Oxygen Storage Incorporated Into Net Power and the Allam-Fetvedt Oxy-Fuel sCO2 Power Cycle – Technoeconomic Analysis

J. Moore, Owen M. Pryor, Ian Cormier, J. Fetvedt
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Abstract

With the planned future reliance on variable renewable energy, the ability to store energy for prolonged time periods will be required to reduce the disruption of market fluctuations. This paper presents a method to analyze a hybrid liquid-oxygen (LOx) storage / direct-fired sCO2 power cycle and optimize the economic performance over a diverse range of scenarios. The system utilizes a modified version of the NET Power process to produce energy when energy demand exceeds the supply while displacing much of the cost of the ASU energy requirements through cryogenic storage of oxygen. The model uses marginal cost of energy data to determine the optimal times to charge and discharge the system over a given scenario. The model then applies ramp rates and other time-dependent factors to generate an economic model for the system without storage considerations. The size of the storage system is then applied to create a realistic model of the plant operation. From the real plant operation model, the amount of energy charged and discharged, the CAPEX of each system, energy costs and revenue and other parameters can be calculated. The economic parameters are then combined to calculate the net present value (NPV) of the system for the given scenario. The model was then run through the SMPSO genetic algorithm in Python for a variety of geographic regions and large-scale scenarios (high solar penetration) to maximize the NPV based on multiple parameters for each subsystem. The LOx storage requirements will also be discussed.
净电力和Allam-Fetvedt含氧燃料sCO2电力循环中的氧储存-技术经济分析
由于未来计划依赖可变的可再生能源,将需要具备长时间储存能源的能力,以减少市场波动造成的破坏。本文提出了一种分析液态氧(LOx)储存/直接燃烧sCO2混合动力循环的方法,并在多种场景下优化经济性能。该系统利用NET Power过程的改进版本,在能源需求超过供应时产生能源,同时通过氧气的低温储存取代了ASU能源需求的大部分成本。该模型使用边际能源成本数据来确定给定场景下系统充电和放电的最佳时间。然后,该模型应用斜坡率和其他与时间相关的因素,为不考虑存储的系统生成经济模型。然后应用存储系统的大小来创建工厂操作的现实模型。从真实的电厂运行模型出发,可以计算出充放电电量、各系统的CAPEX、能源成本和收益等参数。然后结合经济参数来计算给定情景下系统的净现值(NPV)。然后在Python中通过SMPSO遗传算法对各种地理区域和大规模场景(高太阳穿透率)运行该模型,以最大化每个子系统基于多个参数的NPV。还将讨论液氧存储要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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