scholarly journals A Stochastic Dynamic Programming Model for Hydropower Scheduling with State-dependent Maximum Discharge Constraints

2021 ◽  
Author(s):  
Linn Schäffer ◽  
Arild Helseth ◽  
Magnus Korpås
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Stochastic Dynamic ◽  
Environmental Constraint ◽  
Maximum Discharge ◽  
State Dependent ◽  
Hydropower System ◽  
Hydropower Scheduling ◽  
Water Values

<div>We present a medium-term hydropower scheduling model that includes state-dependent environmental constraints on maximum discharge. A stochastic dynamic programming algorithm is used to enable modelling of nonconvex relationships in the problem formulation. The model is applied in a case study of a Norwegian hydropower system with multiple reservoirs. We find that the maximum discharge constraint significantly impacts the water values and simulated operation of the hydropower system. A main finding is that the nonconvex characteristics of the environmental constraint is reflected in the water values, implying a nonconvex objective function. Operation according to the computed water values is simulated for cases with and without the environmental constraint. Even though operation of the system changes considerably when the environmental constraint is included, the total electricity generation over the year is kept constant, and the total loss in expected profit limited to less than 0.8%.</div>

scholarly journals A Stochastic Dynamic Programming Model for Hydropower Scheduling with State-dependent Maximum Discharge Constraints

2021 ◽  
Author(s):  
Linn Schäffer ◽  
Arild Helseth ◽  
Magnus Korpås
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Stochastic Dynamic ◽  
Environmental Constraint ◽  
Maximum Discharge ◽  
State Dependent ◽  
Hydropower System ◽  
Hydropower Scheduling ◽  
Water Values

<div>We present a medium-term hydropower scheduling model that includes state-dependent environmental constraints on maximum discharge. A stochastic dynamic programming algorithm is used to enable modelling of nonconvex relationships in the problem formulation. The model is applied in a case study of a Norwegian hydropower system with multiple reservoirs. We find that the maximum discharge constraint significantly impacts the water values and simulated operation of the hydropower system. A main finding is that the nonconvex characteristics of the environmental constraint is reflected in the water values, implying a nonconvex objective function. Operation according to the computed water values is simulated for cases with and without the environmental constraint. Even though operation of the system changes considerably when the environmental constraint is included, the total electricity generation over the year is kept constant, and the total loss in expected profit limited to less than 0.8%.</div>

Role of hydrologic information in stochastic dynamic programming: a case study of the Kemano hydropower system in British Columbia

2014 ◽  
Vol 41 (9) ◽  
pp. 839-844 ◽  
Author(s):  
Quentin Desreumaux ◽  
Pascal Côté ◽  
Robert Leconte
Keyword(s):  
British Columbia ◽  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Snow Water Equivalent ◽  
Stochastic Dynamic ◽  
Safe Management ◽  
Hydropower System ◽  
Snow Water ◽  
Management Policies ◽  
Hydrological Variables

This paper presents a study describing the effect of various hydrological variables in stochastic dynamic programming (SDP) for solving the optimization problem of managing a hydropower system. We will show how choosing the best hydrological variables can strongly affect management policies. This is especially true for the system studied here, namely the Kemano hydroelectric system located in British Columbia, Canada, which is subject to large streamflow volumes due to significant snow cover during winter. Real-time snow water equivalent (SWE) data can be used directly as a variable in SDP management policies. Results indicate that for the system in this study, the maximum SWE (i.e., highest level of SWE observed from the start of winter to the current decision period) is the best among the methods investigated for effective, safe management, compared with Markov or order p autoregressive models when forecasts are not available.

On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty

Top ◽  
2015 ◽  
Vol 23 (3) ◽  
pp. 703-742 ◽  
Author(s):  
Unai Aldasoro ◽  
Laureano F. Escudero ◽  
María Merino ◽  
Juan F. Monge ◽  
Gloria Pérez
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Stochastic Dynamic
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