Oxygen Storage Incorporated Into Net Power And The Allam-Fetvedt Oxy-Fuel Sco2 Power Cycle - Technoeconomic Analysis

J. J. Moore, Owen 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.
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将储氧纳入净发电和 Allam-Fetvedt 纯氧燃料 Sco2 发电循环 - 技术经济分析
随着未来对可再生能源可变性的依赖,需要具备长时间储能的能力,以减少市场波动带来的干扰。本文介绍了一种分析混合液氧(LOx)存储/直燃二氧化碳发电循环的方法,以及在各种情况下优化经济效益的方法。该系统利用改进版的 NET Power 流程,在能源供不应求时生产能源,同时通过低温储存氧气取代 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在特定情况下系统的最佳充放电时间。然后,该模型应用斜率和其他随时间变化的因素,生成一个不考虑储存因素的系统经济模型。然后再应用储能系统的大小来创建一个真实的电厂运行模型。通过真实的电站运行模型,可以计算出充放电能量、每个系统的资本支出、能源成本和收入以及其他参数。然后将这些经济参数结合起来,计算出给定情景下系统的净现值 (NPV)。然后通过 Python 中的 SMPSO 遗传算法对各种地理区域和大规模场景(高太阳能渗透率)运行该模型,以根据每个子系统的多个参数最大化净现值。此外,还将讨论 LOx 存储要求。
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