Global sensitivity analysis and stochastic optimization of multi-energy complementary distributed energy system considering multiple uncertainties

Multiple uncertainties exist in the planning and design of multi-energy complementary distributed energy system (DES). In order to solve this problem, a more complete framework of two-stage stochastic programming (TSP) approach for optimal multi-energy complementary DES planning and design under multiple uncertainties is proposed. The developed TSP model is formulated as a mixed integer linear programming, which is intended to minimize the equivalent annual cost (EAC) of the multi-energy complementary DES. Probability scenarios of key uncertain parameters selected by treed Gaussian process method for the TSP model are generated and reduced using discrete approximations of probability distributions method and random vector sampling method. The developed method framework is applied to a case study high-rise office building in planning stage to illustrate the TSP model's output. And multiple evaluation indicators including configuration, economy, energy and environment are used to compare and analyze the optimal schemes obtained by deterministic model and TSP model under different building load scenarios. The results show that of the 26 uncertain parameters considered only 7 are selected as the key parameters for the TSP model in this paper. EAC of the optimal multi-energy complementary DES may be overestimated if without considering key uncertain parameters. Thus, the deterministic optimization practices and the cost estimates resulting from them can be considered unreliable. Moreover, the multi-energy complementary DES would want to increase CO2 emission to achieve better economic performance. Finally, the energy share distribution of all scenarios in the stochastic model provides more reliable and more accurate information for decision-makers.