An instrumentation engineer’s review on smart grid: Critical applications and parameters

Bhatt, Jignesh; Shah, Viral B.; Jani, Omkar

doi:10.1016/j.rser.2014.07.187

Cited by 69 publications

(20 citation statements)

References 76 publications

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“…Purely SG focused surveys have presented general overviews of the SG concept [11], [76]- [79] as well as the general communications needs and applications in the SG [80]- [82] and SG communication architectures [2], [83]- [88]. Mechanisms for data collection and networking of SG sensors have been surveyed in [89], while information management strategies have been reviewed in [90].…”

Section: Review Of Related Survey Articlesmentioning

confidence: 99%

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Khan

Rehmani

Reisslein

2016

IEEE Commun. Surv. Tutorials

307

145

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Traditional power grids are currently being transformed into Smart Grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of highspeed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive Radio Networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.Index Terms-Advanced metering infrastructure (AMI), Communication architecture, Cognitive radio network (CRN), Smart grid (SG).1553-877X (c) 2 to license holders on a long-term basis for large geographical regions. This fixed spectrum allocation can result in inefficient spectrum utilization. Cognitive radios (CRs) operating on the principle of Dynamic Spectrum Access (DSA) may be a suitable option to solve these spectrum inefficiency problems [18]. CRs use the existing spectrum through opportunistic access to the licensed bands without interfering with the licensed users. CRs determine the spectrum portions unoccupied by the licensed users known as spectrum holes or white spaces, and allocate the best available channels for CR communication [19], [20].After identification of available spectrum holes, the spectrum utilization can be improved through joint spatial and temporal spectrum sharing. SG applications, especially demand response management, can benefit from joint spatial and temporal spectrum sharing [21]. Machine-to-machine (M2M) communication is an emerging paradigm for providing ubiquitous connectivity between devices to communicate autonomously without human intervention. However, a high number of connected devices can heavily congest the spectrum in existing communication networks. CR technology may be a suitable option for mitigating the potential spectrum shortage arising from M2M communications in the SG [22]. B. Contribution of this Survey ArticleWhile a few overview articles have outline some selected aspects of CR communication in the SG [23]- [25], to the best of our knowledge, there is no prior survey that comprehensively covers CR-based...

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Section: Review Of Related Survey Articlesmentioning

confidence: 99%

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Khan

Rehmani

Reisslein

2016

IEEE Commun. Surv. Tutorials

307

145

View full text Add to dashboard Cite

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“…Teknologi, PETRONAS, Seri Iskandar, 32160, Perak, Malaysia non-fixed pricing [3]. To achieve this efficiently, the DR mechanism of the SG must be upgraded.…”

Section: Department Of Electrical and Electronics Engineering Univermentioning

confidence: 99%

“…In the deregulated power industry, the very short term prediction of energy demand (i.e. anywhere from an hour to a few hours ahead) allows the scheduling of dynamic electricity tariffs and non‐fixed pricing . To achieve this efficiently, the DR mechanism of the SG must be upgraded.…”

Section: Introductionmentioning

confidence: 99%

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

Islam

Baharudin

Nallagownden

2017

IEEJ Transactions Elec Engng

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For the assessment of 1‐h‐ahead electrical energy demand, this paper presents an improved backpropagation neural network that has been integrated with a simulated annealing algorithm and a chaos search genetic algorithm. A self‐adaptive learning rate and modified momentum factor are suggested to enhance the performance of the traditionally used backpropagation algorithm. For the combination scheme, the initial parameters of the improved backpropagation neural network have been modified through the utilization of the global search capability of the genetic algorithm. This has been enhanced by cat chaotic mapping to increase the genetic algorithm's optimization ability. The strong local search feature of the simulated annealing algorithm has been used to further enhance the solution set that was created by the optimized genetic algorithm. The model is tested for small and large‐sized grid data, integrated with renewable sources. The performance of the model is also verified for fluctuating load demand conditions. Furthermore, the proposed model performance is tested for all the four seasons of the year to validate its efficacy during seasonal variations. The results of the proposed technique in all these scenarios show higher prediction accuracy and fast convergence than the existing methods. The acceptable precision of 1‐h‐ahead load forecast and its adaptation in different load demand conditions determine the usefulness of the proposed model in the modern deregulated power industry. In particular, the model can be effectively implemented for the enhancement of demand response and other dynamic features of the smart grid. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Rather, scaling up involves many interdependencies and often features trade-offs between systemic gains and localised losses (see Frantzeskaki andLoorbach 2010, Silvast 2017), including to democratic participation. We argue that for infrastructural change to work towards objectives like climate change mitigation, the systemic must be aligned with the local across a whole range of applications and parameters before upscaling (see Bhatt et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Smart gridlock? Challenging hegemonic framings of mitigation solutions and scalability

Sareen

Rommetveit

2019

Environ. Res. Lett.

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Urban energy transitions are key components of urgently requisite climate change mitigation. Promissory discourse accords smart grids pride of place within them. We employ a living lab to study smart grids as a solution geared towards upscaling and systematisation, investigate their limits as a climate change mitigation solution, and assess them rigorously as urban energy transitions. Our 18 month living lab simulates a household energy management platform in Bergen. Norway's mitigation focus promotes smart meter roll-out as reducing carbon emissions, by (i) unlocking efficiency gains, and (ii) increasing awareness for demand-side management. We problematise this discourse. Raising awareness encounters intractable challenges for smart grid scalability. Scattered efficiency gains constitute modest increments rather than the substantial change requisite for rapid mitigation. Whereas promissory smart grid discourse overlooks these ground-truthed limits, our findings caution against misplaced expectations concerning mitigation. We contest discursive enthusiasm on smart grids and argue for aligning local and systemic concerns before upscaling to avoid obscuring risks. Scaling up requires understanding and addressing interdependencies and trade-offs across scales. Focus group discussions and surveys with living lab participants who used sub-meter monitors to track real-time household electricity consumption data over an extended period show that technical issues and energy behaviour, as well as political economic and policy structures and factors, pose significant limits to smart grids. Urban strategies for climate change mitigation must be informed by this recognition. Our results indicate that upscaling relies on bottom-up popular acceptance of the salient technical, organisational and standardisation measures, but that measures to improve the democratic legitimacy of and participation in energy transitions remain weak. We highlight limits to smart grids as a standalone urban mitigation solution and call for a sharper focus on accompanying thrust areas for systematisation and scalability, such as renewable energy integration and grid coordination.

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An instrumentation engineer’s review on smart grid: Critical applications and parameters

Cited by 69 publications

References 76 publications

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

Smart gridlock? Challenging hegemonic framings of mitigation solutions and scalability

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