Integrated balancing and congestion management under forecast uncertainty

Forecast uncertainty related to the power production from renewables frequently leads to significant changes between the scheduled and the actual power injections. Larger and more frequent fluctuations in the power production require new approaches to handle congestion and ensure system security. In this paper, we investigate how different strategies for reserve activation influence the cost of integrating uncertain in-feeds. In particular, we assess under which conditions it is beneficial for the TSOs to balance across a large region and when deviations should be handled close to where they originate. Together with the mathematical formulation of the corresponding OPF model, which involves chance constraints to account for uncertainty, we show how the chance constraints can be reformulated to form a tractable optimization problem. We demonstrate the feasibility of our approach on the IEEE 118 bus system. The case study illustrates in particular the influence of the different balancing policies on the overall system cost as well as on the utilisation of the transmission system. Comparisons are made between the deterministic and the probabilistic approaches in conjunction with the modelled balancing and congestion management schemes. The results show how accounting for congestion when activating reserves can help reducing the overall cost of handling uncertainty.