A Robust Optimization Model for Managing Uncertainty in Local Electricity Market Prices for Peer-to-Peer Energy Trading

The integration of distributed energy resources (DER), such as solar photovoltaic (PV) panels and electric vehicle (EV) parking facilities, has significantly improved power grid flexibility and efficiency. These resources not only enhance grid management but also enable consumers to reduce costs and generate additional income by trading surplus energy through peer-to-peer (P2P) energy trading. However, the effective utilization of these resources requires adopted and developed optimization strategies to manage operational uncertainties, particularly the variability of renewable energy production, which directly influences electricity market prices. This paper presents an optimization approach for managing risks posed by various uncertain parameters such as the renewable energy-based consumer systems, the availability of EVs in the parking lots, and market prices. In this paper, the uncertainties posed from the renewable resources and EV availability in the parking lots addressed through stochastic programming while the electricity market prices are managed by robust optimization. Electricity prices, as uncertain parameters, can significantly impact decision variables. Since consumers are price-takers rather than price-makers, they have no direct control over market prices, making it crucial for risk-averse decision-makers to address price volatility effectively. By analyzing uncertainties and modeling various scenarios, the proposed approach ensures optimal decision-making under different risk conditions. To evaluate its effectiveness, the model is validated on a case study and also on through a factorial sensitivity analysis with 27 scenarios in a microgrid with multiple households. The optimal configuration (10 homes, 150% PV, 200% ESS) achieves a 38.3% cost reduction compared to the baseline This study provides a comprehensive analysis of the simulation outcomes and real-world implementation, offering valuable insights into improving P2P energy trading in the face of price fluctuations.