Distributionally robust chance-constrained optimization of MEPS considering hydrogen-containing and phased carbon trading mechanisms

In the context of the energy revolution, the new power system is gradually transforming into an energy internet. Sustainability and uncertainty in power systems have become a challenge in energy system planning and operation. To address the above problems, this paper proposes a distributed integrated energy system with an uncertainty scheduling problem. First, the distribution and transmission processes of the electricity-gas-hydrogen system are finely modeled, with the energy system being an electricity-gas transmission network and a multi-energy flow energy hub distribution network. Afterward, an ambiguity set is generated based on historical data and Latin hypercube sampling to construct a multi-energy power system with data-model-driven distributionally robust chance-constrained optimization for system cost minimization. Finally, the computable distributionally robust chance constraint expression is established from historical data. The comparative analysis of the arithmetic example shows that the system self-sufficiency is improved by 3.74%, carbon emission is reduced by 2.26%, and the total cost is reduced by 9.87%.