Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach

Wang, Ziyang; Făgărăşan, Ioana; Ploix, Stéphane; Calofir, Vasile

doi:10.1016/j.renene.2022.06.004

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…Second, there are implications for computational studies focused on modelling peak load scenarios and smart grid operations by incorporating an empirically and theoretically informed approach to collective social profiling of consumers [43][44][45]. While social science research has explored social dynamics, there has been little empirical detail or methodological guidance on how to do this at a collective scale.…”

Section: Discussionmentioning

confidence: 99%

“…Computational research has approached the collective characterisation of social relations by modelling energy systems drawing on a range of social consumer profiling data, however with little or no consideration of their collective relational character. These approaches include modelling consumer behaviours in demand response scenarios [43], simulating consumer behaviour change related to energy consumption data [44], as well as anticipating consumers' network functionalities in resource allocation and scheduling in smart grids [45,46]. Zeng et al [43] propose a cooperative demand response method, incorporating multi-agent deep reinforcement learning to improve the profitability of Load Serving Entities by considering consumer social profiling and load differences in valuing electricity.…”

Section: B Computational Studieskey Perspectivesmentioning

confidence: 99%

“…Zeng et al [43] propose a cooperative demand response method, incorporating multi-agent deep reinforcement learning to improve the profitability of Load Serving Entities by considering consumer social profiling and load differences in valuing electricity. Simoiu et al [44] demonstrate the impact of personalised recommendations on consumer behaviour and the potential for non-enthusiastic members to become enthusiastic through community engagement. Nair et al [45] suggest the utilisation of multiagent systems in optimising resource allocation and scheduling in smart grids.…”

Section: B Computational Studieskey Perspectivesmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: B Computational Studieskey Perspectivesmentioning

confidence: 99%

Section: B Computational Studieskey Perspectivesmentioning

confidence: 99%

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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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“…Linear Programming [13,27,28,34] Mixed-integer linear programming (MILP) [9][10][11][12]14,16,[18][19][20][21][22][23][24][25]29,32,33,[35][36][37][38] Nonlinear programming (NLP) [15] 4.2.1. Linear Programming (LP)…”

Section: Optimization Technique Referencementioning

confidence: 99%

“…In [38], the willingness of consumers to change their behavior was modeled using a stochastic approach. There was a community manager in charge of making recommendations to consumers to change their consumption patterns.…”

Section: Mixed-integer Linear Programming (Milp)mentioning

confidence: 99%

Optimal Scheduling of Controllable Resources in Energy Communities: An Overview of the Optimization Approaches

2022

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In recent years, there has been a growing interest in the study of energy communities. This new definition refers to a community sharing energy resources of different types to meet its needs and reduce the associated costs. Optimization is one of the most widely used techniques for scheduling the operation of an energy community. In this study, we extensively reviewed the mathematical models used depending on the objectives and constraints considered. The models were also classified according to whether they address uncertainty and the inclusion of flexibility constraints. The main contribution of this study is the analysis of the most recent research on the mathematical formulation of optimization models for optimal scheduling of resources in energy communities. The results show that the most commonly used objectives are profit maximization and cost minimization. Additionally, in almost all cases, photovoltaic generation is one of the main energy sources. Electricity prices, renewable generation, and energy demand are sources of uncertainty that have been modeled using stochastic and robust optimization. Flexibility services using demand response are often modeled using interruptible loads and shiftable loads. There is still considerable room for further research on the distribution of benefits among the participants of the energy community and the provision of flexibility services to the electricity grid.

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A Recommendation Strategy Proposal for an Energy Community Modeled as a Multi-agent System

Simoiu

Făgărăşan

Ploix

et al. 2023

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

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Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach

Cited by 14 publications

References 33 publications

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

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

Optimal Scheduling of Controllable Resources in Energy Communities: An Overview of the Optimization Approaches

A Recommendation Strategy Proposal for an Energy Community Modeled as a Multi-agent System

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