Smart Grid Data Research Articles

Economic Viability of Smart Grid Cloud in India

While the innovation and technology is modernizing the industry at rapid pace, the electrical power system is lagging behind the race and is being maintained in the same way for decades. Although the smart grid has ensured demand-response, reduction in transmission and distribution losses, and minimized theft, the inherent characteristic of enormous data generation has become a biggest concern. The absence of IT infrastructure in existing set up for handling unstructured data and huge capital investments are the major obstacles in complete success of smart grid in developing countries like India. However cloud computing has emerged as a solution to this problem. In this work, we have simulated cloud for smart grid on CloudSim and derived the cost for storing the smart grid data on cloud. The total cost of ownership (TCO) and return on investment (ROI) on building the datacenter with same configuration is also calculated using net present value (NPV), and compared with previous calculated cost. The results indicate that the initial cost of building a datacentre is $6673185 and ROI is 22.74 % in 7 years, which is low keeping in view the fact that during average design life of 10 years, a datacenter undergoes major equipment changes 3 to 4 times. On the contrary, outsourcing the same data on to cloud costs $2430720 annually, which is considered as an effective alternative. This analysis would be beneficial to developing countries like India in framing policies for storing the smart grid data on to the cloud which will further help in reaping the benefits of smart grid.

Smart grid data analytics framework for increasing energy savings in residential buildings

Abstract Human energy consumption has gradually increased greenhouse gas concentrations and is considered the main cause of global warming. Currently, the building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. This paper presents a framework for smart grid big data analytics and components required for an energy-saving decision-support system. The proposed system has a layered architecture that includes a smart grid, a data collection layer, an analytics bench, and a web-based portal. A smart metering infrastructure was installed in a residential building to conduct an experiment for evaluating the effectiveness of the proposed framework. Furthermore, a novel hybrid nature-inspired metaheuristic forecast system and a dynamic optimization algorithm are designed behind the analytics bench for achieving accurate prediction and optimization of future energy consumption. The main contribution of this study is that an innovative framework for the energy-saving decision process is presented; the framework can serve as a basis for the future development of a full-scale smart decision support system (SDSS). Through the identification of consumer usage patterns, the SDSS is expected to enhance energy use efficiency and improve the accuracy of future energy demand estimates. End users can reduce their electricity costs by implementing the optimal operating schedules for appliances, which are provided by the SDSS.

QoS Model of WSNs Communication in Smart Distribution Grid

This paper presents an integrated modeling method providing superior Quality of Service (QoS) for wireless sensor networks (WSNs) which is suitable for smart distribution grid (SDG) communication. Traditional QoS model based on IEEE802.15.4 protocol cannot meet the communication requirements of multipriority data in smart distribution grid. In order to meet the specification of power system communication, a new QoS-enabled medium access control (MAC) model based on unfair competition channel access mechanism (UCCAM) is developed in this paper. The proposed model can ensure that the communication system of WSN provides different Qualities of Service for different priority data. According to different requirements for the communication time, the data of SDG are divided into three types: high priority, middle priority, and low priority data. The state transition of buffering queue data in node is described by a three-dimensional Markov chain model. Delay time, effective throughput rate, and channel collision rate models are developed, respectively, to evaluate communication performance of WSN applied in smart distribution grid. The simulation results show that the proposed model can provide different Qualities of Service for different priority data and is more efficient than the IEEE802.15.4e standard and the traditional methods.

Optimal Trust System Placement in Smart Grid SCADA Networks

The objective of this paper is to propose a trust system placement scheme for smart grid supervisory control and data acquisition (SCADA) networks. The functionalities of a trust system include firewalling and network intrusion detection. It is capable of monitoring both ingress traffic and egress traffic. In order to minimize the capital expenditure (CAPEX) and the operational expenditure (OPEX), only a selected number of nodes are equipped with trust systems. Those nodes are known as the trust nodes. This paper studies the trust system placement problem from a network topological perspective. It develops a scheme that aims to defend SCADA networks, deploying minimal number of trust nodes. It uses a network segmentation approach to distribute the trust nodes. It considers the minimum spanning tree (MST) as a measure of geographic dispersion. In the segmentation approach, size balancing and geographic dispersion are two main concerns. The segment sizes affect the number of required trust nodes. On the other hand, geographic dispersion affects the response time. The proposed scheme computes trust nodes using linear programming problem (LPP) formulations and local search. Numerical analysis is conducted through case studies for the IEEE test system topologies. It reveals the consistency of performance, better quality of protection, and low computational time. The proposed scheme can be a useful cyber security planning tool for smart grid operators.

Energy big data: A survey

As a significant application of energy, smart grid is a complicated interconnected power grid that involves sensors, deployment strategies, smart meters, and real-time data processing. It continuously generates data with large volume, high velocity, and diverse variety. In this paper, we first give a brief introduction on big data, smart grid, and big data application in the smart grid scenario. Then, recent studies and developments are summarized in the context of integrated architecture and key enabling technologies. Meanwhile, security issues are specifically addressed. Finally, we introduce several typical big data applications and point out future challenges in the energy domain.

Cost optimisation approach for cloud-based smart grid data management

The smart grid is an advanced power grid with efficient two-way communication of power and data. Due to the modular nature of network and users, the data storage and processing were required. Cloud computing is one of the efficient technology for storing and managing such data in the real world. To solve such data management issues, we proposed a cloud-based smart grid data management (CSGDM) model and an optimisation algorithm to resolve the challenges of such environment. The proposed algorithm deals with the grid utilisation and power usage cost calculation with an optimisation mechanism for cost function to minimise the data storage prices.

Secure and Scalable Data Collection With Time Minimization in the Smart Grid

Deployment of data generation devices such as sensors and smart meters have been accelerating toward the vision of smart grid. The volume of data to be collected increases tremendously. Secure, efficient, and scalable data collection becomes a challenging task. In this paper, we present a secure and scalable data communications protocol for smart grid data collection. Under a hierarchical architecture, relay nodes [also known as data collectors (DCs)] collect and convey the data securely from measurement devices to the power operator. While the DCs can verify the integrity, they are not given access to the content, which may pave the way for third party providers to deliver value-added services or even the data collection itself. We further present optimization solutions for minimizing the total data collection time.

Security in big data

Security in big data

Resumable Load Data Compression in Smart Grids

We propose a compression approach for load profile data, which addresses practical requirements of smart metering. By providing linear time complexity with respect to the input data size, our compression approach is suitable for low-complexity encoding and decoding for storage and transmission of load profile data in smart grids. Furthermore, it allows for resumability with very low overhead on error-prone transmission lines, which is an important feature not available for standard time series compression schemes. In terms of compression efficiency, our approach outperforms transmission encodings that are currently used for electricity metering by an order of magnitude.

Context-awareness and the smart grid: Requirements and challenges

New intelligent power grids (smart grids) will be an essential way of improving efficiency in power supply and power consumption, facilitating the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The delivery of efficiencies and advanced services in a smart grid will require both a comprehensive overlay communications network and flexible software platforms that can process data from a variety of sources, especially electronic sensor networks. Parallel developments in autonomic systems, pervasive computing and context-awareness (relating in particular to data fusion, context modelling, and semantic data) could provide key elements in the development of scalable smart grid data management systems and applications that utilise a multi-technology communications network. This paper describes: (1) the communications and data management requirements of the emerging smart grid, (2) state-of-the-art techniques and systems for context-awareness and (3) a future direction towards devising a context-aware middleware platform for the smart grid, as well as associated requirements and challenges.

Building a Secure and Privacy-Preserving Smart Grid

New technologies for computerized metering and data collection in the electrical power grid promise to create a more efficient, cost-effective, and adaptable smart grid. However, naive implementations of smart grid data collection could jeopardize the privacy of consumers, and concerns about privacy are a significant obstacle to the rollout of smart grid technology. Our work proposes a design for a smart metering system that will allow utilities to use the collected data effectively while preserving the privacy of individual consumers.

Stream Computing: Opportunities and Challenges in Smart Grid

Traditional Power Grid is transformed to Smart Grid by utilizing the technological advancements of Information and Communication Technology. In Smart Grid data is flowing between different components of the system. Advanced and online analytic on this massive data is required to trigger instantaneous action for grid operation and management. Traditional Big Data handling techniques store and analyze high volume data with variety, but failed to handle high velocity data. Stream computing can analyze high velocity data with variety which is essentially required in online data analytics. This paper compared different Big Data analysis techniques and reveals the importance of stream computing and its opportunities in Smart Grid data analytics.

Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues

Smart grid has modernized the way electricity is generated, transported, distributed, and consumed by integrating advanced sensing, communications, and control in the day-to-day operation of the grid. Electricity is a core utility for the functioning of society and for the services provided by information and communication technologies (ICTs). Several concepts of the smart grid, such as dynamic pricing, distributed generation, and demand management, have significantly impacted the operation of ICT services, in particular, communication networks and data centers. Ongoing energy-efficiency and operational expenditures reduction efforts in communication networks and data centers have gained another dimension with those smart grid concepts. In this paper, we provide a comprehensive survey on the smart grid-driven approaches in energy-efficient communications and data centers, and the interaction between smart grid and information and communication infrastructures. Although the studies on smart grid, energy-efficient communications, and green data centers have been separately surveyed in previous studies, to this end, research that falls in the intersection of those fields has not been properly classified and surveyed yet. We start our survey by providing background information on the smart grid and continue with surveying smart grid-driven approaches in energy-efficient communication systems, followed by energy, cost and emission minimizing approaches in data centers, and the corresponding cloud network infrastructure. We discuss the open issues in smart grid-driven approaches in ICTs and point some important research directions such as the distributed renewable energy generation capability-coupled communication infrastructures, optimum energy-efficient network design for the smart grid environment, the impact of green communication techniques on the reliability and latency requirements of smart grid data, workload consolidation with smart grid-awareness, and many more.

Evaluating Content-centric Communication Over Power Line Communication Infrastructure for Smart Grids

Abstract Electricity management systems are being in the center of a major research effort aiming at developing its new generation known as SMART GRID. The novelty in smart grids is a two-way data flow added on top of the classic electricity flow. This information traffic is the reason behind the smartness of the new power management system qualified to be more efficient, more secure and more reliable than its predecessor. In consequence, well-tailored communication architecture with particular necessary characteristics for smart grids is needed. Our motivation behind this work is to assess the adequacy of an emergent communication paradigm referred to as content-centric networking to smart grids. Our first contribution is confronting smart grid communication system requirements with content-centric networking principles in order to theoretically justify our position. Our second contribution is a simulation of smart grid communications using smart grid network topology and data traffic. A content-centric communication solution is deployed to achieve data networking and is compared to the Internet protocol stack allowing us to witness packet delivery delay reduction and better congestion control ability in addition to an increased throughput.

Apache Spark a Big Data Analytics Platform for Smart Grid

Abstract Smart grid is a complete automation system, where large pool of sensors is embedded in the existing power grids system for controlling and monitoring it by utilizing modern information technologies. The data collected from these sensors are huge and have all the characteristics to be called as Big Data. The Smart-grid can be made more intelligent by processing and deriving new information from these data in real time. This paper presents Apache spark as a unified cluster computing platform which is suitable for storing and performing Big Data analytics on smart grid data for applications like automatic demand response and real time pricing.

A Brief Survey on Smart Grid Data Analysis in the Cloud

Abstract Cloud Computing has become more and more of a choice for storing and processing data due to the performance, scalability, availability and interoperability that it offers. The adoption of generic off-the-shelf Information and Communication Technologies (ICT) for the Smart Grid has come with many advantages, but also raised issues regarding the security, availability and reliability of the Smart Grid. The data generated by the Smart Grid's systems has the properties of a time series and its analysis needs to be secure and in real-time. As shown in this paper, cloud computing has become a real candidate for Smart Grid data analysis. We present the particularities of the data generated in the Smart Grid and the operations involved in its analysis. As well we compare existing solutions for stream data processing against this basic set of operations.

Wavelet based steganographic technique to protect household confidential information and seal the transmitted smart grid readings

Smart grids have recently drawn attention because of high efficiency, reliability and sustainability. They transmit (1) periodically collected readings (e.g. watts) and (2) highly sensitive data (e.g. geometric location). However, transmission and storage of smart grid data have many security issues. This paper proposes a novel steganographic technique that guarantees (1) strong end-to-end confidentiality of the sensitive information by hiding them randomly inside the normal readings using a generated key, and (2) robust authenticity for the transmitted readings. To facilitate hiding, Discrete Wavelet Transform is used to decompose normal readings into a set of sub-band coefficients. To achieve minimum distortion, only the least featured sub-band coefficients are used. To achieve high security, a key is used to generate a random hiding order in the form of 2D matrix which allows the system to specify exact locations in the wavelet generated 2D coefficients׳ matrix to hide sensitive data. To accurately measure the distortion after hiding and retrieving the sensitive data, PRD has been used. It is clear from experiments that our technique has little effect on the original readings (<1%). Also, our security evaluation proves that unauthorised retrieval of the confidential information is highly improbable within a reasonable time.

Combining a dynamic battery model with high-resolution smart grid data to assess microgrid islanding lifetime

In this paper, we use experimental data collected from an Austin, Texas smart grid test bed with a system-level battery energy storage model to assess the lifetime of batteries in a microgrid operating in islanded mode during a distribution-level outage. We consider a hypothetical microgrid consisting of 21 single-family detached homes and three transformer-level community energy storage (CES) battery units ranging in size from 25kWh to 75kWh. To describe the performance of CES batteries, we implement a dynamic behavioral circuit model capable of describing voltage transients and rate-capacity effects. We use one-minute electricity production and consumption data collected from the smart grid test bed in 2012 to assess how the timing of an electric outage affects the islanding lifetime of a residential microgrid. We contrast our results with the average outage duration reported by U.S. electric utilities to quantify how often a residential microgrid could withstand a typical outage. Our results show that increasing the amount of rooftop PV in a residential microgrid does not significantly increase how often it can withstand an average-duration outage. However, combining PV with CES extends the median islanding lifetime by up to 11.6h during morning outages. Based on our results, 50kWh CES provides the best tradeoff between the cost of a CES system and its reliability benefit, allowing downstream loads to withstand an average-duration outage approximately 93% of the time.

Disastrous Data Fusion Method Based on Dempster-Shafter Evidence Theory for Smart Gri

Smart grid has the powerful ability of self-healing and self-adaption. It can timely predict faults and disasters by analyzing and processing the detected data from sensors of the power grid before faults and disasters emerge. In order to improve the detection accuracy and reduce the redundant information, data fusion technology is introduced to process sensor data of smart grid. In this paper, Dempster-Shafter (D-S) evidence theory is applied to smart grid disaster predicting, two data fusion methods based on D-S evidence theory are proposed. The paper gives the data fusion framework of smart grid, then based on it presents the two data fusion methods in details, nally compares their performance by simulation test.

Uses of Cluster Computing Techniques to Perform Big Data Analytics for Smart Grid Automation System

Smart grid is a complete automation system, where large pool of sensors is embedded in the existing power grids system for controlling and monitoring it by utilizing modern information technologies. The data collected from these sensors are huge and have all the characteristics to be called as Big Data. The Smart-grid can be made more intelligent by processing and deriving new information from these data in real time. This paper presents Apache spark as a unified cluster computing platform which is suitable for storing and performing Big Data analytics on smart grid data for applications like automatic demand response and real time pricing.