A risk-averse approach for the planning of a hybrid energy system with conventional hydropower

• Published in: Computers & operations research Vol. 126; pp. 105092 - 15
• Main Authors: Çavuş, Özlem, Kocaman, Ayse Selin, Yılmaz, Özlem
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.02.2021; Pergamon Press Inc
• Subjects: Algorithms; Bootstrapping; Conditional Value-at-Risk; Exact solutions; Hybrid energy system; Hybrid systems; Hydroelectric power; Hydropower; Operations research; Risk; Risk-averse two-stage stochastic programming; Scenario decomposition; Conditional Value-at-Risk; Bootstrapping; Scenario decomposition; Hydropower; Risk-averse two-stage stochastic programming; Hybrid energy system
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2020.105092

•We propose a risk-averse model for a hybrid energy system with conventional hydropower.•We assume that the amount of streamflow to the reservoirs is uncertain.•We present a case study for the Mediterranean Region in Turkey using real data.•We use a non-parametric bootstrapping model to generate scenarios for streamflow.•We propose an exact solution approach based on L-Shaped method to solve our model. We present a risk-averse two-stage stochastic programming model for the planning of a hybrid energy system with conventional hydropower component. Using Conditional Value-at-Risk as our measure of risk-aversion, we take into consideration the dispersion of the random total cost arising due to uncertain streamflow amount. We propose an exact solution approach based on scenario decomposition to solve our large scale problem. We then present a case study for the Mediterranean Region in Turkey and generate scenarios using a modified k-nearest neighbor algorithm for bootstrapping the historical time series data of Manavgat River. The results of our computational study show how an optimal solution differs based on the degree of risk-aversion and demonstrate the computational power of our solution approach. Our algorithm is able to solve instances that cannot be solved by CPLEX, furthermore, CPLEX requires 5.84 times more computation time than our algorithm.