2021-07-30, 12:50–13:00 (UTC), JuMP Track
In this talk, we present the open-source Power Market Tool (POMATO), which has been designed to study capacity allocation and congestion management policies of zonal electricity markets, especially flow-based market coupling.
Europe's increase in electricity production from renewable energy resources (RES) in combination with a significant decline of conventional generation capacity has spawned political and academic interest in the transmission system's ability to accommodate this transition. Central to this discussion is the efficiency of capacity allocation and congestion management (CACM) policies between and within electricity market areas that are interconnected by shared and synchronized transmission infrastructure. To facilitate unrestricted cross-border electricity trading in the presence of finite physical transmission capacity, European system and electricity market operator inaugurated flow-based market coupling (FBMC).
FBMC is a coordinated multi-stage process that requires detailed forecasts and network models, which are typically not or only partially disclosed by the system operators. Academic publications that synthesize FBMC in model frameworks agree on a three step process – D-2 (base case), D-1 (day-ahead) and D-0 (redispatch) – but differ greatly in some core assumptions. Further, FBMC effectiveness for a future renewable-dominant generation mix is typically overlooked in the current literature.
The open-source Power Market Tool (POMATO) has been designed to study CACM policies of zonal electricity markets, especially flow-based market coupling (FBMC). For this purpose, POMATO implements methods for the analysis of simultaneous zonal market clearing, nodal (N-k secure) power flow computation for capacity allocation, and multi-stage market clearing with adaptive grid representation and redispatch. Additionally, POMATO includes risk-aware optimal power flow via chance constraints to internalize forecast uncertainty during the market clearing process. All optimization features rely on Julia/JuMP, leveraging its accessibility, computational performance, and solver interfaces. The Julia Code is embedded in a Python front-end, providing flexible and easily maintainable data processing and user interaction features.