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Modeling Multi-Objective Pareto-Optimal Reservoir Operation Policies using State-of-the-art Modeling Techniques
  • Aadhityaa Mohanavelu,
  • Soundharajan Bankaru-Swamy,
  • Ozgur Kisi
Aadhityaa Mohanavelu
Department of Civil Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India

Corresponding Author:[email protected]

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Soundharajan Bankaru-Swamy
Department of Civil Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
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Ozgur Kisi
Civil Engineering Department, Ilia State University, 0162 Tbilisi, Georgia
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A novel challenge faced by the water scientists and water managers today is the efficient management of the available water resources for meeting crucial demands such as drinking water supply and irrigation at the same time ensuring sufficient water is available for other critical activities such as hydro-power generation. Modeling of optimal operation polices is imminent for better management of reservoir systems especially under competing multiple objectives such as irrigation, flood control, water supply etc., with decreasing reliability of these systems under climate change. This study compares six different state-of-the-art modeling techniques namely; Deterministic Dynamic Programming (DDP), Stochastic Dynamic Programming (SDP), Implicit Stochastic Optimization (ISO), Fitted Q-Iteration (FQI), Sampling Stochastic Dynamic Programming (SSDP), and Model Predictive Control (MPC), in modeling pareto-optimal operational policies considering two competing reservoir operational objectives of irrigation and flood control for the Pong reservoir system in Beas River, India. Pareto-optimal (approximate) set of operation policies were derived using the six methods mentioned above based on different convex combinations of the two objectives and finally the performances of the resulting sets of pareto-optimal operational solutions were compared with respect to resilience, reliability , vulnerability and sustainability indices. Modeling results suggests that the optimal-operational solution designed via DDP attains the best performance followed by the MPC and FQI. The performance of Pong reservoir operation assessed by comparing different performance indices suggest that there is high vulnerability (~0.65) and low resilience (~0.10) in current operations and the development of pareto-optimal operation solutions using multiple state-of-the-art modeling techniques might be crucial for making better reservoir operation decisions.