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Modeling Multi-objective Pareto-optimal Reservoir Operation Policies Using State-of-the-art Modeling Techniques

Abstract

A novel challenge faced by water scientists and water managers today is the efficient management of the available water resources for meeting crucial demands such as drinking water supply, irrigation and hydro-power generation. Optimal operation of reservoirs is of paramount importance for better management of scarce water resources under competing multiple demands such as irrigation, 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 developing pareto-optimal reservoir operation solutions considering two competing operational objectives of irrigation and flood control for the Pong reservoir located in Beas River, India. Set of pareto-optimal (approximate) solutions were derived using the above-mentioned six methods based on different convex combinations of the two objectives and finally the performances of the resulting sets of pareto-optimal solutions were compared. Additionally, key reservoir performance indices including resilience, reliability, vulnerability and sustainability were estimated to study the performance of the current operation of the reservoir. Modeling results indicate that the optimal-operational solution developed by DDP attains the best performance followed by the MPC and FQI. The performance of the Pong reservoir operation assessed by comparing different performance indices suggests 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.

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No external funding was received to undertake the study.

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A.M: Conceptualization, Writing- Original Draft, Investigation, Writing- Review and Editing, Software, Validation; S.B: Conceptualization, Writing- Review and Editing, Validation, Supervision; O.K: Writing- Review and Editing, Validation, Supervision.

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Correspondence to Aadhityaa Mohanavelu.

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Appendix

Appendix

Fig. 2
figure 2

Scheme of the implementation procedure of the performed modeling

Fig. 3
figure 3

Zone of operation discretion for the Pong reservoir confined by the minimum and maximum feasible release functions

Table 2 Overview of recent optimal reservoir operation studies
Table 3 Salient features of Pong reservoir (Note: All the levels are mentioned as reduced levels from Mean Sea Level)
Table 4 Key parameter setting of models used
Table 5 Weight combination for aggregation of objectives
Table 6 Output of modeled Decision Variables – Irrigation release- Irr (m3/s) and Flood control- Fc (m)
Table 7 Convergence time for obtaining optimal reservoir operation policies (Note: Convergence time might increase or decrease with varying computation capacity)

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Mohanavelu, A., Soundharajan, BS. & Kisi, O. Modeling Multi-objective Pareto-optimal Reservoir Operation Policies Using State-of-the-art Modeling Techniques. Water Resour Manage 36, 3107–3128 (2022). https://doi.org/10.1007/s11269-022-03191-4

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Keywords

  • Reservoir operations
  • Multi-objective optimization
  • Pareto-optimal solutions
  • Reservoir performance