基于多运行场景与富氧燃烧捕集技术的低碳能源系统容量优化配置

针对光热(concentrating solar power, CSP)电站利用率低、风电场弃风率高以及传统燃气机组碳排放水平较高且受“以热定电”的运行限制等问题,引入富氧燃烧捕集技术对传统机组进行改造,配置含热回收的CSP电站实现热电解耦,耦合高温固体氧化物电解池等能量转化设备,构建了电-热-氢低碳能源系统及其容量优化配置方法。首先,考虑到风电出力和光照强度的不确定性以及与电负荷之间的时序相关性,建立了基于两阶段时空聚类的多运行场景提取模型。其次,在基于概率的多运行场景基础上,通过条件风险价值(conditional value at risk, CVaR)理论度量因不确定性带来的风险,以总成本最小为目标,构建低碳能源系统容量优化配置模型。最后,通过算例进行仿真验证,结果表明该系统满足负荷需求情况下,可降低年碳排放量和弃风率,提高CSP电站利用率,并为不同风险偏好的决策者面对系统容量优化配置问题时提供了定量依据。

Capacity optimization of low carbon energy systems based on multiple operating scenarios and oxygen-enriched combustion capture technology

There is an issue of low utility rate of solar thermal power plants, high waste rate of wind power, and high carbon emission of traditional gas-fired units and a limitation of "heat-dependent power" operation, etc. To tackle this, there are the following actions to be taken: introducing oxygen-enriched combustion capture to retrofit conventional units, configuring photothermal power plants with heat recovery to realize thermal electrolysis coupling, coupling high-temperature solid oxide electrolysis cells and other energy conversion equipment to build an electricity-thermal- hydrogen low-carbon energy system and its capacity optimization allocation method. First, considering the uncertainty of wind power output and light intensity as well as the temporal correlation with electric load, a multi-run scenario extraction model based on two-stage temporal clustering is established. Second, using probability-based multi-run scenarios, this paper measures the risk caused by uncertainty through conditional value at risk (CVaR) theory, and constructs a low-carbon energy system capacity optimization allocation model with the objective of minimizing the total cost. Finally, the simulation is validated by an arithmetic example, and the results show that the system can reduce annual carbon emission and wind abandonment rate and improve the utilization rate of CSP plants when meeting the load demand. It provides a quantitative basis for decision makers with different risk preferences when facing the problem of system capacity optimization.