计及不确定性和阶梯碳的含CCS-P2M园区综合能源系统容量优化配置

Capacity Optimization Configuration of Park-Level Integrated Energy System with CCS-P2M Considering Uncertainties and Tiered Carbon Pricing

摘要: 在双碳目标驱动与可再生能源高比例渗透背景下，工业园区的低碳转型面临源荷双重不确定性与减排压力飙升的严峻挑战。本研究提出一种融合碳捕集–电转甲醇(CCS-P2M)耦合系统与阶梯型碳交易机制的园区综合能源系统两阶段分布鲁棒优化容量配置框架。针对风光出力与多能负荷的时序随机波动，通过改进的K-means聚类与笛卡尔积重构多维极端联合场景，并利用1-范数与∞-范数构建概率分布模糊集以量化预测偏差风险。在电–热–氢–醇异质能流物理时空耦合约束下，以年化全生命周期总成本最小为目标建立规划模型，并采用列与约束生成算法高效求解。算例研究表明：CCS-P2M系统的引入使年售醇收益达2640万元，系统年化总成本较无CCS-P2M方案降低36.4%，年净碳排放量降低55.2%，实现从被动合规向主动资源化获利的根本性转变。碳市场参数敏感性分析揭示，价格区间长度$d\in \left[1000,3000\right]$ kg、价格增长率$\alpha \in \left[0.25,0.35\right]$ 为兼顾经济效益与深度减排的推荐参数区间，为零碳工业园区的精细化能碳协同规划提供了量化决策依据。

Abstract: Driven by the dual carbon goals and the high penetration of renewable energy, the low-carbon transition of industrial parks is confronted with the severe challenges of dual uncertainties in supply and demand and mounting pressure for emission reduction. This study proposes a two-stage distributionally robust optimization (DRO) framework for capacity planning of park-level integrated energy systems (IES), incorporating a deeply coupled carbon capture system and power-to-methanol (CCS-P2M) system and a tiered carbon trading mechanism. To characterize the stochastic temporal fluctuations in wind and solar power outputs and multi-energy loads, an improved K-means clustering algorithm combined with Cartesian product reconstruction is employed to generate multi-dimensional extreme joint scenarios. A probability distribution ambiguity set is then constructed using 1-norm and ∞-norm constraints to quantify the risks arising from prediction deviations. Under the physical spatio-temporal coupling constraints among heterogeneous energy carriers—electricity, heat, hydrogen, and methanol—a capacity planning model is formulated with the objective of minimizing the annualized full-life-cycle cost, and solved efficiently via the column-and-constraint generation algorithm. Case study results demonstrate that, the integration of the CCS-P2M system yields annual methanol revenues of 26.4 million CNY, reducing the total annualized cost by 36.4% and net carbon emissions by 55.2% relative to the CCS-P2M-free baseline, fundamentally transitioning the system from passive regulatory compliance to proactive resource valorization. Sensitivity analysis of carbon market parameters reveals that an interval length of $d\in \left[1000,3000\right]$ kg and a price escalation rate of $\alpha \in \left[0.25,0.35\right]$ represent the recommended parameter ranges for jointly achieving economic viability and deep decarbonization, providing actionable quantitative guidelines for the fine-grained energy-carbon co-optimization planning of zero-carbon industrial parks.