Assessment of the economic impact of forecasting errors in Peer-to-Peer energy trading

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer- to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply-demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks.