Abstract
For water resources engineering community, reservoir operation is a complex job. Real-time reservoir operation is furthermore complex as it has to consider the real-time hydrological uncertain events. In this paper, a real-time operation model is presented for Tanahu Hydropower Reservoir System in Nepal. To handle the real-time hydrology, it has to predict the reservoir inflow, which is done by using genetic programming (GP). For this, GP-based inflow forecasted models are developed. The reservoir optimization model is solved using EMPSO method for few years’ inflow data, and the optimal solutions are obtained and used to generalize the operational policies. The release policies are used that obtained from EMPSO model and generalization is done with the function of initial storages and inflows to it by using GP model. Finally, the reservoir operation policies are formulated with the forecasted inflow. Performance of models is measured by using coefficient of determination (R2) and root mean squared error (RMSE) and found that the real-time operational model shows good accuracy.
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Abbreviations
- β i :
-
Regression coefficient
- γ :
-
Unit weight of water
- η :
-
Overall generation efficiency
- DQ t :
-
Environmental flow provided in time period t
- DQ min :
-
Minimum environment flow required in various time periods
- E :
-
Hydropower energy
- EV t :
-
Evaporation loss for any time period t
- E t :
-
Evaporation loss for any time period t, t − 1, …
- e t :
-
Rate of evaporation in time period t
- H t :
-
Difference in elevation with water level at time t
- n :
-
Number of observations
- k :
-
Number of independent variables
- O t :
-
Reservoir overflow during time period t
- P min :
-
Minimum power production limit per week
- P max :
-
Maximum power production limit per week
- P t :
-
Power production at time period t
- Q t :
-
Reservoir inflow at time period t
- R t :
-
Water release at time period t
- R min :
-
Minimum limit of water releases from the reservoir in a week
- R max :
-
Maximum limits of water releases from the reservoir in a week
- sd(.):
-
Standard deviation
- S min :
-
Allowable minimum storage volume
- S max :
-
Allowable maximum storage volume
- S t :
-
Storage volume at the beginning of time period t
- S t+ 1 :
-
Reservoir storage volume at the end of time period t
- T :
-
Set of examples that reaches the node
- T i :
-
Subset of examples that have the ith outcome of the potential set
- T t :
-
Number of plant operating hours in a week
- t :
-
Time step
- EC:
-
Evolutionary Computation
- FSL:
-
Full Supply Level
- GP:
-
Genetic Programming
- GWh:
-
Giga Watt hour
- IIL:
-
Intake Invert Level
- JJAS:
-
June, July, August, and September
- MLR:
-
Multiple Linear Regression
- MT:
-
Model Tree
- MOL:
-
Minimum Operation Level
- MW:
-
Mega Watt
- MWh:
-
Mega Watt hour
- PSO:
-
Particle swarm optimization
- RMSE:
-
Root Mean Square Error
- SDR:
-
Standard Deviation Reduction
- TWL:
-
Tail water level
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Ghimire, B.N.S., Shrestha, R.N., Bhatta, U.D. (2020). Real-Time Reservoir Operation Policy: A Case Study of Tanahu Hydropower Project. In: AlKhaddar, R., Singh, R., Dutta, S., Kumari, M. (eds) Advances in Water Resources Engineering and Management. Lecture Notes in Civil Engineering , vol 39. Springer, Singapore. https://doi.org/10.1007/978-981-13-8181-2_3
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DOI: https://doi.org/10.1007/978-981-13-8181-2_3
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