Short Term Optimal Operation of Water Supply Reservoir under Flood Control Stress using Model Predictive Control

Uysal G., Schwanenberg D., Alvarado-Montero R., ŞENSOY ŞORMAN A.

WATER RESOURCES MANAGEMENT, cilt.32, sa.2, ss.583-597, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s11269-017-1828-x
  • Dergi Adı: WATER RESOURCES MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.583-597
  • Anahtar Kelimeler: Reservoir operation, Optimization, Simulation, Water supply, Flood mitigation, Model Predictive Control, OPTIMAL-DESIGN, LEVEL, MANAGEMENT, BASIN
  • Anadolu Üniversitesi Adresli: Hayır

Özet

Reservoir operations require enhanced operating procedures for water systems under stress attributed to growing water demand and consequences of changing hydro-climatic conditions. This study focuses on the management of the Yuvacik Dam Reservoir for water supply and flood mitigation in the Marmara Region of Turkey. We present an improved operating technique for fulfilling the conflicting water supply and flood mitigation objectives. This is accomplished by incorporating the long term water supply objectives into a Guide Curve (GC) whereas the extreme floods are attenuated by means of short-term optimization based on Model Predictive Control (MPC). The reference case implements operating rules with a constant GC at maximum forebay elevation targeting the fulfillment of the water supply objective. We compare the reference with a new time-dependent GC, derived using an Implicit Stochastic Optimization (ISO) approach. This new curve shows nearly the same performance regarding the water supply objectives, but significantly reduces the flooding risk downstream of the dam. Possible flood events observed at the end of the wet season, when the reservoir is at the maximum level to enable water supply for the dry season, can be eliminated by the application of an additional short-term optimization by MPC. The robustness of the approach is demonstrated via hindcasting experiments.