Oral Presentation
Registered User PID: jhstagge
OPTIMIZATION OF MULTI RESERVOIR OPERATING RULES USING GENETIC
ALGORITHMS
James H. Stagge*, Glenn E. Moglen*
* Department of Civil and Environmental Engineering, Virginia Tech
Continued urban expansion coupled with predictions of longer inter-rainfall (time between
precipitation events) periods in the Mid-Atlantic States due to climate change points to a potential increase in
the frequency and severity of drought conditions. In light of this evolving water supply situation,
assessments of water resources management policies are necessary to ensure that water supply systems
address current needs, while also being sufficiently robust to satisfy future requirements. The Washington
DC metropolitan area (WMA) water supply is of particular interest because the region’s water is governed
primarily by the Potomac River, which in turn is supplemented by a system of reservoirs located
approximately 9-10 days travel time upstream of the city’s intakes (Figure 1). This design allows the
Potomac watershed to remain largely uncontrolled, but increases the importance of effective water
management decisions. In the face of hydrologic uncertainty brought about by long travel times and the
region’s complex government and institutional landscape, a system is needed to quantitatively and
objectively determine the optimum timing, magnitude and location of water supply releases.
The objective of this study is to derive an optimal water management scheme for the WMA, based
on advanced optimization methods. A realistic hydrologic model of the WMA system was developed using
node-arc architecture, including reservoirs, reaches, inputs and withdrawals. Reservoir operating rules were
optimized under current demand and climate conditions using long duration simulations of historic and
synthetic streamflow time series. Optimization was performed using a genetic algorithm solver wrapped
around OASIS, a water management linear program solver (Hydrologics, Inc.). Operating rules were solved
via linear programming using a daily time step, mimicking the imperfect foresight of daily operational
decision-making. Performance of the system under optimized rules was compared to simulations of the
system using the existing reservoir release strategy. Additionally, rule curve parameters were compared to
the existing policy to determine the nature of the difference (Figure 2).
This research focuses on optimization of the current water management system, but is an important
first step towards evaluation of the system under future conditions. Application of genetic algorithms to
reservoir optimization remains a relatively new field [1,2,3] and its application to a system with a significant
lag between release and benefit represents an interesting contribution. In addition to its significance to water
management research, this study is important in potentially improving the efficiency of the WMA water
supply system under drought conditions and supporting ongoing reliability studies by the Interstate
Commission on the Potomac River Basin (ICPRB).
Word Count: 411
REFERENCES:
[1]
Chen, L. (2003). “Real coded genetic algorithm optimization of long term reservoir operation.” J. Amer. Water Res.
Assoc, 39(5).
[2]
Oliveira, R., and Loucks, D.P. (1997). “Operating rules for multireservoir systems.” Water Resour. Res., 33(4).
[3]
Momtahen, S. and Dariane, A.B. (2007). “Direct search approaches using genetic algorithms for optimization of water
reservoir operating policies.” J. Water Resour. Plann. Manage,133 (3): 202-209.
Figure 1. Washington D.C. Metropolitan Area (WMA) water supply system with the Potomac watershed boundary
outlined in green. Reservoir volumes are presented in Million Cubic Meters (MCM).
450
Jennings-Randolph Elevation (m- MSL)
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Jan
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USACE Max
USACE Avg
USACE Min
Max. Curve
Ave. Curve
Min. Curve
Figure 2. Sample optimized rule curve showing monthly maximum, minimum and target storage elevations. Existing
rule curves are presented as dashed lines, while optimized curves are shown as solid lines.