Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Assad, Ussama; Hassan, Muhammad Arshad Shehzad; Farooq, Umar; Kabir, Asif; Khan, Muhammad Zeeshan; Bukhari, S. Sabahat H.; Jaffri, Zain ul Abidin; Oláh, Judit; Popp, József

doi:10.3390/en15062003

Cited by 31 publications

(14 citation statements)

References 320 publications

(249 reference statements)

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“…The current load forecasting methods mainly include traditional regression models represented by ARIMA [3] and machine learning methods represented by deep learning [4]. Meanwhile, demand response has gained popularity currently due to its good performance in load dispatching [5], which helps to enhance the efficiency of the power system [6].…”

Section: Introductionmentioning

confidence: 99%

Two-layer Optimization of Energy Efficiency Based on Provincial Smart Energy Service Platform

Cheng

Zhang²,

Deng³

et al. 2023

J. Phys.: Conf. Ser.

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The study relies on the provincial smart energy service platform of Jiangsu Province, optimizes its accurate prediction module of energy consumption and the energy efficiency improvement module, and proposes an efficiency improvement strategy based on the two modules of the platform. The accurate prediction module adopts the data processing and feature analysis method based on STL-MIC and builds a deep learning framework based on CNN-BiLSTM. The projected predicted data is then input to the energy efficiency improvement module, which employs the method of flexible load scheduling to achieve the best configuration of demand-side resources.

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Section: Introductionmentioning

confidence: 99%

Two-layer Optimization of Energy Efficiency Based on Provincial Smart Energy Service Platform

Cheng

Zhang²,

Deng³

et al. 2023

J. Phys.: Conf. Ser.

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“…Foreseeing these collective activities is critical for developing and operating socially smart grids [11,28]. Whilst computational studies have built-in assumptions of some potential collective behaviour in energy demand scenarios for instance in [29], there has been a lack of empirical or theoretical advances in this direction to date. Social Identity Theory (SIT) provides a useful framework for analysing collective behaviour in the context of crisis or disaster events, including those related to the energy [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A socially intelligent approach to consumers’ collective capabilities in smart grids

Bagheri-Moghaddam,

Oliveira,

Atkins

et al. 2023

2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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There is a pressing need to investigate consumers' social relations within energy systems particularly in the context of socially smart grids at the domestic level. However, no studies to date have categorised or explained how such social relations manifest and what role different consumers play in managing home energy demand. This work provides for the first time, a categorisation of household archetypes through the social relations that govern them. This study draws on mixed data including a large-scale ethno-visual survey and interviews conducted with energy consumers in Glasgow and Bristol, UK. The analysis forms part of a wider study which integrates social identity theory (SIT), practice theory and rhythm-analysis. We primarily focus on insights derived from SIT as an approach to identifying consumers' capabilities in smart energy systems and Home Energy Management (HEM) through a study of social relations. The findings reveal novel perspectives on how social identities shape HEM patterns and how the consequent sociospatial and technical implications play a role in future demand reduction and the development of socially smart grids. The contribution of this study is two-fold; firstly, to demonstrate how prioritising social practices, identities, and rhythm-analysis can lead to novel interventions in smart grids and redefine the roles of the community, and neighbourhoods, and secondly to discuss the policy implications for planning future automated demand management via the acquisition of new socio-spatial insights into how diverse social identities and practices can foster just transitions and equitable energy futures in the UK and beyond.

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“…With the acceleration of urbanisation, the widespread penetration of clean energy and the increasing demand for loads have brought new challenges to the power system's balance of supply and demand [1]. The user's flexible load as a dispatchable resource to participate in demand response is considered an essential way to solve the power supply and demand balance problem.…”

Section: Introductionmentioning

confidence: 99%

Community Flexible Load Dispatching Model Based on Herd Mentality

Jiang

Zeng

et al. 2022

Energies

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In the context of smart electricity consumption, demand response is an important way to solve the problem of power supply and demand balance. Users participate in grid dispatching to obtain additional benefits, which realises a win-win situation between the grid and users. However, in actual dispatching, community users’ strong willingness to use energy leads to low enthusiasm of users to participate in demand response. Psychological research shows a direct connection between users’ herd mentality (HM) and their decision-making behavior. An optimal dispatching strategy based on user herd mentality is proposed to give full play to the active response-ability of community flexible load to participate in power grid dispatching. Considering that herd mentality is generated by the information interaction between users, by calling on some users to share the experience of successfully participating in demand response in the community information center and using the Nash social welfare function to model herd mentality to explore the impact of the user. The analysis of an example shows that the proposed strategy gives full play to the potential of community flexible loads to participate in demand response. When users have similar electricity consumption behavior, the herd mentality can effectively improve users’ enthusiasm to participate in demand response, and the user response effect meets managers’ expectations.

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Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Cited by 31 publications

References 320 publications

Two-layer Optimization of Energy Efficiency Based on Provincial Smart Energy Service Platform

Two-layer Optimization of Energy Efficiency Based on Provincial Smart Energy Service Platform

A socially intelligent approach to consumers’ collective capabilities in smart grids

Community Flexible Load Dispatching Model Based on Herd Mentality

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