Robust low-carbon scheduling optimization for energy hub amidst bilateral uncertainties in source-side and load-side conditions

Abstract

In the era of burgeoning renewable integration, the shift toward low-carbon energy hubs is a pivotal developmental trajectory. Amidst this paradigm, the operational challenges posed by the inherent uncertainty of variable renewable sources, such as wind and solar power, alongside stochastic load fluctuations, must be reckoned with. Herein, we present an innovative, economically viable low-carbon operational strategy that embraces fuzzy opportunity constraints, thereby accommodating the dual-sided uncertainty impact on energy hubs. First, an advanced optimization framework is developed for an energy hub that holistically couples electricity, cooling, gas, and heat sectors. Leveraging energy conversion technologies, it amplifies the complementary interaction among diverse energy sources and implements an integrated demand response model to mitigate load variability. Subsequently, ladder-type carbon trading and green certificate trading mechanisms are incorporated, designed to pare down both carbon emissions and operational expenditures. Addressing the unpredictability of grid-connected wind and solar resources, the model introduces fuzzy chance constraints. These transform rigid deterministic system limitations into more flexible constraints encapsulating fuzzy variables and employing trapezoidal fuzzy parameters to elucidate their nature. The robustness and practical utility of the proposed model are substantiated through meticulous case analyses.