| ID | 186449 |
| Title Proper | Optimal dynamic regulation in retail electricity market with consumer feedback and social learning |
| Language | ENG |
| Author | Wang, Pengyu ; DebinFang ; Wang, Shuyi |
| Summary / Abstract (Note) | Effective regulation is critical to advancing electricity retail market reforms to incentivize electricity quality improvements of retail electricity suppliers (REPs) and promote consumer market participation. In addition, with the development of social media, consumer feedback (CF) and social learning (SL) have become more accessible and frequent. However, there is still a research gap on how regulator can use these feedbacks as an aid to optimize dynamic regulation. Therefore, we construct dynamic Stackelberg game and consumer social network models with incomplete information, where the regulator optimizes its strategy to improve regulatory efficiency, and REPs determine electricity product quality to maximize profits. Customers integrate information in social networks to update purchase decisions and provide information feedback, affecting the profits and regulatory, respectively. This paper analyzes the optimal strategy and equilibrium in a variety of scenarios through simulation, and the main conclusions are as follows:(i) CF and SL motivate regulator to play an extremely active role and REPs to focus on continuous improvement of electricity quality. (ii) CF and SL can effectively increase market dynamics, improve regulatory efficiency, enhance social welfare, and curb market blindness. This paper provides decision support for regulator on how to improve regulatory efficiency and use consumer regulation. |
| `In' analytical Note | Energy Policy Vol.168; Sep 2022: p.113148 |
| Journal Source | Energy Policy 2022-09 168 |
| Key Words | Social Learning ; Retail Electricity Market Supervision ; Consumer Feedback ; Dynamic Stackelberg Game ; Reinforcement Learning |