Assessing hydropower operational profitability considering energy and reserve markets

Abstract

The authors analyse the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting. A mathematical model based on stochastic dynamic programming is used to compute the water values for the system considering different power plant configurations. The uncertainties in inflow and both energy and reserve capacity prices are considered through a discrete Markov chain. Subsequently, the system operation is simulated based on the obtained water values to assess system performance and expected revenues from the two markets. The model is applied in a case study for a Norwegian hydropower producer, showing how the power plant operation changes and profitability increases when considering sale of reserve capacity. The authors emphasise on how the water values are influenced by the opportunity to sell reserve capacity, and assess how the representation of non-convex relationships in the water value computations as well as simulation influence the profitability. P g max , P g min Max./Min. capacity, MW Q min minimum river flow, m 3 /s Q gm discharge in point m, m 3 /s T number of weeks in planning horizon V max , V min Max./Min. reservoir volume, Mm 3 V n reservoir volume at point n, Mm 3

Nonconvex Environmental Constraints in Hydropower Scheduling

“…Although the MIP problem formulated in the previous section could be solved directly using classical stochastic dynamic programming such as in [18], this type of algorithm has limits in dealing with the state space in multi-reservoir systems. The recently developed SDDiP algorithm [6] is believed to have a higher potential for handling realistic multireservoir hydropower systems than SDDP, and is outlined in the following.…”

Profitability improvement of a reservoir power station based on virtual reservoir bidding

“…Few scholars pay attention to the profitability improvement of hydropower enterprises. Helseth et al analysed the operational profitability of a hydropower system selling both energy and reserve capacity in a competitive market setting, showing that how the power plant operation changes and profitability increases when considering the sale of reserve capacity (Helseth et al, 2017). The primary contribution of other work (Akcay et al, 2017) lied in the creation of a method that allowed investors to assess the profitability of a hydropower investment using a stochastic approach.…”

A two‐stage stochastic optimisation model for the water value calculation in a hybrid diesel/wind/pumped‐storage power system

“…We are in this way emulating a decision process with a reliable 24-h ahead net demand forecast [40] and with imperfect information of the next 312 h. We have chosen to use a two-stage approach instead of a multistage one, mainly because the water values are to be updated daily. The scenario tree we have used is referred to by some authors as scenario fan [41]. It is not the aim of this paper to determine the most suitable structure for the scenario tree in the second stage (last 312 h).…”