Simulation-optimization model for Dokan reservoir system operation

Abstract EN

Reservoir operation system is the essential part of water resources management, and each reservoir has a special policy for operation.

Simulation and optimization are two different techniques for the operating process of any reservoir.

Combined simulation-optimization (S-O) model as a new technique in recent years to minimize the deficit in hydropower generation and irrigation demand has been developed for Dokan reservoir system in Kurdistan Region, Iraq.

The model combines the Simulink and genetic algorithm (GA) as techniques for simulation and optimization respectively.

For comparison, one traditional simulation model based on the standard operating policy (SOP) and two optimization models using nonlinear programming (NLP) and discrete differential dynamic programming (DDDP) optimization methods was developed.

In the present study, three performance evaluation criteria, namely; reliability, resiliency and vulnerability have been used for comparing and evaluating the developed models.

The proposed models were run over a period of 54 years using monthly time step interval, i.e.

648 months starting from Jan-1958 to Dec-2011.

The results reveal that the SOP model (Model-I) has serious deficit events in minimum downstream demands, although, it has a higher reliability in the irrigation demand (0.94).

In addition, the other models; NLP, DDDP and S-O by considering weight factors (w_1=0.2) and (w_2=0.8)almost have the same reliability, 0.90, 0.90 and 0.91 respectively.

Furthermore, the results show a low resilience for NLP model and a high vulnerability for S-O modelwhich causes higher severity of failure events.

Moreover, for the operation period from 1995 to 2011, the annual productions of hydropower are 1280, 1339, 1344and 1296 MW by increasing of 24.9, 30.64, 31.16 and 26.46 % more than the actual hydropower production (1025 MW) for SOP, NLP, DDDP and S-O models respectively.

Finally, the conclusions present that the DDDP optimization model provides high reliability as well as more power generation at the same time.

The model can be more easily applied to solve the nonlinear and multi objective problems with less computational time.