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Reinforcement learning and rule-based pricing compared for P2P electricity trading

By PulseAugur Editorial · [1 sources] ·

This paper explores two pricing mechanisms for peer-to-peer electricity trading in residential photovoltaic communities: rule-based and reinforcement learning (RL) based. The rule-based methods include bill-sharing, mid-market rate, and supply-demand-ratio pricing. The RL approach, utilizing a Deep Q-Network, was evaluated with SDR-shaped pricing, showing improved community savings when battery energy storage was introduced. However, rule-based pricing remained competitive, and benefit distribution was uneven among households. AI

IMPACT This research could inform the development of more efficient and equitable electricity trading systems in decentralized energy grids.

RANK_REASON The item is an academic paper detailing a comparison of different pricing mechanisms for peer-to-peer electricity trading. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reinforcement learning and rule-based pricing compared for P2P electricity trading

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The item is an academic paper detailing a comparison of different pricing mechanisms for peer-to-peer electricity trading. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pablo Benalcazar, Maciej Kalka, Wilian Guam\'an, Jacek Kami\'nski ·

    Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities

    arXiv:2609.01680v1 Announce Type: new Abstract: 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 mechan…

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