Summary:
Hydropower stands out for its fast response ability and flexible operation, playing a predominant role in the provision of regulation reserves. As hydro power is an energy-constrained generation technology, it needs to be protected against any possible deployment of the scheduled reserves. However, the few models that formulate a detailed hourly co-optimization of energy and regulation reserves, neglect infeasibilities that could be derived from the requested reserves in real-time. As the regulation reserve market is becoming increasingly important, hydro producers can no longer neglect such effect. This paper presents a stochastic optimization model to find the optimal hourly schedule of a set of hydraulically coupled hydropower plants to obtain the weekly operation that simultaneously maximizes the expected profit in both energy and regulation reserves markets. The model is formulated for a price-taker agent, and it considers a very detailed representation of the system including minimum-maximum water flows, net head dependency, and fractional water travel time. The main contribution is that the obtained solution protects a multi-reservoir system against risk of water and storage unavailability due to the uncertainty in real-time use of regulation-up and down reserves, respectively, and that the reserves deem the net head dependency. The paper presents a realistic case study where the proposed formulation has been tested successfully with real data from the Spanish electricity market.
Keywords: Day-ahead electricity market; Secondary regulation reserve market; Stochastic programming; Cascaded reservoir system
JCR Impact Factor and WoS quartile: 2,688 - Q2 (2016); 3,900 - Q2 (2022)
DOI reference:
https://doi.org/10.1016/j.epsr.2015.08.014
Published on paper: January 2016.
Published on-line: September 2015.
Citation:
M. Chazarra, J. García-González, J.I. Pérez-Díaz, M. Arteseros Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research. Vol. 130, pp. 67 - 77, January 2016. [Online: September 2015]
