Introduction: The depletion of fossil fuel resources and increasing global environmental concerns are accelerating China's transition to a low-carbon energy system, driving the development of new power systems. Wind power has become a major renewable energy source in this context. However, its inherent intermittency and volatility pose serious challenges to the stable operation of power systems. To achieve low-carbon and economically efficient operation of a multi-energy complementary system integrating wind power, Carbon Capture Power Plants (CCPP), and gas-fired generation, effective capacity planning is essential. Methods: To capture wind power uncertainty, this study employs Latin Hypercube Sampling (LHS) and Kantorovich reduction to generate representative scenarios. Unlike traditional stochastic programming or robust optimization methods, this approach enhances tractability while capturing extreme wind fluctuations. A mixed-integer linear programming model is formulated to minimize system costs, considering time-shifted CCPP operation and system constraints. The model uniquely integrates two-stage Power-to-Gas (P2G) devices, hydrogen-blended gas turbines, CCPPs, and a tiered carbon trading mechanism into a unified multi-energy planning framework. Results: The model is validated on a modified IEEE 30-bus system. Results demonstrate that the proposed method improves both the economic performance and wind power accommodation compared to conventional planning models. Discussion: Sensitivity analyses reveal that wind power penetration levels and natural gas prices significantly influence capacity planning outcomes. The planning model effectively adapts to fluctuations in renewable energy and supports robust investment decisions. Conclusion: The proposed planning model demonstrates strong practical value and potential for decision-making support in future low-carbon power systems under uncertainty.
Chen et al. (Tue,) studied this question.