Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast

Ehsan Mostaghimzadeh, Arash Adib, Seyed Mohammad Ashrafi and Ozgur Kisi

Agricultural Water Management, 2022, vol. 265, issue C

Abstract: Long-term changes in reservoir inflow due to climate changes and human interference violate the assumptions of hydrologic stationarity especially in the reservoir design. Utilization of uncertain prediction into a reservoir operating rule curves somehow reflects the challenges that imposed by nonstationary conditions. This study proposes a hedging based policy incorporated forecast term to manage release decisions in two separate phases. Hedging is applied firstly regarding to reservoir water level similar to conventional hedging rules and secondary according to an extra simulation in the near future. To determine the time interval of future effects, an exterior optimization model is introduced to handle the trade-off between forecast uncertainty and future information which imposed by forecast horizon. Future inflows are forecasted introducing a model including a wrapper-based feature selection method and AdaBoost.RT as a learning algorithm. The results of applying the model to a real six reservoir system in IRAN showed that incorporating future inflows into the real time decisions significantly improves the total squared relative deficit about 20% and 10% compared to conventional hedging rule curve (CHRC) and standard operation policy as objective function. Also having a glance at the near future reduces the vulnerability of the system about 5% and 27% respectively against CHRC and SOP. The results also showed that, although the SOP reaches to a best reliability of satisfying water demands in total system as 31% and 27% better than CHRC and the proposed two-phase policy, but the number of intensified failures was higher than two others which somehow influences on volume-based indices like vulnerability.

Keywords: Hedging policy; Forecast uncertainty; Nonstationarity; Climate change; AdaBoost algorithm; Teleconnection indices (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:265:y:2022:i:c:s0378377422000890

DOI: 10.1016/j.agwat.2022.107542

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