Data Aggregation Points Research Articles

New Dual Algorithm to Placement the Data Aggregation Point for Smart Grid Meters

New Dual Algorithm to Placement the Data Aggregation Point for Smart Grid Meters

Geometric Deep-Learning-Based Spatiotemporal Forecasting for Inverter-Based Solar Power

Conventional power grids dominated by synchronous generators gradually shift to variable renewable-energy-integrated grids. With the growth of building-level photovoltaic (PV) panels and other inverter-based resource (IBR) deployments in recent years, market retailers and distribution operators have had to deal with the additional operational and management challenges posed by unobserved energy flow if its behind-the-meter (BTM) configuration. Also, the intermittent and stochastic nature of IBR-based solar power introduces uncertainty into the net load forecasting of electric distribution systems. Hence, management and control with high uncertainty in load prediction due to unobservable PV is a technical challenge. This article presents deep-learning-based algorithms for BTM PV power generation using a limited number of sensors in a given distribution system and extending to adjacent geographical areas. The proposed BTM PV forecasting method is based on geometric deep learning—a spatiotemporal graph neural network by processing the relationship between data in a non-Euclidean graph structure. The predictions of short-term BTM PV are aggregated with loss estimation at the data aggregation point with the net load forecasting to compute the true load forecasting with superior performance. The developed BTM PV forecasting method significantly improved true load forecasting results, which were validated and analyzed using a collection of actual BTM PV and load measurements in a test distribution feeder.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

Optimizing data aggregation point location with grid-based model for smart grids

With the construction of smart grids, smart meters are gradually being installed in every house. In order to transfer the user data collected by smart meters to the control center, it is necessary to transfer the data to the data aggregation point (DAP) before being transmitted to the control center. The numbers and locations of DAPs affect the communication quality and cost of the smart meter neighborhood network, and because smart meters rely on wireless technology to transmit data, their transmission range is limited. Thus, suburban and rural areas require a large number of DAP installation needs, and it is very important to reduce their numbers. For this problem, this study proposes a grid-based relay DAP placement scheme and presents the corresponding algorithms to reduce the number of DAPs and to avoid the large impact of relay DAP locations on communication quality for the two cases of whether or not the number of relay DAPs is limited. This paper used random smart meter coordinates for testing, and the test results verify that the proposed solution can in fact significantly reduce the number of DAPs and avoid the large impact of relay DAP locations on communication quality.

Data Aggregation Point Placement for Smart Meters in the Smart Grid

Smart meters (SMs) measure and transmit fine-grained electricity consumption data to the data center at a certain high frequency (such as every 15 minutes). A data aggregation point (DAP) is a relay device between a cluster of SMs and data centers. The placement of DAPs significantly impacts the cost-effectiveness and quality of service (QoS) of the communication system. Nowadays, many newly installed SMs not only can send data to the DAPs but also receive/send data from/to other SMs. This paper proposes a novel approach for the optimal DAP placement in a multi-hop routing scenario, where SMs can serve as small relay devices so that communications among SMs can be leveraged to reduce the overall communication cost. To do so, we formulate a mixed-integer optimization problem that minimizes three types of cost: installation, transmission, and delay cost. Since the optimization problem involves too many binary variables to be efficiently solved to optimality, a heuristic approach is developed to obtain a desirable solution with a low computational burden. The heuristic iteratively implements clustering approximation, pole assignment, and optimal routing until convergence of the total cost value. Both an illustrative example and case studies verify the effectiveness of the proposed method.

Cost-Effective Data Aggregation Method for Smart Grid

Finding a more efficient use of energy is an important problem that needs attention. Compared with the traditional power grid, a smart grid can monitor users’ electricity situation and electricity consumption instantly. However, it involves many problems of deploying network equipment. Consequently, it is vital to promote smart grids by collecting data from smart meters efficiently and keeping costs low. In this article, we propose a two-stage method of data collection for smart grids. The main contribution of this paper is to lower the number of data aggregation points (DAPs) so that the cost can be reduced. By using the K-means method, an entire smart grid can be divided into many smaller parts. In addition, the needs of transmitting and receiving data in the entire smart grid can be met by installing the least number of DAPs. Finally, the simulations show that the proposed two-stage method of data collection can use fewer DAPs to collect data than other methods which use one-stage methods, so the proposed scheme is more cost-effective.

Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities

Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.

Enhancement of Advanced Metering Infrastructure Performance Using Unsupervised K-Means Clustering Algorithm

Data aggregation may be considered as the technique through which streams of data gathered from Smart Meters (SMs) can be processed and transmitted to a Utility Control Center (UCC) in a reliable and cost-efficient manner without compromising the Quality of Service (QoS) requirements. In a typical Smart Grid (SG) paradigm, the UCC is usually located far away from the consumers (SMs), which has led to a degradation in network performance. Although the data aggregation technique has been recognized as a favorable solution to optimize the network performance of the SG, the underlying issue to date is to determine the optimal locations for the Data Aggregation Points (DAPs), where network coverage and full connectivity for all SMs deployed within the network are achieved. In addition, the main concern of the aggregation technique is to minimize transmission and computational costs. In this sense, the number of DAPs deployed should be as minimal as possible while satisfying the QoS requirements of the SG. This paper presents a Neighborhood Area Network (NAN) placement scheme based on the unsupervised K-means clustering algorithm with silhouette index method to determine the efficient number of DAPs required under different SM densities and find the best locations for the deployment of DAPs. Poisson Point Process (PPP) has been deployed to model the locations of the SMs. The simulation results presented in this paper indicate that the NAN placement scheme based on the ageless unsupervised K-means clustering algorithm not only improves the accuracy in determining the number of DAPs required and their locations but may also improve the network performance significantly in terms of network coverage and full connectivity.

Clustering Algorithm-Based Network Planning for Advanced Metering Infrastructure in Smart Grid

Nowadays, legacy electrical distribution systems are migrating to a new modern electric grid with the capability of supporting different applications such as advanced metering infrastructure (AMI), distributed energy resources, and electric vehicles. Among these applications, AMI is playing an important role in delivering data from customers to power utilities, supporting reliable real-time monitoring, and remote operation of power quality data and voltage profile. The AMI consists of smart meters, data aggregation points (DAPs), the utility control center, and communication networks. Appropriate network planning plays an essential role in facilitating the exchange of data between consumers and power utilities as well as accommodating new smart grid applications and future growth. This work proposes an optimal placement of DAPs for AMI based on machine learning clustering techniques in residential grids. Network partitioning is introduced to create sub-networks and graph algorithms generate a deployment topology given optimization constraints. A new measurement metric called coverage density is considered to indicate neighborhood area networks (NAN) zones with the appropriate coverage. Three real scenarios of NAN are considered: urban, suburban, and rural. The proposed algorithm is evaluated and compared with conventional heuristic optimization methods with respect to average and maximum distance between smart meters and DAPs, coverage density, and execution time.

Efficient Positioning of Data Aggregation Point for Wireless Sensor Network

Efficient Positioning of Data Aggregation Point for Wireless Sensor Network

THOR: A framework to build an advanced metering infrastructure resilient to DAP failures in smart grids

Abstract Smart Grids represent an evolution of the currently deployed power transmission and distribution systems. It implements a bidirectional data and energy flow infrastructure, which enables new important applications, such as distributed generation, remote metering and demand response. These applications require that a control center collects data from and sends commands to smart meters on the consumer premises. The communication between the control center and smart meters is performed through a gateway, called Data Aggregation Point (DAP). The interconnection of smart meters and DAPs results in an Advanced Metering Infrastructure (AMI), which is a key component of Smart Grids. Because a DAP is a potential single point of failure, the AMI must be implemented in such a way that each smart meter can reach the control center through multiple DAPs. In this work, we propose THOR, a framework that takes advantage of DAP redundancy to improve AMI reliability in case of DAP failures. In addition, we also propose the MultiDAP Selection Algorithm (MDSA) as an efficient implementation of THOR. Simulations results show that MDSA is able to guarantee the delivery of nearly 100% of all sent messages, without imposing significant message delay penalties.

Cost Efficient Data Aggregation Point Placement With Interdependent Communication and Power Networks in Smart Grid

In a smart grid, data aggregation points (DAPs) are communication hubs between sensors, actuators, and control center. Each DAP has a limited communication capacity and requires an installation cost. We want optimal placement for DAPs to minimize cost without compromising communication quality-of-service (QoS) requirements. DAP placement problem must not be dealt with as a communication issue in isolation because of interdependency between communication network and power network. Inter-network cascading failures may occur when a failure in DAP leads to a failure in power network, which leads to further failures in other DAPs. To avoid such failures, we have identified three requirements: power independence , communication robustness , and power robustness . We have formulated a constrained optimization problem, called cost minimization DAP placement (CMDP) to minimize DAP installation cost while satisfying communication QoS requirements, and avoiding inter-network cascading failures. CMDP is proven NP-hard, and we have proposed a heuristic algorithm to produce sub-optimal solution in reasonable time. Evaluation results show that for small systems, compared to exhaustive search, the heuristic algorithm produces identical solutions at computation time which is 375 000 times shorter. Results also indicate that communication robustness and power robustness requirements can increase linearly total DAP installation cost.

MOSKOU: A Heuristic for Data Aggregator Positioning in Smart Grids

Smart grids represent a revolution in efficient energy distribution, requiring a series of new devices that communicate with each other. Thus, in addition to energy, smart grids also induce an information flow. The advanced metering infrastructure (AMI) is a middle-range communication network which provides communication between clients’ smart meters and data aggregation points (DAPs). This paper focuses on determining the minimum number of DAPs and their positions such that every smart meter is covered and able to communicate through good quality wireless links. In our proposal, the AMI planning is seen as an instance of the set covering problem. This instance is built by determining all good quality wireless links between smart meters and potential DAP installation sites. The proposal is further extended to consider the possibility of multi-hop communication and to find solutions with a given minimum coverage redundancy for each meter. We solve the problem by applying a heuristic, named memory oriented split using ${K}$ -means with post-optimization unification (MOSKOU), which splits the original problem into smaller ones that are optimally solved. The split is based on the ${K}$ -means clustering algorithm. Results show that MOSKOU is capable of finding near-optimal solutions for huge instances while greatly reducing the amount of RAM memory needed and execution time.

Cost- and reliability-oriented aggregation point association in long-term evolution and passive optical network hybrid access infrastructure for smart grid neighborhood area network

With the rapid development of the smart grid, the data aggregation point (AP) in the neighborhood area network (NAN) is becoming increasingly important for forwarding the information between the home area network and wide area network. Due to limited budget, it is unable to use one-single access technology to meet the ongoing requirements on AP coverage. This paper first introduces the wired and wireless hybrid access network with the integration of long-term evolution (LTE) and passive optical network (PON) system for NAN, which allows a good trade-off among cost, flexibility, and reliability. Then, based on the already existing wireless LTE network, an AP association optimization model is proposed to make the PON serve as many APs as possible, considering both the economic efficiency and network reliability. Moreover, since the features of the constraints and variables of this NP-hard problem, a hybrid intelligent optimization algorithm is proposed, which is achieved by the mixture of the genetic, ant colony and dynamic greedy algorithm. By comparing with other published methods, simulation results verify the performance of the proposed method in improving the AP coverage and the performance of the proposed algorithm in terms of convergence.

Data Aggregation Point Placement Problem in Neighborhood Area Networks of Smart Grid

A smart meter neighborhood area network is usually regarded as the last mile network, which plays a significant role for communications in smart grid. A neighborhood area network typically consists of smart meters and Data Aggregation Points (DAPs), which collect energy consumption or billing information from smart meters and forward the information to wide area network gateways via wireless communications. The location of DAPs significantly affects the distance and associated transmission routes between DAPs and smart meters. In this paper, we investigate the DAP placement problem and propose solutions to reduce the distance between DAPs and smart meters. Specifically, the DAP placement problem is formulated with two objectives, e.g., the average distance minimization and the maximum distance minimization. The concept of network partition is introduced in this paper and two associated algorithms are developed to address the DAP placement problem. Extensive simulations are conducted based on a real suburban neighborhood topology. The simulation results verify that the proposed solutions are able to remarkably reduce the communication distance between DAPs and their associated smart meters.

Scalable Route Map for Advanced Metering Infrastructure Based on Optimal Routing of Wireless Heterogeneous Networks

This article presents a scalable route map for the least cost deployment of wireless heterogeneous networks that support traffic from the advance metering infrastructure (AMI). We first explore the performance of a common scenario in which a single technology is employed to connect smart meters sending traffic to the utility. Based on simulations with actual city maps, we study the coverage provided to smart meters by an LTE cellular network. In order to improve the coverage, an optimization model that considers network capacity and range is proposed to determine the optimal location of base stations to achieve a target coverage of smart meters. According to these preliminary results with a single access technology, we propose an evolved network architecture that considers several alternatives of wireless heterogeneous networks to guarantee the coverage to smart meters with the least use of resources. We introduce a heuristic model that involves elements from base stations, universal data aggregation points, number of smart meters, and an optimal routing to achieve the desired connectivity from the group of smart meters. We employ geo-referenced models to consider actual characteristics of cities as well as geographical conditions. Results from the evolved model demonstrate that by combining technologies and employing data aggregation points with optimized localizations, the network is able to achieve a target coverage of smart meters with a reduced cost in terms of technological resources.

Fuzzy-based trusted routing to mitigate packet dropping attack between data aggregation points in smart grid communication network

Providing adequate security measure during data transmission is one of the essential requirements of smart grid communication network (SGCN). In this paper, a novel trust management model is proposed to alleviate packet dropping attack between data aggregation points of SGCN. The concept of probability theory is used to calculate trust of every node in the network before transmitting the packet. Trust evaluation is conducted in four stages viz., Direct, Indirect, Integrated and Overall trust and in each stage, suitable equations are suggested to measure the trust of each node. A novel technique based on fuzzy set theory is used to route the packet through the trusted channel. Hop count, reliability and capacity of the link are the parameters considered for constructing if-then rules and membership function. A qualitative reasoning is performed using inference mechanism and a defuzzication strategy is applied on the calculated trust value to make decision. Simulations are carried out to evaluate the performance of the proposed approach by varying the trust threshold and malicious nodes. From the experiments, it is observed that for all the metrics, the proposed trusted routing using fuzzy theory helps to improve the network performance with much reliable communication and reduced packet loss.

Mobility assisted data gathering with solar irradiance awareness in heterogeneous energy replenishable wireless sensor networks

Wireless sensor networks adopting static data gathering may suffer from unbalanced energy consumption due to non-uniform packet relay. Although mobile data gathering provides a reasonable approach to solving this problem, it inevitably introduces longer data collection latency due to the use of mobile data collectors. In the meanwhile, energy harvesting has been considered as a promising solution to relieve energy limitation in wireless sensor networks. In this paper, we consider a joint design of these two schemes and propose a novel two layer heterogeneous architecture for wireless sensor networks, which consists of two types of nodes: sensor nodes which are static and powered by solar panels, and cluster heads that have limited mobility and can be wirelessly recharged by power transporters. Based on this network architecture, we present a data gathering scheme, called mobility assisted data gathering with solar irradiance awareness (MADG-SIA), where sensor nodes are clustered around cluster heads that adaptively change their positions according to solar irradiance, and the sensing data are forwarded to the data sink by these cluster heads working as data aggregation points. We evaluate the performance of the proposed scheme by extensive simulations and the results show that MADG-SIA provides significant improvement in terms of balancing energy consumption and the amount data gathered compared to previous work.

Hybrid Spread Spectrum based Smart Meter Network using Fast Frequency Hopping

Smart metes used in electric grids need a dedicated network that should be highly reliable & cost effective. Various techniques like 3G cellular have been proposed to improve efficiency of this smart grid electric meter network. For distribution of proper information in smart grid system Hybrid Spread Spectrum using slow frequency technology is also better choice. To improve the performance of this network in the terms of throughput and number of smart meter per data aggregation point (DAP), we have proposed HSS-FFH (to implement AMI) method. These techniques give better result in terms of coverage high density population area & interference immunity.

Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

Large scale, duty-cycled, wireless sensor networks provide support for applications ranging from anomaly detection to vehicle tracking. In order meet the requirements of these applications an autonomous configuration and maintenance method that is efficient and effective is required. When selecting a management solution it is important to consider both the direct and indirect costs associated with the different solution. For example, the overhead associated with communication synchronization and scheduling is an example of an indirect cost that can significantly impact the network lifetime. Further, an effective solution needs to recognize that in-network data aggregation and analysis presents significant benefits and should configure the network with a structure that benefits application layer functions. NOA, the proposed network management protocol, utilizes a multi-parent hierarchical logical structure. The multi-parent structure provides application layer functions with significant inherent benefits such as, but not limited to: elimination of the single-parent network divisions, data resolution guarantees when comparisons are performed at data aggregation points, and redundancies for communication as well as in-network data aggregation, analysis and storage.