Efficient Simulator for P2P Energy Trading: Customizable Bid Preferences for Trading Agents

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences.