Abstract:The authors describe a method for long-term hydro-thermal scheduling allowing treatment of detailed large-scale hydro systems. Decisions for each week are determined by solving a two-stage stochastic linear programming problem considering uncertainty in weather and exogenous market prices. The overall scheduling problem is solved by embedding such two-stage problems in a rolling horizon simulator. The method is verified on data for the Nordic power system, studying the incremental changes in expected socio-economic surplus for expansions in both the transmission and generation systems. Comparisons are made with a widely used existing long-term hydro-thermal scheduling model. The results indicate that the model is well suited to valuate the flexibility of hydropower in systems with a high share of intermittent renewable generation. Nomenclature Index setsA set of price zones C o, t set of Benders cuts for scenario o and week t D a set of price-elastic demand steps in zone a G a set of thermal generators in zone a ℋ a set of hydropower modules in zone a K set of time steps within the week ℒ a set of interconnections connected to zone a ℳ set of exogenous markets N h set of efficiency-curve segments for module h P a set of pumps in zone a S set of N S scenarios S R reduced set of N R scenarios ω h

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This paper presents a method for solving the long-term multireservoir hydro-thermal scheduling problem based on stochastic linear programming. Decisions for each week are determined by solving a two-stage stochastic linear programming problem, where reservoir inflow stochasticity for future weeks is represented as a fan of historical inflow records. The overall scheduling problem is then solved as a sequence of such problems. The method is tested on a data set for the Nordic power system, and its performance-in terms of socio-economic surplus, reservoir handling and computation time-is compared with a well established model for solving the identical problem.