Evaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System


Bacaksız E., Opan M., Kara Dilek Z. E., KARADENİZ M.

Water Resources Management, cilt.37, sa.15, ss.5829-5848, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 15
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11269-023-03633-7
  • Dergi Adı: Water Resources Management
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.5829-5848
  • Anahtar Kelimeler: Critical Cases, Dynamic Programming, Energy Production, Multi-Reservoir Systems, Multivariate Regression Analysis, SPSS
  • Kocaeli Üniversitesi Adresli: Evet

Özet

In a multi-reservoir system, the stochastic nature of basin data resulting from rainfall introduces risk into water management operations. Effective management that accounts for these risks can obtain maximum benefits from the system. This study presents a description of a multi-reservoir water resources system with hydroelectric power plants, utilizing the energy optimization model developed by OPAN in 2007. The model was applied to reservoirs located successively on the Lower Kızılırmak River in the Kızılırmak Basin, with the objective function being the maximization of firm power by using drought period inflows and total energy by using monthly average inflows. The study considered three scenarios: deterministic, probabilistic, and risky (critical cases), with probabilities of inflows from the basin being determined for the latter. Monthly inflows with determined probabilities were used to obtain data for the risky case. Optimum operating levels were determined based on this data to maximize firm power and total energy. According to the operating levels, the reservoir with the largest useful volume manages the operation. The values obtained from the optimization were then used in multivariate regression analysis using the Statistical Package for Social Scientists (SPSS), a statistical analysis program. The analysis explored the effects of monthly operating levels in the reservoirs, the amount of inflow released from the spillway, the amount of inflow released for energy production, the monthly average inflow to the reservoirs, and firm power values on energy production.