Benders decomposition algorithm for sustainable energy hub design under risk considering Conditional Value at Risk

This paper presents a mixed integer linear program (MILP) model as well as a solution method based on the Benders decomposition algorithm for optimal design of sustainable Energy Hub (EH). A modeling framework to consider environmental (Env) and social (Soc) impacts of the EH's components is incorporated to achieve sustainable EH. First, the life cycles of different components are analyzed in order to determine Env and Soc impacts. Then, the EH design model is developed by using scenarios-based stochastic programming integrating Conditional Value-at-Risk (CVaR) in its objective function as a risk criterion to deal with uncertain nature of parameters. Benders decomposition (BD) algorithm is used to decompose the original problem in order to address heavy computational burden of the problem, especially when a large number of scenarios is used to properly represent uncertainties. The results shows effectiveness of the proposed BD to handle large problem sizes compared to the CPLEX solver and indicates that taking the external cost into account resulted in higher renewable Distributed Energy Resources (DERs) in the optimal configuration, which have lower negative Env and Soc impacts. Also, strength of stochastic programming in handling data uncertainty and controlling risk level is investigated.