Using Multivariate Copulas to Capture Forecasting Error and Optimize Flexibility Provision From a Local Energy Community

While forming a local energy community (LEC), flexibility provision is an emerging service along with the increase of distributed energy resources (DERs) to cope with the uncertainty of DERs and the forecasting error. This paper proposes a stochastic optimization model for day-ahead scheduling to decide on the flexibility provision of LEC taking into account all profile scenarios occurring due to forecasting errors. Considering the correlation between time steps, the profile scenarios and their probabilities are generated by using Multivariate Copulas based on the historical data. In comparison with forecast-based deterministic optimization, the results show that the flexibility provision based on the proposed model is conservatively optimized, avoiding penalties due to unavailability of flexibility. Additionally, generated profiles both in forecast-based deterministic and stochastic optimization do not violate the network constraints which are verified at Bunnik living lab in the Netherlands.