The impact of transparency policies on local flexibility markets in electric distribution networks

Abstract

The energy transition brings various technical, economic, and organizational challenges. One major topic, especially in zonal electricity markets, is the organization of future congestion management. Local flexibility market (LFM) is an often discussed concept of market-based congestion management. Like the whole energy system, the market transparency of LFMs can influence individual bidders' behavior. In this context, the predictability of the network status of distribution networks and an LFM's outcome, depending on a given transparency policy, is investigated in this paper. For this, forecast models based on artificial neural networks (ANN) are implemented on synthetic distribution network and LFM data. Three defined transparency policies determine the amount of input data used for the models. The results suggest that the transparency policy can influence the distribution network status and LFM outcome predictability, but appropriate forecasts are generally feasible. Therefore, the transparency policy should not conceal information but provide a level playing field for all parties involved. Providing semi-disaggregated data on the network area level can be suitable for bidders' decision-making and reduces transaction costs.