Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing
Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing
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DOI：
10.1109/globecom48099.2022.10001173
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Ashutosh Timilsina;S. Silvestri
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文献类型：
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作者：
Ashutosh Timilsina;S. Silvestri
分布式能源的广泛采用和智能电网技术的出现，使得传统的被动电力系统用户能够积极参与能源交易。认识到传统的集中式电网驱动的能源市场为这些用户提供的盈利能力很小，最近的研究已经将重点转移到分散的点对点（P2P）能源市场。在这些市场中，用户相互交易能源，比向电网购买或出售能源的收益更高。然而，大多数P2P能源交易的研究在很大程度上忽略了交易过程中的用户感知，假设恒定的可用性，参与性和完全合规性。因此，这些方法可能会导致消极的态度，并随着时间的推移减少参与。在本文中，我们设计了一个自动化的P2P能源市场，考虑到用户的感知。我们采用前景理论建模的用户感知和制定一个优化框架，以最大限度地提高买方的感知，同时匹配的需求和生产。鉴于优化问题的非线性和非凸性，我们提出了基于差分进化的能量交易算法DEbATE。此外，我们引入了一个风险敏感的Q学习算法，命名为定价机制与Q学习和风险敏感性（PQR），学习卖方的最优价格考虑他们的感知效用。基于能源消耗和生产的真实的痕迹以及现实的前景理论函数的结果表明，与最近的最先进的方法相比，我们的方法为买家实现了26%的感知价值，为卖家提供了7%的奖励。
The widespread adoption of distributed energy resources, and the advent of smart grid technologies, have allowed traditionally passive power system users to become actively involved in energy trading. Recognizing the fact that the traditional centralized grid-driven energy markets offer minimal profitability to these users, recent research has shifted focus towards decentralized peer-to-peer (P2P) energy markets. In these markets, users trade energy with each other, with higher benefits than buying or selling to the grid. However, most researches in P2P energy trading largely overlook the user perception in the trading process, assuming constant availability, participation, and full compliance. As a result, these approaches may result in negative attitudes and reduced engagement over time. In this paper, we design an automated P2P energy market that takes user perception into account. We employ prospect theory to model the user perception and formulate an optimization framework to maximize the buyer's perception while matching demand and production. Given the non-linear and non-convex nature of the optimization problem, we propose Differential Evolution-based Algorithm for Trading Energy called DEbATE. Additionally, we introduce a risk-sensitive Q-learning algorithm, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), which learns the optimal price for sellers considering their perceived utility. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approach achieves a 26% higher perceived value for buyers and generates 7% more reward for sellers, compared to a recent state of the art approach.