Multi-Objective Optimization of Distributed Energy Systems Under Uncertainty

Uncertainty complicates the optimization model of distributed energy systems, whereas it is favorable to address the fragility of optimal solutions. This work presents a two-stage stochastic programming model to optimize the economic and environmental objective simultaneously, considering the uncertainties including energy demands and renewable energy resources. Meanwhile, the single-objective optimization is combined with the epsilon-constraint method in order to obtain the Pareto frontier, and then the decision making method is introduced to identify the tradeoff solution considering the both objectives. The results shows that, compared with the deterministic model, the stochastic model leads to an underestimation of the total cost, but a similarity of carbon emission. In addition, the computational cost of the stochastic model is significantly higher than that of the deterministic model, which means that the designers need to take their computational resources into account when modeling DES under uncertainty.