发表机构
Mineral and Energy Economy Research Institute, Polish Academy of Sciences; Escuela Superior Politécnica de Chimborazo (ESPOCH)（波兰科学院矿物与能源经济研究所; 钦博拉索高等理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比住宅光伏社区P2P电力交易的基于规则与基于学习的定价，发现规则定价竞争力强，储能可提升强化学习策略的社区节约量，且SDR形状定价优于乘数参数化。
AI 中文摘要
本文对比了住宅光伏社区中点对点（P2P）电力交易的基于规则和基于学习的定价机制。基于规则的基准包括作为事后分配机制的账单分摊、中间市场价格以及供需比定价。强化学习（RL）公式通过深度Q网络实现，并在基于乘数和可学习SDR形状的定价下进行评估，其中固定参数SDR变体作为非学习对照组。性能通过社区节约量以及补充的财务和运营指标进行评估。在仅含光伏的基础配置中，基于规则的基准优于最佳RL策略。对于仅评估RL策略的含电池储能的场景，最佳RL策略下的社区节约量从734.23欧元增加至978.52欧元。在基于学习的模式和两种配置中，SDR形状定价优于所考虑的乘数参数化。结果表明，在直接比较两类定价的情况下，基于规则的定价仍具有很强的竞争力，储能在此核算方式下显著改善了基于学习的结果，同时家庭间的利益分配仍存在异质性。
英文摘要
This paper compares rule-based and learning-based pricing mechanisms for peer-to-peer (P2P) electricity trading in residential photovoltaic communities. The rule-based benchmarks comprise bill-sharing as an ex post allocation mechanism, the mid-market rate, and supply-demand-ratio pricing. The reinforcement-learning (RL) formulation is implemented through a Deep Q-Network and evaluated under multiplier-based and learnable SDR-shaped pricing, with a fixed-parameter SDR variant as a non-learning control. Performance is assessed through community savings together with complementary financial and operational indicators. In the base PV-only configuration, the rule-based benchmarks outperform the best RL policy. With battery energy storage, evaluated for the RL policies only, community savings under the best RL policy increase from EUR 734.23 to EUR 978.52. Across the learning-based modes and in both configurations, SDR-shaped pricing outperforms the multiplier-based parameterization considered. The results indicate that rule-based pricing remains highly competitive wherever the two families are compared directly, and that storage substantially improves the learning-based outcomes under this accounting, while the distribution of benefits remains heterogeneous across households.
Comments22 pages, 2 figures, submitted to ARTIIS 2026 (Conference on Advanced Research in Technologies, Information, Innovation and Sustainability) https://www.artiis.org/special-sessions/iwet-2026
