Reliability Of Smart Grid Research Articles

IEC 61850-Based Communication Layer Modeling for Electric Vehicles: Electric Vehicle Charging and Discharging Processes Based on the International Electrotechnical Commission 61850 Standard and Its Extensions

With the increasing shift toward electric vehicles (EVs) as a means of transportation, their participation in smart grid operations is gaining importance. If not managed properly, charging a large fleet of EVs could lead to detrimental effects on the smart grid's reliability and stability. On the other hand, EVs can support the grid through vehicle-togrid (V2G) operation and help improve frequency stability and maximize energy use from renewable resources. Managing all these cases requires effective coordination, which hinges on communication between different equipment from varying vendors. Therefore, a standardized modeling that lends itself to interoperability is vital.

Smart Sensors for Smart Grid Reliability.

Sensors for monitoring electrical parameters over an entire electricity network infrastructure play a fundamental role in protecting smart grids and improving the network’s energy efficiency. When a short circuit takes place in a smart grid it has to be sensed as soon as possible to reduce its fault duration along the network and to reduce damage to the electricity infrastructure as well as personal injuries. Existing protection devices, which are used to sense the fault, range from classic analog electro-mechanics relays to modern intelligent electronic devices (IEDs). However, both types of devices have fixed adjustment settings (offline stage) and do not provide any coordination among them under real-time operation. In this paper, a new smart sensor is developed that offers the capability to update its adjustment settings during real-time operation, in coordination with the rest of the smart sensors spread over the network. The proposed sensor and the coordinated protection scheme were tested in a standard smart grid (IEEE 34-bus test system) under different short circuit scenarios and renewable energy penetration. Results suggest that the short-circuit fault sensed by the smart sensor is improved up to 80% and up to 64% compared with analog electromechanics relays and IEDs, respectively.

A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids

Electric power systems are moving toward smarter and more sustainable systems. These trends result in several positive advantages such as active participation of customers in electricity markets. However, resulting demand side flexibilities cause high demand fluctuations and increase the difficulty to maintain the power balance and reliability of smart grids. To address this challenge, this paper proposes a self-partitioning local neuro fuzzy model, which is capable of performing a fast and accurate short-term load forecasting. The proposed model, not only maintains the linearity as well as learning–from-data property via their fuzzy inference systems of local linear neuro fuzzy, but also benefits from partitioning the input space into linear and nonlinear vectors and assigning them separately into different local models. The proposed model is trained with the hierarchical binary-tree learning algorithm and rule premises are calculated through sigmoid partitioning functions. These appealing properties make the model appropriate for a fast and accurate analysis of the load time series featuring both linear and nonlinear characteristics. The effectiveness of the proposed model is compared with recently published forecasting models in terms of statistical performance.

A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study’s GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs.

Real-Time Monitoring & Adaptive Protection of Power Transformer to Enhance Smart Grid Reliability

Abstract Power transformer is one of the most important pieces of equipment in the grid to reliably and efficiently transmit power to the consumers. Asset management and protection are the best concepts for prolongation of transformer lifespan as well as for the increase of grid reliability. This article presents electrical and non-electrical parameter-based power transformer monitoring and protection. Various data such as core flux, age of asset, heat generation, current harmonics and temperature are monitored in real time and processed accordingly to enhance the working capability of the transformer. The proposed scheme is successfully tested on 15 kVA laboratory transformer using Arm CORTEX-M4 processor. A Fitness Function (Ff) is estimated from the collected data to examine the working condition of the transformer. Moreover, voltage, current and power-based inrush detection as well as Adaptive Power Differential Protection (APDP) are applied to protect the transformer against fault. The hardware implementation and result validation prove the effectiveness of the proposed scheme in enhancing reliability of the distribution grid.

Study on the performance indicators for smart grids: a comprehensive review

This paper presents a detailed review on performance indicators for smart grid (SG) such as voltage stability enhancement, reliability evaluation, vulnerability assessment, Supervisory Control and Data Acquisition (SCADA) and communication systems. Smart grids reliability assessment can be performed by analytically or by simulation. Analytical method utilizes the load point assessment techniques, whereas the simulation technique uses the Monte Carlo simulation (MCS) technique. The reliability index evaluations will consider the presence or absence of energy storage elements using the simulation technologies such as MCS, and the analytical methods such as systems average interruption frequency index (SAIFI), and other load point indices. This paper also presents the difference between SCADA and substation automation, and the fact that substation automation, though it uses the basic concepts of SCADA, is far more advanced in nature.

A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles

Because of the importance of smart grids reliability, various stochastic-based simulation methodologies e.g. Monte Carlo simulation (MCS) have been developed. In contrast, the much less effort has been devoted in literature to develop the generalized analytical ones. In this paper, a new generalized analytical methodology is developed for smart grids’ reliability assessment. The main contributions of this article are proposing a new state matrix (S-matrix) method including the novel model for investigating the smart grid operating modes by using the segmentation concepts and graph theory; developing a new comprehensive model of PHEVs considering the all their uncertainties; and introduction of a novel integrating methodology of separate elements.The proposed method is applied to the IEEE 33-bus test system. The comparison of test results and those of MCS-based methods and also with other available analytical methods illustrate the accuracy of the proposed method. The sensitivity analyses results imply that the proposed method is not adversely affected due to changes in different uncertain parameters.

Deep ensemble learning based probabilistic load forecasting in smart grids

With the availability of fine-grained smart meter data, there has been increasing interest in using this information for efficient and reliable energy management. In particular, accurate probabilistic load forecasting for individual consumers is critical in determining the uncertainties in future demand with the goal of improving smart grid reliability. Compared with the aggregate loads, individual load profiles exhibit higher irregularity and volatility and thus less predictable. To address these challenges, a novel deep ensemble learning based probabilistic load forecasting framework is proposed to quantify the load uncertainties of individual customers. This framework employs the profiles of different customer groups integrated into the understanding of the task. Specifically, customers are clustered into separate groups based on their profiles and multitask representation learning is employed on these groups simultaneously. This leads to a better feature learning across groups. Case studies conducted on an open access dataset from Ireland demonstrate the effectiveness and superiority of the proposed framework.

Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations

Abstract There is a knowledge gap about how the load modeling of smart grid affects the accuracy of the analytical reliability evaluation process and its calculation time. This paper tries to fill such a research gap by contributing to developing a methodology which is used to quantify the time reduction of the smart grid reliability assessment provided by various load state reduction strategies. In this paper, four novel load state reduction strategies (the aggregation of identical load states and time/magnitude/time of use pricing-dependent ones) are developed. It is useful to determine the appropriate number of load states according to the trade-off between the computing time and calculation accuracy. The sensitivity analyses are performed to get insight into how the load modeling of smart grids is affected due to various DG technology scenarios and their different penetration levels. To illustrate the advantages of the proposed method, it is applied to IEEE 69-bus test system. The test results imply that a significant time reduction of the reliability assessment process has been achieved while the desired accuracy criteria have been satisfied.

BAP: A Batch and Auditable Privacy Preservation Scheme for Demand Response in Smart Grids

Advancing network technologies allow the setup of two-way communication links between energy providers and consumers. These developing technologies aim to enhance grid reliability and energy efficiency in smart grids. To achieve this goal, energy usage reports from consumers are required to be both trustworthy and confidential. In this paper, we construct a new data aggregation scheme in smart grids based on a homomorphic encryption algorithm. In the constructed scheme, obedient consumers who follow the instruction can prove their data consumption's adjustment by using a range proof protocol. Additionally, we propose a new identity-based signature algorithm in order to ensure authentication and integrity of the constructed scheme. By using this signature algorithm, data usage reports are verified in real time. Extensive simulations demonstrate that our scheme outperforms other data aggregation schemes.

Big Data Acquisition Under Failures in FiWi Enhanced Smart Grid

With the increasing of monitoring devices and advanced measurement infrastructures, smart grids (SGs) collect large amounts of data every moment, which gives rise to the SG big data. In order to fulfill diverse communication requirements of various energy-related data in SG, it is obviously impractical to rely on a single communication technology, and a hybrid communication architecture of low-latency fiber optic and cost-effective wireless technologies could be a promising solution. Note that, low-latency data acquisition under failures is of particular importance to SG reliability, considering that SGs are vulnerable to various failures. Toward this end, we provide in this paper a hybrid SG communication architecture integrating fiber optic and WiFi-based mesh networks, i.e., fiber-wireless (FiWi) enhanced SG, and study the problem of data acquisition under failures in FiWi enhanced SG. The problem is first formulated as a constrained optimization problem, and then three algorithms are proposed as our solutions, i.e., an optimal enumeration routing algorithm (OERA), a greedy approximation routing algorithm (GARA), and a heuristic greedy routing algorithm (HGRA). Numerical results reveal that both GARA and HGRA can achieve near-optimal solutions to the problem of data acquisition under failures, and have higher computational efficiency compared to our benchmark, i.e., OERA.

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers’ comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.

Efficiency Network Construction of Advanced Metering Infrastructure Using Zigbee

An advanced metering infrastructure (AMI) consists of a meter data management system, a concentrator, and a meter. Each component can communicate with all other components, through either wired or wireless connections. In an AMI local area networks, various protocols, such as Zigbee, WiFi, and power-line communication, have the potential to be used in AMI communication. Of these protocols, Zigbee offers self-constructing networks, low costs, and superior safety levels. Zigbee is adopted as the LAN communication protocol because of these advantages in this study. In a LAN, a concentrator may encounter many emergent situations, any of which might cause the concentrator to lose its network connection function. Consequently, additional time is required for network construction, which tends to reduce the reliability of smart grids with LANs of this type. To solve these issues, this paper proposes the fast join process (FJP) and enhanced FJP to shorten the time of Zigbee network construction. The strategy employed in this paper is to let every meter join a specified concentrator. The proposed FJP does not scan unnecessary channels. By simplifying association message exchange, we further propose a new method called the enhanced FJP. By using these methods, not only can message flow be managed easily, but the time of network construction can also be improved.

A Novel Scheme for Improving the Reliability in Smart Grid Neighborhood Area Networks

A smart grid, as its name implies, is an intelligent grid, which features two-way transmission for electricity and information. A smart grid can deliver various types of electrical services and streamlined, highly efficient energy consumption, but only if its communication systems are highly reliable. A smart grid can be based on wireless networks, which offer high speed and low cost, but problems with wireless technology can impair communications performance and destabilize the smart grid. Wireless local area network (LAN) mesh networks based on IEEE 802.11s can serve as the backbones of smart grids. IEEE 802.11s provides flexible, scalable, high-speed communications with low installation and management costs. In this paper, we first describe IEEE 802.11s, default routing protocol, Hybrid Wireless Mesh Protocol (HWMP); Even though HWMP has several shortcomings that destabilize smart grids, we propose a novel scheme to improve smart grid routing reliability. We present a simulation of our proposed scheme using ns-3 simulation software, and prove that our method can improve smart grid reliability.

Review on smart grid control and reliability in presence of renewable energies: Challenges and prospects

This paper deals with smart grid concept and its reliability in presence of renewable energies. Around the globe an adjustment of electric energy is required to limit CO2 gas emission, preserve the greenhouse, limit pollution, fight climate change and increase energy security. Subsequently renewable energy expansion is the real test for designers and experts of smart grid system. This initiative has made significant progress toward the modernization and growth of the electric utility infrastructure and aims to integrate it into today’s advanced communication era, both in function and in architecture. The study is focused on the difference between a conventional grid and a smart grid concept and the integration of renewable energy in a smart grid system where grid control is a must for energy management. Assuring a good grid reliability, taking the right control measures in order to preserve continuous electricity supply for the customers are challenges highlighted in the present paper.

Shunt Capacitor Banks Online Monitoring Using a Superimposed Reactance Method

A new indicating quantity, named superimposed reactance (SR), is presented in this paper to determine failed capacitor elements’ involved phase and number in shunt capacitor banks (SCBs). The proposed quantity is estimated using available measurements to the unbalance protection function of SCBs numerical protective relays. The proposed SR adopts calibrating factors for element failures online monitoring and can provide live report of the number of failed capacitor elements. The proposed method applications are: faster identifying SCBs faulty units, for fuseless and internally fused designs, and planning preventive/scheduled maintenance for all types of unit designs including externally fused units. With integration of these applications into protection intelligent electronic devices that provide captured and time-tagged event records via communication protocols for HMIs and SCADA systems, outage times would be reduced and smart grids reliability would be improved. The developed algorithm supports three different grounding arrangements, Wye-ungrounded, Wye-grounded via a low ratio current transformer, and Wye-grounded via a grounding capacitor at the SCB neutral point. Comprehensive simulation and fault-location sensing in PSCAD and MATLAB have verified the proposed algorithm performance. Advantages of the proposed method reports over conventional unbalance relaying alarms are also demonstrated using a commercial relay test results comparison.

On Reliability Analysis of Smart Grids under Topology Attacks

Building an efficient, smart, and multifunctional power grid while maintaining high reliability and security is an extremely challenging task, particularly in the ever-evolving cyber threat landscape. The challenge is also compounded by the increasing complexity of power grids in both cyber and physical domains. In this article, we develop a stochastic Petri net based analytical model to assess and analyze the system reliability of smart grids, specifically against topology attacks under system countermeasures (i.e., intrusion detection systems and malfunction recovery techniques). Topology attacks, evolving from false data injection attacks, are growing security threats to smart grids. In our analytical model, we define and consider both conservative and aggressive topology attacks, and two types of unreliable consequences (i.e., system disturbances and failures). The IEEE 14-bus power system is employed as a case study to clearly explain the model construction and parameterization process. The benefit of having this analytical model is the capability to measure the system reliability from both transient and steady-state analysis. Finally, intensive simulation experiments are conducted to demonstrate the feasibility and effectiveness of our proposed model.

Evaluating smart grid reliability based on impacts of cyber (control, monitoring and protection) network and its different topologies

The modern smart grid involves two distinct and intricate cyber-power networks. Cyber networks are employed to protect, monitor and control different types of physical systems. Various topologies of the cyber network affect reliability of the power grid. In conventional methods, it is supposed that the cyber components are failure free, but this simplification has errors and is not precise. So, in this paper, a novel analytical method is proposed to evaluate the reliability indices considering cyber-power interdependencies. The introduced method evaluates the smart grid reliability under failure of power and cyber elements and uncertainties of renewable resources such as wind distributed generation. Also, a new procedure is proposed based on changing structure of cyber topology. The proposed method is applied to a realistic distribution network in Iran. The reliability indices of each configuration are calculated. The test results indicate that it is vital to consider cyber effects on power grid in reliability assessment of smart grid as well as power effects. Cyber network properties and topologies which are important are investigated.

On Stability and Robustness of Demand Response in V2G Mobile Energy Networks

Demand response (DR) plays a significant role in enhancing the reliability of future smart grids. Electric vehicles (EVs) can be exploited to facilitate DR because their batteries, as a form of flexible energy storage, can be controlled to consume energy from or feed energy back to the grid depending on user needs. However, EVs' mobility is inherently probabilistic, which presents a challenge for system stability particularly. This paper analyzes the stability of DR in which mobile EVs participate. Using the methodology of dynamical complex networks, we present a DR model of vehicle-to-grid mobile energy network in which the EVs generally move across different districts represented as network nodes. EV fleets, therefore, transport energy and energy storage capacity among these nodes in general. A difference equation system is developed to model the DR dynamics of the nodes, which mutually affect each other. A DR algorithm is proposed to control the demand for EV charging and discharging. It is proved that the stability of the algorithm is robust to internode coupling. Numerical results show that incoming EVs that bring new energy and storage into a district can impact the DR stability. Real-world traces of vehicle mobility are used in simulations to illustrate the DR model.

Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations.

Supporting a conventional power grid with advanced communication capabilities is a cornerstone to transferring it to a smart grid. A reliable communication infrastructure with a high throughput can lay the foundation towards the ultimate objective of a fully automated power grid with self-healing capabilities. In order to realize this objective, the communication infrastructure of a power distribution network needs to be extended to cover all substations including medium/low voltage ones. This shall enable information exchange among substations for a variety of system automation purposes with a low latency that suits time critical applications. This paper proposes the integration of two heterogeneous wireless technologies (such as WiFi and cellular 3G/4G) to provide reliable and fast communication among primary and secondary distribution substations. This integration allows the transmission of different data packets (not packet replicas) over two radio interfaces, making these interfaces act like a one data pipe. Thus, the paper investigates the applicability and effectiveness of employing heterogeneous wireless networks (HWNs) in achieving the desired reliability and timeliness requirements of future smart grids. We study the performance of HWNs in a realistic scenario under different data transfer loads and packet loss ratios. Our findings reveal that HWNs can be a viable data transfer option for smart grids.