Smart Grid Assets Research Articles

Distributed robust [formula omitted]asso-[formula omitted] based on [formula omitted]ash optimization for smart grid: Guaranteed robustness and stability

The integration of variable renewable energy supplies into smart grid energy management poses several obstacles to system operation. An efficient solution for resource management is essential to ensuring reliable operation. This research presents distributed robust Lasso-model predictive control (D − RLMPC) as a way to handle energy problems in a multi-layer and multi-time frame optimization method. The D − RLMPC is a hierarchical system that integrates a centralized supervisory management (SM) layer for long-term optimization with a distributed coordination management (CM) layer for short-term adaptation to high power fluctuations. The higher layer, known as the SM, is responsible for providing the grid operator with specific operating plans and offering guidance to the bottom layer, known as the CM. The CM is responsible for coordinating the interaction between the centralized optimization goals and the physical power system layer. Furthermore, a distributed extended Kalman filter (DEKF) is used to ascertain the inter-dependencies among subsystems. Next, an iterative approach based on Nash optimization is proposed to get the globally optimum solution of the whole system in a partly distributed manner. The simulation results demonstrate the effectiveness of the proposed control approach, which combines the advantages of centralized and distributed control to provide a comprehensive solution for the grid operating issue. To verify and assess the effectiveness of the suggested approach, the acquired outcomes are compared to those of the centralized robust, distributed robust, and distributedMPC approaches. The simulation findings confirm the practicality of using the suggested system to manage future smart grid assets.

Role of Voltage Control Devices in Low Voltage State Estimation Process

Megatrends, such as the proliferation of distributed generation, electrification, and the appearance of aggregator companies, put the low voltage power grids under intense pressure. Since the network infrastructure developments cannot keep up with the trends, distribution system operators turned to alternative solutions. Smart grid assets, such as on-load tap-changing distribution transformers or serial low voltage regulators, are promising solutions. However, the energy transition cannot be handled with the network expansive on the distribution level. Control centers are predicted to expand to this voltage level in the near future, and distribution system state estimation could be an enabler of all functionalities. On the low voltage level, data scarcity is a great challenge in observability; therefore, research must focus on the creation of pseudomeasurements and integration of available data sources. This paper examines the inclusion of smart assets from the conceptual point to the application on two sites, based on data from operational environments, both with a pseudomeasurement and an integrated metering point approach. The results showed that integrating smart assets could considerably mitigate voltage fluctuations, and reduce estimation errors by two magnitudes on the low voltage network.

Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications

High Temperature Superconducting (HTS) Magnetic Energy Storage (SMES) devices are promising high-power storage devices, although their widespread use is limited by their high capital and operating costs. This work investigates their inclusion in smart grids when used in tandem with hydrogen fuel cells and other energy storage devices using a novel two-stage model. The first stage presents a stochastic allocation algorithm to optimally size the smart grid assets with the maximum expected profit. The next stage models the time system evolution with these optimal capacities using a dynamic Monte Carlo model to investigate the system reliability, capacity credit and loss of load expectation. A novel energy management algorithm is also presented that maximises the time with which a fleet of energy storage devices can fulfil a stochastically unknown power request. The analysis shows that from a purely economic standpoint, HTS SMES and hydrogen energy do not achieve high penetration levels (up to 10 %) due to their high costs, although the penetration levels can be improved with higher power imbalance penalties in day ahead markets. The novel capacity credit and reliability investigation shows that they can improve the reliability and capacity credit of wind turbines by approximately 30 %, thereby improving potential wind penetration levels. The optimal energy management algorithm improves the fleet lifespan and reliability by up to 9 % depending on the connected capacity.

Smart Grid Asset Monitoring and Management through Blockchain Technology

The smart grid is an electrical network built on digital technology that transmits power to users in both directions. The system enables monitoring, analysis, control, and communication throughout the supply chain to enhance efficiency, lower energy usage and expenses, and increase the supply chain’s transparency and dependability. Here, we used a number of IOT devices to obtain meter readings and related information and send them to the control center for processing the data and adding them to the blockchain ledgers. During the transmission of data from IoT devices to the control center, the data can be tampered. This can be prevented using a blockchain. Identification and communication will also be implemented to maintain confidentiality and reliability which can be attained through ABE. We will outline a architecture for a blockchain-based platform for managing smart grids that include tamper-proof registration and energy data metering. We used the blockchain to manage transactions in the smart grid

SDN-Based Resilient Smart Grid: The SDN-microSENSE Architecture

The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.

Cyber Risks to Critical Smart Grid Assets of Industrial Control Systems

Cybersecurity threats targeting industrial control systems (ICS) have significantly increased in the past years. Moreover, the need for users/operators to understand the consequences of attacks targeting these systems and protect all assets is vital. This work explores asset discovery in ICS and how to rank these assets based on their criticality. This paper also discusses asset discovery and its components. We further present existing solutions and tools for asset discovery. We implement a method to identify critical assets based on their connection and discuss related results and evaluation. The evaluation utilises four attack scenarios to stress the importance of protecting these critical assets since the failure to protect them can lead to serious consequences. Using a 12-bus system case, our results show that targeting such a system can increase and overload transmission lines values to 120% and 181% MVA, which can affect the power supply and disrupt service, and it can increase the cost up to 60%, affecting the productivity of this electric grid.

Reliable and QoS aware routing metrics for wireless Neighborhood Area Networking in smart grids

Smart Grid networks use data collected from sensors in evaluating the current system state to make optimal resource distribution decisions and facilitate two-way energy exchange. Efficient and timely communication between smart grid assets using high efficiency and reliable Neighborhood Area Network (NAN) for the last-mile can play a significantly important role. For this requirement, wireless mesh networking can be used to offer scalable, flexible and resilient wireless connectivity. Despite significant advantages and tremendous applications, smart grid NANs are vulnerable to security and reliability issues during electric power delivery. After being compromised by an adversary, cascading-failure-induced disasters may disrupt the power grid from a remote location; customer’s private information may become vulnerable if accessed illegally; the adversary may also compromise selected nodes (change the normal behavior of a node to malicious behavior) and thus fail the critical mission of the Supervisory Control and Data Acquisition (SCADA) system. Moreover, the Enhanced Distributed Channel Access (EDCA) mechanism of IEEE 802.11e wireless technology can be leveraged for service differentiation based routing among flows of differing priorities. Routing decisions in mesh networks are based on routing metrics which have been the focus of research for the past decade. However, NANs offer a different implementation scenario from traditional mesh infrastructures and also necessitate the need for differentiated quality of service. In this paper, we discuss how state-of-the-art routing metrics have limitations for the NAN scenario, especially for EDCA based differentiated access. Moreover, we propose and design a secure and EDCA based contention-aware routing approach - iRoute, for 802.11e based wireless NANs which enables the selection of reliable routes that also satisfy QoS requirements (low contention, high throughput routes). Simulation-based performance evaluation of the proposed approach shows that it can provide significantly better performance in terms of throughput and delay for NAN communication.

Research on Benefit Evaluation Method for Smart Distribution Grid Projects Based on Improved QPA

[Introduction] The comprehensive benefits of smart grid projects are the key factors influencing the formulation of technical solutions and investment decisions. Therefore, this paper proposes a new method for quantitative assessment of comprehensive benefits based on improved QPA, aiming at the shortcomings of existing methods, including the narrow scope of application, failed in differentiate between stakeholders, and distinguishing between current realizable returns and long-term potential earnings. Methods By analyzing the asset composition of a smart grid project, the cost, functions and corresponding benefits of various types of smart grid assets and relevant parties were mapped in the proposed method, and benefits and costs in accordance with relevant parties and cashing cycles were classified. Ultimately, a cost-benefit analysis of a smart grid project was conducted in accordance with the internal operational mechanisms of sub-systems and overall projects and external market mechanisms. Results This method can be used to quantify more comprehensive and effective multi-scale smart grid projects. Conclusion Based on practical engineering examples, it is shown that the proposed method is accurate and effective, and provides method support for the decision-making of project investment operation mode and technical solution.

Delay-Aware MAC Protocol for Wireless Sensor and Actor Networks in Smart Grid

The conventional electric grid is converting in to the new emerging smart grid by utilizing information and communication technologies. The smart grid allows both the utilities and consumers to monitor and manage energy consumption effectively with the help of wireless sensor and actor networks (WSANs). WSANs have large number of low-power, low-cost, and multi-functional wireless sensor and actor nodes. The wireless sensor nodes collect the data from the physical condition of environment, while actor nodes perform different tasks according to the application requirements. For the monitoring and controlling of smart grid assets, WSANs can be considered as potential tools having the capability of low cost, high latency, and flexibility. In smart grid environment, WSANs have the problem of delay in channel access due to packet collisions. In this research work, we compare the performance results of various medium access control protocols in order to analyze which one among them is the best with respect to better throughput and minimum delay for WSANs in the smart grid environment.

Data-Centric Hierarchical Distributed Model Predictive Control for Smart Grid Energy Management

The smart grid energy management with variable renewable energy resources presents many challenges to the grid operation. An optimized solution to manage the available resources is necessary to achieve reliable operation. This paper presents the hierarchical distributed model predictive control (HDMPC) to solve the energy management problem in the multitime frame and multilayer optimization strategy. The HDMPC combines the concept of enabling the optimization over long time-horizon for a centralized supervisory management (SM) layer and another short time-horizon during high-power variability for a distributed coordination management (CM) layer. The information exchange and interoperability between different layers are provided through the data-centric communication approach. The SM (upper layer) works to present the grid operator with certain operational plans and gives the guidelines to the CM (lower layer). The CM has the responsibility to coordinate the relationship between the centralized optimization objectives and the physical power system layer. The proposed HDMPC control was verified both numerically and experimentally. The obtained simulation results show that the control strategy proposed here is successful and combines the benefits of both the centralized and distributed control for a global solution of the grid operation problem. The experimental results demonstrate the feasibility of the real-time implementation of the proposed system for deployment to control future smart grid assets.

Wireless Sensor Network Based Smart Grid Communications: Cyber Attacks, Intrusion Detection System and Topology Control

The existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems that are deployed to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

QoS-Aware and Load-Balance Routing for IEEE 802.11s Based Neighborhood Area Network in Smart Grid

Monitoring and transforming smart grid (SG) assets in a timely manner is highly desired for emerging smart grid applications. This critically requires the design of a neighborhood area network (NAN) which is capable of providing high-efficiency and reliable two-way last mile communication from meters to other SG domains. For this demand, IEEE 802.11s based wireless mesh network (WMN) is anticipated to be utilized in a NAN as it can provide high scalability, high-speed and cost-effective wireless transmission. In this paper, we propose a NAN QoS-aware and load-balance routing scheme (NQA-LB) based on the default hybrid wireless mesh protocol (HWMP) of IEEE 802.11s, which aims to address multiple QoS requirements from different NAN applications, and guarantee the highly reliability transfer of NAN traffic data towards gateway. With the NQA-LB, various QoS requirements can be satisfied through sufficient differentiated services as well as network congestion is mitigated by achieving load balance between multiple transmission paths. In order to improve the reliability of NQA-LB, we present an EDCA based adaptive priority adjustment scheme, called AP-EDCA, which dynamically adjusts packet's priority to increase the throughput under low load condition and to mitigate the collision under heavy load condition to improve the reliability of applications with high QoS requirements. Extensive simulation experiments demonstrate the superiority of the proposed scheme in terms of packet delivery ratio, end-to-end delay and throughput while satisfies various QoS requirements much better at the same time.

Strategic Valuation of Smart Grid Technology Options in Distribution Networks

The increasing penetration of renewable distributed generation (DG) sources in distribution networks can lead to violations of network constraints. Thus, significant network reinforcements may be required to ensure that DG output is not constrained. However, the uncertainty around the magnitude, location, and timing of future DG capacity renders planners unable to take fully informed decisions and integrate DG at a minimum cost. In this paper, we propose a novel stochastic planning model that considers investment in conventional assets as well as smart grid assets such as demand-side response, coordinated voltage control and soft open points. The model also considers the possibility of active power generation curtailment of the DG units. A node-variable formulation has been adopted to relieve the substantial computational burden of the resulting mixed integer nonlinear programming problem. A case study shows that smart technologies can possess significant strategic value due to their inherent flexibility in dealing with different system evolution trajectories. This latent benefit remains undetected under traditional deterministic planning approaches which may hinder the transition to the smart grid.

Delay Critical Smart Grid Applications and Adaptive QoS Provisioning

Wireless sensor networks (WSNs) are anticipated to be widely adopted in the various monitoring and control applications due to their versatility and low cost. One of the most promising and emerging WSNs applications is their use in monitoring smart grid assets. Although WSNs can provide cost efficient and reliable solutions, they are not suitable for delay critical application, because they were initially designed for low data rate applications and they may be challenged when sudden faults or failures occur in the monitored environments. Therefore, to prevent extensive delays of critical data, appropriate quality of service (QoS) techniques should be used. In this paper, we present an adaptive QoS scheme (AQoS) and an adaptive guaranteed time slot (AGTS) allocation scheme for IEEE 802.15.4-based WSNs used in high traffic intensity smart grid monitoring applications. Both AQoS and AGTS schemes can adaptively reduce the end-to-end delay and flexibly tune the GTS to provide the required QoS differentiation to delay critical smart grid monitoring applications.

Distributor pricing approaches enabled in Smart Grid to differentiate delivery service quality

Industry practitioners who advocate retail competition and Demand-side Participation now look for approaches to link both initiatives through distributor pricing. As distributors incrementally convert more traditional assets into Smart Grid assets, they also need to consider different pricing appro

Priority- and Delay-Aware Medium Access for Wireless Sensor Networks in the Smart Grid

Monitoring smart-grid assets in a timely manner is highly desired for emerging smart-grid applications such as transformer monitoring, capacitor bank control, plug-in hybrid-electric-vehicle load management, and power quality assessment. Wireless sensor and actor networks (WSANs) are anticipated to be widely utilized in a wide range of smart-grid applications due to their numerous advantages along with their successful adoption in various critical areas including military and health. For resource-constrained WSANs, transmitting delay-critical data from smart-grid assets calls for data prioritization and delay responsiveness. In this paper, we introduce two medium-access approaches, namely, delay-responsive cross-layer (DRX) data transmission and fair and delay-aware cross-layer (FDRX) data transmission, which aim to address the delay and service requirements of smart grids. DRX is based on delay-estimation and data-prioritization steps that are performed by the application layer, in addition to the MAC layer parameters responding to the delay requirements of the smart-grid application and the network condition. On the other hand, FDRX incorporates fairness into DRX by preventing a few nodes from dominating the communication channel. We provide a comprehensive performance evaluation of those approaches. We show that DRX reduces the end-to-end delay while FDRX has lower collision rate compared with DRX. We outline the tradeoffs regarding these approaches and draw future research directions for robust communication protocols for smart-grid monitoring applications.

Smart grid monitoring with service differentiation via EPON and wireless sensor network convergence

Passive Optical Networks (PON) are recognized as a fundamental component of high-speed network access, and emerge as a solid networking solution for the smart grid. Smart grid aims to incorporate two-way communications between the customers and the utility, for the purposes of advanced monitoring and intelligent control of demand and supply of electricity. At the user-end, Wireless Sensor Networks (WSN) are ideal tools for energy consumption monitoring at residential premises, as well as, event and ambient monitoring at substations, power lines and vaults. In this paper, we target a seamless integration of WSN technology with PONs to provide Quality of Service (QoS) to both the data collected by billions of sensors and to the Fiber-To-The-Home/Building/Curb (FTTX) users in the smart grid environment. We present the design of a Fiber-Wireless Sensor Network (Fi-WSN) gateway where data prioritization is fundamental since ambient data collected from a home will have lower priority than an alarm generated at a smart grid asset. We show that the proposed gateway design is able to offer low delay for high priority packets while maintaining the delay of FTTX traffic and the reliability of the WSN at desired levels under various loads, concentrations of service levels and aggressive/non-aggressive bursting mechanisms.