对比了规则与强化学习定价在光伏储能社区中的表现，发现带储能时学习方法更优。
Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities
- 用深度Q网络实现强化学习定价，比较了多种参数化方式
- 有储能时社区节省从734.23欧元增至978.52欧元
- 基于供需比的定价优于乘数法，但规则方法仍具竞争力
本文对比了住宅光伏社区中基于规则和基于学习的点对点（P2P）电力交易定价机制。规则基准包括账单分摊、中间市场价和供需比定价。强化学习（RL）采用深度Q网络实现，评估了基于乘数和可学习的供需比（SDR）形状定价，同时设置固定参数的SDR作为非学习控制。通过社区总节省额及配套的财务与运行指标评估性能。在仅含光伏的基础配置下，规则基准优于最优RL策略；加入电池储能后，最优RL策略的社区节省额从734.23欧元提升至978.52欧元。在所有学习模式及两种配置下，基于SDR形状的定价均优于乘数参数化方式。结果表明，当两类方法直接比较时，规则定价依然具有高度竞争力，且储能显著提升了学习型策略的表现，但收益分配在各家庭间仍不均衡。
原文摘要 · Abstract (English)
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.
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