Locational scenario-based pricing in a bilateral distribution energy market under uncertainty

In recent years, there has been a significant focus on advancing the next generation of power systems. Despite these efforts, persistent challenges revolve around addressing the operational impact of uncertainties on predicted data, especially concerning economic dispatch and optimal power flow. To tackle these challenges, we introduce a stochastic day-ahead scheduling approach for a community, with bilateral distribution interactions and guided by a locational scenario-based pricing mechanism. This method involves iterative improvements in economic dispatch and optimal power flow, aiming to minimize operational costs by incorporating quantile forecasting. Then, we present a real-time market and payment problem to handle optimization in real-time decision-making and payment calculation. This work contributes to the development of smart electricity markets by integrating advanced forecasting, decentralized trading, and grid-aware optimization. The proposed framework leverages historical data and data-driven methods to improve decision-making under uncertainties, enabling more flexible, efficient, and sustainable energy management. We assess the effectiveness of our proposed method against benchmark results and conduct a test using data from 50 real households to demonstrate its practicality. Furthermore, we compare our method with existing studies in the field across two different seasons of the year. In the summer season, our method decreases optimality gap by 60 % compared to the baseline, and in the winter season, it reduces optimality gap by 67 %. Moreover, our proposed method mitigates the congestion of distribution network by 16.7 % within a day caused by uncertain energy, which is a crucial aspect for implementing smart electricity markets in the real world.