The impact of transparency policies on local flexibility markets in electrical distribution networks: a case study with artificial neural network forecasts

The energy transition brings various challenges of technical, economic and organizational nature. One major topic, especially in zonal electricity systems, is the organization of future congestion management. Local flexibility market (LFM) is an often discussed concept of market-based congestion management. Similar to the whole energy system, the market transparency of LFMs can influence the individual bidders' behavior. In this context, the predictability of the network status 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 synthetical 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 predictability of network status and LFM outcome, but appropriate forecasts are generally feasible. Therefore, the transparency policy should not conceal information but provide a level playing field for all parties involved. The provision of semi-disaggregated data on the network area level can be suitable for bidders' decision making and reduces transaction costs.