentarium and materials used for apexification.

Zero-pole cancellation for identity-based aggregators:a constant-size designated verifier-set signature

In this paper we present a designated verifier-set signature(DVSS),in which the signer allows to designate many verifiers rather than one verifier,and each designated verifier can verify the validity of signature by himself.Our research starts from identity-based aggregator(IBA)that compresses a designated set of verifier’s identities to a constant-size random string in cryptographic space.The IBA is constructed by mapping the hash of verifier’s identity into zero or pole of a target curve,and extracting one curve’s point as the result of aggregation according to a specific secret.Considering the different types of target curves,these two IBAs are called as zeros-based aggregator and poles-based aggregator,respectively.Based on them,we propose a practical DVSS scheme constructed from the zero-pole cancellation method which can eliminate the same elements between zeros-based aggregator and poles-based aggregator.Due to this design,our DVSS scheme has some distinct advantages:(1)the signature supporting arbitrary dynamic verifiers extracted from a large number of users;and(2)the signature with short and constant length.We rigorously prove that our DVSS scheme satisfies the security properties:correctness,consistency,unforgeability and exclusivity.This is a preview of subscription content,log in to check access.

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator(LA).Therefore,this paper regards the flexible user-side resources as a virtual energy storage(VES),and uses the traditional narrow sense energy storage(NSES)to alleviate the uncertainty of VES.In order to further enhance the competitive advantage of LA in electricity market transactions,the operation mechanism of LA in day-ahead and real-time market is analyzed,respectively.Besides,truncated normal distribution is used to simulate the response accuracy of VES,and the response model of NSES is constructed at the same time.Then,the hierarchical market access index(HMAI)is introduced to quantify the risk of LA being eliminated in the market competition.Finally,combined with the priority response strategy of VES and HMAI,the capacity allocation model of NSES is established.As the capacity model is nonlinear,Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it.In order to verify the effectiveness of the model,the data from PJM market in the United States is used for testing.Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response,and the economic benefit of LA can be increased by 52.2%at its maximum.Through the reasonable NSES capacity allocation,LA is encouraged to improve its own resource level,thus forming a virtuous circle of market competition.