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

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Abstract:The establishment of more severe hydrological environmental constraints, usually as seasonal minimum flows (ϕ) and maximum ramping rates (ρ), on hydropower operation is a growing trend. This paper presents a study on the influence of ϕ and ρ on the water values (WV) of a real hydropower plant that participates in the Spanish day-ahead electricity market. For this purpose, a master-slave algorithm, based on stochastic dynamic programming (SDP) and deterministic mixed integer linear programming (DMILP), is used on a real hydropower plant. The master module, based on SDP, has a yearly planning period with weekly time steps and considers three state variables: stored water volume in the reservoir at the beginning of each week; weekly water inflow; and average weekly energy price. The slave module, based on DMILP, has a weekly planning period with hourly time steps and considers many features of the hydropower plant operation, such as: start-up costs, evaporation, wear and tear costs, etc. The results indicate that WV of a hydropower plant are very sensitive to the presence of these constraints; ϕ especially during the wettest season and ρ during the driest one. As the severity of ϕ and ρ increase, WV increase and decrease, respectively.

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