A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

Shaukat, N.; Ali, Saima; Mehmood, C. A.; Khan, Bilal; Jawad, Muhammad; Farid, U.; Ullah, Zahid; Anwar, Syed Muhammad; Majid, Muhammad

doi:10.1016/j.rser.2017.05.208

Cited by 147 publications

(81 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the operations of traditional power systems and SCADA-enabled functions, the new SG should cover the provision of data access and management directly for end-users. To this end, an AMI system capable of improving the data management for the demand side [37] plays an essential role. As defined by Rahman et al [26], an AMI is an advanced solid-state electronic instrument that can collect time-based electricity data for further information analysis and power management.…”

Section: Smart Grid (Sg)mentioning

confidence: 99%

“…As defined by Rahman et al [26], an AMI is an advanced solid-state electronic instrument that can collect time-based electricity data for further information analysis and power management. In addition to enabling end-users to query the Meter Data Management System (MDMS) for power usage data through the network, the AMI can also perform demand-side power management, which helps to intelligently monitor peak power consumption, adjust the power transmission and distribution plan, and avoid abnormal loads [37][38][39]. The AMI-supported data can be stored in the MDMS and analyzed to serve electricity-related management services such as pricing plans, automatic meter recording, demand response, and power quality management [40].…”

Section: Smart Grid (Sg)mentioning

confidence: 99%

See 1 more Smart Citation

Determining Information Security Threats for an IoT-Based Energy Internet by Adopting Software Engineering and Risk Management Approaches

Chen

Huang

2019

Inventions

View full text Add to dashboard Cite

This paper introduces an information security threat modeling (ISTM) scheme, which leverages the strengths of software engineering and risk management approaches, called I-SERM. The proposed I-SERM scheme effectively and efficiently prioritizes information security threats for IT systems that utilize a large number of sensors, such as Internet of Things (IoT)-based energy systems. I-SERM operations include determining functional components, identifying associated threat types, analyzing threat items, and prioritizing key threats with the use of software engineering tools such as product flow diagrams, use case diagrams, and data flow diagrams. By simultaneously referring to a proposed STRIDE+p matrix and a defined threat breakdown structure with reference score (TBS+r) scheme, the I-SERM approach enables systematic ISTM. To demonstrate the usability of I-SERM, this study presents a practical case aimed at electricity load balancing on a smart grid. In brief, this study indicates a substantive research direction that combines the advantages of software engineering and risk management into a systematic ISTM process. In addition, the demonstration of I-SERM in practice provides a valuable and practical reference for I-SERM application, and contributes to research in the field of information security designs for IoT-based Energy Internet systems. Inventions 2019, 4, 53 2 of 22ISTs, and taking the considerable remedial actions required are important steps that should be taken based on both theoretical and pragmatic consideration.The IoT has become a mainstream of ICT-based technology enabling smarter applications in the fields of facility monitoring and process management (i.e., Industry 4.0), medical areas, etc. IoT technology also makes possible the construction of smart power systems with intelligent energy management applications. Pan et al. [1] designed and implemented an IoT framework for smart energy functions in buildings. Marinakis and Doukas [2] also emphasized the enhancement of IoT-enabled intelligent energy management in buildings. In addition, Ejaz et al. [3] discussed the issue of how to adopt IoT technology to provide efficient energy management in smart cities. Noor-A-Rahim et al. [4] proposed an IoT-based framework to provide reliable communications between renewable energy facilities, remote IoT components, and control centers. These articles indicate that complex IoT-supported systems like smart buildings or cities, with many factors affecting the total energy consumption in different energy application scenarios, have been the focus of much Energy Internet research.Electricity is undoubtedly an indispensable form of energy for modern society. With the trend of rapid urbanization, people have become highly dependent on their use of electricity. As a result, the stability and reliability of the power supply plays a critical role in the operation of modern power systems. In addition to relying on the effective operation of power equipment, an ICT-supported infrastructure platform capable of superviso...

show abstract

Section: Smart Grid (Sg)mentioning

confidence: 99%

Section: Smart Grid (Sg)mentioning

confidence: 99%

Determining Information Security Threats for an IoT-Based Energy Internet by Adopting Software Engineering and Risk Management Approaches

Chen

Huang

2019

Inventions

View full text Add to dashboard Cite

show abstract

“…Thus, it reduced the cost of electricity. The differences between TG and SG are summarized in Table 1 [3]. Data analytics is the phenomenon of dealing with big data obtained from different sources.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids

et al. 2019

View full text Add to dashboard Cite

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.

show abstract

“…Smart grid (SG) technologies are attracting growing attention given their capacity to sustainably manage power using intelligent grids. SG infrastructures require a bi-directional communication to enhance the energy efficiency, reliability and safety of the electricity systems [1]. When exploring SG communication systems, a universal data and network communications protocol is essential to achieve an interoperable environment [2].…”

Section: Introductionmentioning

confidence: 99%

Characteristic Impedance Analysis of Medium-Voltage Underground Cables with Grounded Shields and Armors for Power Line Communication

Zhao¹,

Zhang²,

Wang³

2019

Electronics

View full text Add to dashboard Cite

The characteristic impedance of a power line is an important parameter in power line communication (PLC) technologies. This parameter is helpful for understanding power line impedance characteristics and achieving impedance matching. In this study, we focused on the characteristic impedance matrices (CIMs) of the medium-voltage (MV) cables. The calculation and characteristics of the CIMs were investigated with special consideration of the grounded shields and armors, which are often neglected in current research. The calculation results were validated through the experimental measurements. The results show that the MV underground cables with multiple grounding points have forward and backward CIMs, which are generally not equal unless the whole cable structure is longitudinally symmetrical. Then, the resonance phenomenon in the CIMs was analyzed. We found that the grounding of the shields and armors not only affected their own characteristic impedances but also those of the cores, and the resonance present in the CIMs should be of concern in the impedance matching of the PLC systems. Finally, the effects of the grounding resistances, cable lengths, grounding point numbers, and cable branch numbers on the CIMs of the MV underground cables were discussed through control experiments.

show abstract

A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

Cited by 147 publications

References 133 publications

Determining Information Security Threats for an IoT-Based Energy Internet by Adopting Software Engineering and Risk Management Approaches

Determining Information Security Threats for an IoT-Based Energy Internet by Adopting Software Engineering and Risk Management Approaches

Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids

Characteristic Impedance Analysis of Medium-Voltage Underground Cables with Grounded Shields and Armors for Power Line Communication

Contact Info

Product

Resources

About