Hydro reservoir handling in Norway before and after deregulation

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

“…The scenario problems are built according to (5) by summing contributions to the objective from (7) and adding constraints (8)- (10) for consecutive weeks along a scenario s. The iterative evaluations of the scenario problems serve to create a future expected cost function for the operational problem through cuts of type (11), as described in Section 3.1. In the following, we describe several practical trade-offs between the quality of the cuts and the computational burden of solving the scenario problems.…”

“…The EMPS is a fundamental LTHTS model based on aggregation-disaggregation techniques [5], and is in widespread use by players in the Nordic market. For all SFS runs, a scenario length of 52 weeks was used, and the endvaluation was obtained from the EMPS model run on the same case.…”

“…In particular, we evaluate the changes in expected socio-economic surplus when expanding the cable capacity towards Germany and upgrading an existing pumped-storage plant in southern Norway. The model performance is compared with the EFI's Multiarea Power Market Simulator (EMPS) model [5], which is an existing and widely used LTHTS model.…”

“…Applied methodologies for solving the high-dimensionality LTHTS problem traditionally rely on aggregation-disaggregation techniques, such as in [3][4][5][6]. That is, the physical reservoirs are represented by aggregate equivalents when computing the strategy, and then the operation is simulated on the detailed system using the obtained strategy.…”

Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation

“…Whereas simulation models can address a high level of detail, optimization approaches have traditionally been kept simpler owing to difficulties in mathematically expressing the complexity of the decision problem and in maintaining a computationally feasible optimization problem (Loucks and van Beek 2005;Harou et al 2009). The hydropower sector has historically faced similar optimization problems while trying to predict the management of reservoir systems, yielding, for example, the highest hydropower benefits (Pereira and Pinto 1991;Wolfgang et al 2009). The water value method, which is based on stochastic dynamic programming (SDP), is a widely used technique to optimize reservoir operation (Stage and Larsson 1961;Stedinger et al 1984; 1 Pereira and Pinto 1991).…”