Optimization of a refined power-to-gas integrated energy system considering reward-penalty stepped carbon trading mechanism

Aiming at the low-carbon economic operation of integrated energy systems (IES), this paper proposes a collaborative optimization model that addresses multi-source renewable energy uncertainty by integrating refined power-to-gas (P2G) technology with a reward-penalty stepped carbon trading mechanism. First, the original wind-PV historical data are transformed into marginal distributions for wind and PV power using kernel density estimation (KDE). Subsequently, based on these marginal distributions, a multi-time-scale joint probability model for wind-PV output is established through Copula theory. By combining Latin hypercube sampling (LHS) combined with K-means clustering is then applied to generate a set of typical scenarios that retain the spatiotemporal coupling characteristics of renewable generation. On this basis, P2G technology is introduced to realize bidirectional conversion between electricity and natural gas as well as carbon cycle utilization, and a dynamic carbon trading cost function with reward and penalty factors is constructed. Through sensitivity analysis of carbon trading parameters, the influence mechanism of stepped carbon price thresholds and reward- penalty coefficients on system economy is revealed. Finally, comparative experiments based on four different scenarios demonstrate that the proposed method can substantially reduce carbon emissions and total costs, providing an innovative solution for the low-carbon economic operation of IES under the background of the new power system. • EL-MR coupled for electricity-hydrogen-carbon closed-loop P2G system. • KDE-Copula hybrid model for wind-PV output joint probability distribution. • Latin hypercube-K-means combined sampling for high-confidence typical scenarios. • Reward-penalty stepped carbon trading mechanism with carbon market parameter sensitivity analysis.