The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach

IF 5.4 Q2 ENERGY & FUELS Smart Energy Pub Date : 2024-03-20 DOI:10.1016/j.segy.2024.100138
Sara Golmaryami, Manuel Lopes Nunes, Paula Ferreira
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Abstract

Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households’ electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

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社会学习对消费者参与需求方管理意愿的影响:基于代理的建模方法
实现可持续能源的未来需要清洁、可负担的能源供应以及消费者对能源市场的积极参与。本研究建议使用基于代理的建模方法来评估和模拟能源消费者参与需求侧管理计划的意愿,以解决社会学习效应这一影响能源消费者行为的关键因素。所提出的基于代理的模型模拟了家庭电力消费者之间的互动,研究了如何通过社会环境来鼓励改变用电习惯的意愿,同时考虑到了消费者之间的多样性。葡萄牙的一项调查数据,包括朋友或家人的建议对个人参与需求响应活动意愿的影响问题,都被用来测试所提出的模拟。研究结果表明,社会学习对需求响应的接受度有重大影响,但这种影响的程度因家庭电力消费者的社会经济特征而异。这项研究证实了基于代理的模型是捕捉社会动态和支持电力市场决策的有效方法,为制定消费者参与战略提供了宝贵的见解。
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来源期刊
Smart Energy
Smart Energy Engineering-Mechanical Engineering
CiteScore
9.20
自引率
0.00%
发文量
29
审稿时长
73 days
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