Unit commitment model development for hydropower on the Day-Ahead spot market.

Abstract: In the aftermath of the liberalization of European Energy Markets in the 2000s, Power Exchange platforms have constantly evolved towards more integrated and competitive designs, where quality forecasts and effective optimization strategies play decisive roles. This study presents the development of a hydropower scheduling optimization algorithm for the Day-Ahead spot market using Mixed Integer Linear Programming (MILP). This work was supported by the hydro asset management team of ENGIE Global Energy Markets (GEM) located in Brussels.  The model developed is focusing on the optimization of Coindre Hydraulic Power Plant (HPP), located in the highlands of Massif Central in France. With the combined water discharge of its two interconnected reservoirs, Grande-Rhue and Petite-Rhue, the powerhouse can reach up to 36 MW of power output capacity. The two reservoirs are located kilometres apart from each other and have different storage capacities and catchment areas. The reservoirs naturally exchange water due to the level difference along an interconnection pipe. Maximum power output is limited by water level differences in both reservoirs, which makes modelling complicated. These operational constraints are a limiting factor in terms of operability, as a result the scheduling process is a non-trivial task and is time-consuming.  A framing study of the power plant was conducted over a hydraulic year to identify the governing parameters of the model. The multi-reservoir nature of the optimization problem oriented the model development towards a Mixed Integer Linear Formulation. After experimenting with different solvers, Gurobi 28.1.0 was chosen for its performance in the Branch and Cut Algorithm for the power scheduling task.  The performance of the new model has been validated by re-running the model on past production plans, results show that reservoir volume errors are less than 5% of their respective capacities on a 5 days’ time-horizon. After backtesting it was found that the new optimization strategy results in higher revenue for the plant due to the optimized operation at higher average energy prices. The results also bring out the importance of proper valve actuation in the optimization strategy, as well as the need for future studies.