Aggregator Research Articles

PSAK: A provably secure authenticated key agreement scheme based on extended Chebyshev chaotic maps for smart grid environments

AbstractSmart grid (SG) has attracted great attention for its reliability, sustainability, and efficiency, which uses artificial intelligence technology, automation technology, and Internet of Things (IoT) technology. In SG, it is crucial to set up a secure communication between an Aggregator (AG) and a Smart Meter (SM) to achieve mutual authentication and key agreement. However, most previous authentication and key management solutions have their own shortcomings in either security or efficiency, which make them difficult to satisfy the security requirements of SG. In this paper, in order to balance the security and efficiency, we propose an efficient and provably secure authenticated key agreement scheme based on the extended Chebyshev chaotic maps for SG environments. The AG and SM can conduct a mutual authentication and key agreement process by applying the extended Chebyshev polynomial operations to reduce the total calculation delay. The proposed scheme provides anonymity protection for SMs to achieve perfect forward secrecy and can resist to the typical attacks including replay attacks and identity forgery attacks by informal security analysis. The security properties are evaluated by using the Canetti and Krawczyk (CK) adversary model. The performance comparison demonstrates that the proposed scheme is efficient and the required security attributes are not compromised.

Improvement of regional market management considering reserve, information-gap decision theory, and emergency demand response program

There is a variety of items which should be taken into consideration by a regional market manager (RMM). Participants in the market, technical constraint, price variation/reaction, electricity-price uncertainty and types of the applied demand response program are some instances in this regard. One of the demand response programs is Emergency Demand Response Program (EDRP) which is considered in this paper. In the present study, the objective function of the RMM is formulated in a market environment in order to determine the optimal demand, incentive and power purchased with considering some of technical constraints such as incentive limits, demand limits, power purchased and power balance. Co-evolutionary Improved Teaching Learning-Based Optimization (C-ITLBO) is applied to maximize the RMM’s profit. Furthermore, determination of the demand level in the EDRP is performed on the basis of a logarithmic model which includes the price elasticity matrix (PEM) is included. The reserve supplied due to Aggregators (AGGs) is also prioritized using the reserve-margin factor (RMF). In addition, information-gap decision theory (IGDT) is applied to model uncertainty in the initial electricity price. The above mentioned items are modeled in a multi-level formulation.

An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game

In order to utilize demand-side flexibility and meet system requirements, the aggregator (AGG) has become a vital role in the ancillary service market. To reflect the willingness of each market subject, a tri-layer ancillary service market framework for ancillary service market operator (ASMO), AGG, and users is proposed in this paper, which contains the interactions between different layers including ASMO and AGG, AGG and users. We develop a detailed market timeline that takes multi-type ancillary services into account. Moreover, due to the hierarchical market structure, we conduct a two-loop Stackelberg game to depict the effect of each market subject’s strategic behavior. Based on the game models of all the market subjects, the existence and uniqueness of the game equilibrium point are proved. In addition, considering the correlation between multi-type ancillary services, the clearing process is decomposed into two sub-problems and a distributed joint market clearing based on the particle swarm optimization algorithm is presented. Case study shows that the proposed method is profitable for all the market subjects, the market timeline is reasonable, and the joint clearing method is effective.

A fault tolerant optimal relay node selection algorithm for Wireless Sensor Networks using modified PSO

Wireless Sensor Networks (WSNs) have energy-constraints that restricts to achieve prolonged network lifetime. To optimize energy consumption of sensor nodes, clustering is one of the efficient techniques for minimization of energy conservation in WSNs. This technique sends the collected data towards the SINK based on cluster head (CH) nodes that leads to the saving of energy. WSNs have been faced a crucial issue of fault tolerance and the overall data communication is collapsed due to the failure of cluster head. Various fault-tolerance clustering methods are available for WSNs, but they are not selected the backup nodes properly. The backup nodes’ closeness or location to the other remaining nodes is not considered in these methods. They may increase network overhead with the backup nodes accessibility. A fault-tolerance cluster-based routing method is presented in this paper that aims on providing fault tolerance for relay selection in addition to the data aggregation method for clustered WSNs. The proposed method utilizes backup mechanism & the Particle Swarm Optimization (PSO) to achieve this. Based on the distance from sink, residual energy, and link delay parameters, the CHs are chosen and the network is categorized into the clusters. The Backup CHs are selected by estimating the centrality among the nodes. As a part of intra-cluster communication for reducing the aggregation overhead among CHs, the Aggregator (AG) nodes are deployed in every cluster. So that they act as the bridge between the member nodes and CHs. These AG nodes aggregates the information from member nodes and deliver it to the CHs. The PSO with modified fitness function is used to identify the best relays between AG and member nodes. The proposed mechanism is compared with existing techniques such as EM-LEACH AI-Sodairi and Ouni (2018), QEBSR Rathee et al. (2019), QOS-IHC Singh and Singh (2019), and ML-SEEP Robinson et al. (2019). The simulation results proved that the proposed mechanism reduces overhead by 55% and improve the energy consumption & throughput by 40% & 60% respectively.

Modeling a Local Electricity Market for Transactive Energy Trading of Multi-Aggregators

The present article aims at modeling a day-ahead local electricity market (DA LEM) for transactive energy trading at the distribution level. In this regard, a wide range of distributed energy resources (DERs) in the form of multiple aggregators (AGs) participates in the DA LEM in order to trade energy with the distribution system operator (DSO), the operator of the market. On the other hand, the DSO, as the owner of the system, has the responsibility to procure the required energy of its customers with respect to the technical constraints of the distribution network. To settle the designed local market, a Stackelberg game-based approach is exploited in this research work. In the raised Stackelberg scheme, the leader of the game, the DSO, seeks to maximize its expected profit, while followers of the game, DER AGs, tend to minimize their operating costs. Ultimately, to evaluate the proposed framework, a typical case study is implemented on a modified IEEE-33 bus test system.

A Certificateless Pairing-Free Authentication Scheme for Unmanned Aerial Vehicle Networks

In unmanned aerial vehicle networks (UAVNs), unmanned aerial vehicles with restricted computing and communication capabilities can perform tasks in collaborative manner. However, communications in UAVN confront many security issues, for example, malicious entities may launch impersonate attacks. In UAVN, the command center (CMC) needs to perform mutual authentication with unmanned aerial vehicles in clusters. The aggregator (AGT) can verify the authenticity of authentication request from CMC; then, the attested authentication request is broadcasted to the reconnaissance unmanned aerial vehicle (UAV) in the same cluster. The authentication responses from UAVs can be verified and aggregated by AGT before being sent to CMC for validation. Also, existing solutions cannot resist malicious key generation center (KGC). To address these issues, this paper proposes a pairing-free authentication scheme (CLAS) for UAVNs based on the certificateless signature technology, which supports batch verification at both AGT and CMC sides so that the verification efficiency can be improved greatly. Security analysis shows that our CLAS scheme can guarantee the unforgeability for (attested) authentication request and (aggregate) responses in all phases. Performance analysis indicates that our CLAS scheme enjoys practical efficiency.

A Stackelberg Game-Based Approach for Transactive Energy Management in Smart Distribution Networks

Recently, with the penetration of numerous Distributed Energy Resources (DER) in Smart Distribution Networks (SDN), Local Transactive Markets have emerged. Exchanging energy between all participants of local markets results in the satisfaction of producers and consumers. Based on these issues, this study provides a novel framework for the participation of SDN-independent entities in wholesale and local electricity markets simultaneously. In this regard, the considered system’s players, namely Distribution System Operator (DSO) and DER Aggregator (AG), take part within local as well as wholesale markets in two-day ahead and real-time stages. Moreover, to deal with the inherent conflict between the existing players’ interests, a Stackelberg game-based technique is proposed. In the raised competition, the leader, DSO, attempts to minimize its operating costs, while the follower, DER AG, tends to maximize its profit. Therefore, actors’ actions choices within both markets are made non-cooperatively. On the other hand, to handle the uncertain nature of stochastic parameters in the depicted problem, Monte Carlo Simulation (MCS), together with a fast backward/forward scenario reduction approach, is exploited. Ultimately, to evaluate the efficiency of the proposed scheme, two different case studies, with and without considering the competitive environment, are implemented on a modified IEEE-33 bus SDN.

Optimal Bidding and Operation Strategies for EV Aggegators by Regrouping Aggregated EV Batteries

We propose an optimal operation strategy for an electric vehicle (EV) aggregator (AGG), which performs energy arbitrage in the energy market and provides ancillary services from aggregated EVs, while providing charging services to EVs to maximize the profit in a future energy market. We group EV batteries as several virtual batteries (VB) with respect to their departure time and stages to implement multiple schedules simultaneously in the multi-stage stochastic optimization (MSSO). They are continuously grouped and regrouped as EVs arrive and depart. We predict VB scenario trees of stepwise EV driving routes and optimize the decisions in responding to uncertain EV movements. The VB states change as EVs enter and exit through the stages, so we should indirectly track time-varying VB characteristics at each stage. Then, we distribute the regulation bids for the VBs to the individual EVs. To reduce mismatches between the bidding amounts for VBs and actual transacted amounts for EVs, we suggest a novel binary progressive hedging algorithm to quickly determine the VB operations. Based on data from historical vehicle statistics, we verify that our strategy with the MSSO model provides a higher profit to the AGG than two-stage stochastic models.

Energy and Information Management of Electric Vehicular Network: A Survey

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

A Discounted Stochastic Multiplayer Game Approach for Vehicle-to-Grid Voltage Regulation

Along with the diversification of electricity market, the voltage regulation (VR) service is opened up to various qualified providers to meet the enormous demand, among which, the electric vehicle (EV) aggregators (AGGs) can integrate the scattered EVs and play the role of VR sources with a high response speed and low cost. In order to coordinate the VR service providers to satisfy the total VR requirement, auction mechanisms are widely used to select VR sources to achieve better performance. Yet, it is challenging to optimize the strategy of each EV AGG in the auction process due to the stochastic EV mobility, various distribution network topology, and the competition mechanism. To address these challenges, we proposed a discounted stochastic multiplayer game (DSMG) approach to analyze the competition among EV AGGs. Due to the constraint of distribution network topology, the efficiency of the VR sources at different locations can be different. Thus, the impact of distribution network topology on the VR efficiency is investigated by DSO when evaluating the capacity of AGGs. The randomness of EV numbers is considered when predicting the AGGs’ available VR capacity so that the tendency for the AGGs to follow the optimal strategies can be modeled accurately. Accordingly, a linear power flow analysis approach and a battery pool model are developed to address the distribution network topology and EV mobility, respectively. Then, the DSMG approach is used in the VR auction process to optimize the AGGs’ strategies. The existence proof of the stationary Markov perfect equilibrium is presented, and the corresponding algorithms to obtain the equilibrium is proposed. The performance of the proposed DSMG approach is evaluated and compared with other approaches based on the IEEE 33-bus test feeder, IEEE 123-bus test feeder, and the real-world generation and load data from PVWatts Calculator and Market Analysis and Information System, respectively.

Contract Farming in the Morocco Cereal Sector: Contract Clauses, Ambiguity, and Opportunism

Our main aim was to analyze of the various types of contractual clauses framework of Agricultural Aggregation Projects (AAP) in the cereal sector in Morocco. We used the data from the content of the contracts and by individual interviews with key actors complicated in the process of implementation of the aggregation projects of cereals. The results reveal that only eight Agricultural Aggregation Project, out of 119, received the certificate of aggregation. This is reflecting the fact that, the implementation of this model of vertical coordination was underway. In addition, the analysis of the contract-farming data for clauses has shown us 5 important clauses categories: production (quantity and quality) and payment methods; agricultural extension systems; inputs supply; management of risks; and dispute resolution mechanisms. These findings highlight the value added to the contractual clauses and the need to strengthen the visibility and role of the contract farming as a framework to accompany the investments of ‘‘The Green Morocco Plan’’, in particular with regard to agricultural aggregation projects. We also show that the contract participation reduces the transaction costs in aggregation system, reduction in the number of intermediaries and ensure the link between the producers and markets. The study concludes that, despite enactment of laws on farm aggregation, contract smallholders remain vulnerable to opportunist behavior. It suggests that the contract clauses must be accompanied by commensurate controls and Involving farmers in negotiating contract terms to ensure ‘win-win’ outcomes for Aggregator (AG) and Aggregated (Ag).

Energy-Aware Optimal Data Aggregation in Smart Grid Wireless Communication Networks

We propose a two-hop wireless communication architecture for smart grid (SG) consisting of smart meters (SMs), aggregators (AGs) and cellular base stations (BSs). The architecture is then considered to transfer the periodic traffic of the SMs to the BSs via the AGs with data concatenation or direct transmission. We formulate an optimization problem to minimize the energy consumption by the optimal AG selection for the SMs, data concatenation and transmit power allocation which turns out as a mixed integer non-linear programming. By means of several analytical formulations and an optimization tool, we solve the problem for many SG network instances and demonstrate that two hop communication architecture with the optimal configuration requires significantly less energy compared to a single hop communication architecture without data aggregation. We also investigate the effects of the number of the SMs, modulation order, wireless channel, and sizes of packets on energy consumption and provide various engineering insights. Further, a heuristic algorithm is developed for solving the optimization problem without any optimization tool. The effectiveness of the heuristic algorithm is verified by comparing the energy consumptions of the heuristic and the optimal algorithms. The comparison results show that the difference of the energy consumptions is not significant.

Design of Modified Droop Controller for Frequency Support in Microgrid Using Fleet of Electric Vehicles

Droop control has been extensively used by academia and industry for managing frequency fluctuations at microgrid (MG). This controller generates series of reference signals to different distributed energy sources (DES) such as diesel generator, PV, and wind system based on their respective maximum capacities. However, the combination of inertial and noninertial DES leads to frequency fluctuations at MG. Therefore, this paper proposes a novel frequency support strategy by controlling the operation of DES and controllable loads, i.e., electric vehicles (EVs). Moreover, fleet of EVs batteries maximum capacity depends upon constraints, such as state of charge (SoC), random arrival, and departure patterns. Hence, to accomodate EVs' batteries with droop controller, it has been enhanced with communication links and feedback mechanism and referred as modified droop controller (MDC). This MDC would be now able to dispatch reference signals to both DES and EVs. However, for meeting reference signal given to fleet of EVs, an aggregator (AG) is proposed. AG can alter the charging and discharging rates of EVs' thereby satisfying EVs' needs in terms of battery's SoC. Extensive simulations have been done using the proposed scheme on data available from Santa Rita Jails' MG.

DETAILED STUDY OF AGGREGATOR FOR UPDATES

Nowadays, personal weblogs, news web sites, and dis cussion forums are publishing RSS feeds for their sretrieve newpostings.There has been a dramatic increase in t he use of XML data to deliver information over the Web. Everydaymillions of new articles are published and presented by RSS fee ds.Also, news feeders, newsgators are available for subscribers or the readers bythe individual sites, the news feeders regularly pull articles from theWeb sites, the aggregated eff ect by all news feeders puts anenormousload on many sites. We propose ablog aggr egator approach where a central aggregatormonitors and retrieves new postings from different data sources and subsequent ly disseminates them to the subscribersto alleviate such a problem. We study how the aggregator should monitor thedata sources t o quickly retrieve new postings using minimalresour ces and to provide its subscribers with fast news alert. Thus, this reduce s the load onthe monitored data sources by a signif icant amount.

Scalability improvement of the real time control protocol

Scalability problems arise when the Real Time Control Protocol (RTCP), which is the control protocol of the Real-time Transport Protocol (RTP), is used in large multicast groups. The problems include: increased feedback delay, increased storage state at every member, and ineffective RTCP bandwidth usage. We have designed a Scalable RTCP (S-RTCP) which is based on a hierarchical structure in which members are grouped into local regions. For every region, there is an Aggregator (AG) which receives the feedback Receiver Reports (RRs) sent by its local members. The AG summarizes important information in the RRs, derives some statistics, and sends them to a Manager. The Manager performs additional statistical analysis to monitor the transmission quality and to identify regions suffering massively from congestion. A simulation of S-RTCP using the Network Simulator (NS) showed that S-RTCP alleviates some of the RTCP scalability problems and makes effective use of RRs. Consequently, the feedback provides timely and useful QoS information required for network monitors and for adaptive applications. Details of the simulations and performance analysis are presented and described which show the advantages of using S-RTCP over the original RTCP.