Parametric sensitivity analysis under double-uncertainty optimization in distributed integrated energy systems

Distributed integrated energy systems have the advantages of cleanliness and flexibility, contributing to the sustainable development of community and urban energy systems. The uncertainty of meteorology and load affects the planning and operation of the system, and it is of great significance to study the impact of key parameter fluctuations on performance indicators. Taking a regional energy system in Northwest China as a research case, the optimal configuration under certainty conditions is obtained through multi-objective optimization. Wind speed, solar irradiance, ambient temperature, and electrical load are used as uncertainty parameters of energy input and output, combined with Latin hypercube sampling and Monte Carlo simulation, to acquire the equipment capacity change when uncertainty is considered. Based on the planning results, the performance sensitivity of distributed energy systems is comparatively explored in terms of economy, environment, and energy utilization in the presence of varying degrees of stochastic fluctuations in meteorological parameters, electrical load, and electricity prices. The results show that wind speed has the greatest impact on the environment and energy utilization, while solar irradiance has the most obvious influence on economy, and ambient temperature has the least effect on the comprehensive performance of the system. After uncertainty optimization, the system is less sensitive to load and electricity prices, and price-based demand response can effectively improve the economics of the system when electricity prices change. This work aims to improve the robustness of distributed energy systems under double uncertainties, which is of great theoretical value and practical guidance for promoting the optimal design and operation management of distributed energy systems.