The impacts of social learning on a real-time pricing scheme in the electricity market

GangCheng Cao, Debin Fang and Pengyu Wang

Applied Energy, 2021, vol. 291, issue C, No S0306261921003639

Abstract: With the global electricity system reform, the Real-time pricing scheme, a representative of the price-based demand response program, is usually adopted as the default rate for electricity retailers. Concurrently, social learning dramatically affects the customers' decisions in marketing due to the prevalence of social media. Hence, it is crucial to evaluate how social learning affects customers consumption alterations under implementation of real-time pricing. This paper proposes an evolutionary model in a monopoly electricity retailing market and studies the end-customer consumption alterations under implementing a real-time pricing by comparing impacts of different rescheduling strategies. We apply a bipartite network to present the dynamic relations between the power provider and consumers, thus transform the consumer behavior alteration problem into a rewiring issue in the network. We demonstrate that social learning among the customers contributes to their utilities by incurring a more massive drop of the average price in real-time pricing schemes and it deteriorates the retailer’s revenue but stabilizes the total demand distributions. Social learning helps power companies balance the demand and supply, and at the same time promotes the dividends of the power market to flow from the side of power sales to consumers. This research provides decision support for consumer behavior and pricing of power retail companies in the context of social learning.

Keywords: Social learning; RTP; Demand response (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2021.116874

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