Review of demand response under smart grid paradigm

Balijepalli, V. S. K. Murthy; Pradhan, Vedanta; Khaparde, S.A.; Shereef, R. M.

doi:10.1109/iset-india.2011.6145388

Cited by 249 publications

(131 citation statements)

References 61 publications

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“…Combining the results with and without normalization would help to target in a precise manner consumers who exhibit certain characteristics in terms of the shape of the peaks in their consumption pattern curves and levels of consumption (in the case of non-normalized data). This can be valuable information for the application of a demand response program [32]. …”

Section: Interpretation Of the Clustering Resultsmentioning

confidence: 99%

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Melzi

Samé²,

Zayani

et al. 2017

Energies

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Abstract:The large amount of data collected by smart meters is a valuable resource that can be used to better understand consumer behavior and optimize electricity consumption in cities. This paper presents an unsupervised classification approach for extracting typical consumption patterns from data generated by smart electric meters. The proposed approach is based on a constrained Gaussian mixture model whose parameters vary according to the day type (weekday, Saturday or Sunday). The proposed methodology is applied to a real dataset of Irish households collected by smart meters over one year. For each cluster, the model provides three consumption profiles that depend on the day type. In the first instance, the model is applied on the electricity consumption of users during one month to extract groups of consumers who exhibit similar consumption behaviors. The clustering results are then crossed with contextual variables available for the households to show the close links between electricity consumption and household socio-economic characteristics. At the second instance, the evolution of the consumer behavior from one month to another is assessed through variations of cluster sizes over time. The results show that the consumer behavior evolves over time depending on the contextual variables such as temperature fluctuations and calendar events.

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Section: Interpretation Of the Clustering Resultsmentioning

confidence: 99%

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Melzi

Samé²,

Zayani

et al. 2017

Energies

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“…The smart grid technologies and standards are reviewed in [21]. The demand response of the smart grids is reviewed in [22]. These surveys are summarized in Fig.…”

Section: Related Workmentioning

confidence: 99%

Recent advances of the signal processing techniques in future smart grids

Uddin

Ahmad

Qamar

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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IntroductionSmart grid is a network of electric supply that manages power demand in reliable and economic manner by detecting and reacting to local changes in usage. The infrastructure comprises of smart meters, appliances, and resources with a combination of modern technologies like, control, power, instrumentation, and communication. In such a complex scenario, signal processing techniques are essential to understand, plan, design and operate the complex future smart electronic grids [1]. In addition to this, signal processing has wide variety of applications and is becoming an important tool for electric power system analysis. This is due to the fact that measurements retrieved from numerous locations of the grid can be used for data analysis. These measurements can also be used for a variety of issues such as voltage control, power quality and reliability, power system and equipment diagnostics, power system control and protection, etc [2][3][4][5][6].Power quality is one of the main issue of the smart grid research where voltage, current and frequency deviations in the power system are the main concerns of the system operator [7]. The characterization of the incompatibilities caused by these deviations requires an understanding of their principal cause. Other possible aspects that need inspection are the efficient representation of the voltage and current variations in various electrical Abstract Smart grid is an emerging research field of the current decade. The distinguished features of the smart grid are monitoring capability with data integration, advanced analysis to support system control, enhanced power security and effective communication to meet the power demand. Efficient energy consumption and minimum costs are also included in the prodigious features of smart grid. The smart grid implementation requires intelligent interaction between the power generating and consuming devices that can be achieved by installing devices capable of processing data and communicating it to various parts of the grid. The efficiency of these devices is greatly dependent on the selection and implementation of the advance digital signal processing techniques. This paper provides a comprehensive survey on the applications of signal processing techniques in smart grids, plus the challenges and shortcomings of these techniques. Furthermore, this paper also outlines some future research directions related to applications of signal processing in smart grids. Uddin et al. Hum. Cent. Comput. Inf. Sci. (2018) Page 2 of 15 Uddin et al. Hum. Cent. Comput. Inf. Sci. (2018) 8:2 equipment. Moreover, the signal processing of the power patterns leads to better understanding the behavior of these equipment. Continuous monitoring is also required to capture various events and variations. To meet future demands, methods and techniques must be developed to explore the full range of signals derived from the complex interaction between suppliers, consumers and network operators [8]. REVIEWA smart grid performs measurement, monitoring ...

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“…DR is defined as the changes in the use of electricity, for end consumers, of their normal consumption patterns in response to changes in the price of electrical energy over time, or the incentive payments intended to induce a better use of electricity at peak hours [11]. Load management is defined as a set of objectives which aim to control directly or indirectly and/or modify patterns of electricity consumption of various consumers, aiming to reduce peak demand.…”

Section: Analysis Of the Condition Of Demand Responsementioning

confidence: 99%

Energy Efficiency in Smart Grid: A Prospective Study on Energy Management Systems

Loschi¹,

León²,

Iano³

et al. 2015

SGRE

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The term Smart Grid has become a term to represent the benefits of a smart and sophisticated electrical grid, which can meet various social expectations related to sustainability and energy efficiency. The Smart Grid promises to enable a better power management for energy utilities and consumers, to provide the ability to integrate the power grid, to support the development of micro grids, and to involve citizens in energy management with higher levels of responsibility. However, this context comes with potential pitfalls, such as vulnerabilities to cyber-security and privacy risks. In this article, a prospective study about energy management, and exploring critical issues of modeling of energy management systems in a context Smart. Grid is presented along with background of energy management systems. An analysis of the demand response condition is also presented. Finally, the advantages and disadvantages of the implementation of energy management systems, and a comparison with the Brazilian electricity system are discussed.

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Review of demand response under smart grid paradigm

Cited by 249 publications

References 61 publications

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Recent advances of the signal processing techniques in future smart grids

Energy Efficiency in Smart Grid: A Prospective Study on Energy Management Systems

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