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Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves

机译:使用剩余负荷持续时间曲线在长期能源经济模型中代表电力部门的可变性和可变可再生能源的整合

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摘要

We introduce a new method for incorporating short-term temporal variability of both power demand and VRE (variable renewables) into long-term energy-economy models: the RLDC approach. The core of the implementation is a representation of RLDCs (residual load duration curves), which change endogenously depending on the share and mix of VRE. The approach captures major VRE integration challenges and the energy system's response to growing VRE shares without a considerable increase of numerical complexity. The approach also allows for an endogenous representation of power-to-gas storage and the simultaneous optimization of long-term investment and short-term dispatch decisions of non-VRE plants. As an example, we apply the RLDC approach to REMIND-D, a long-term energy-economy model of Germany, which was based on the global model REMIND-R 1.2. Representing variability results in significantly more non-VRE capacity and reduces the generation of VRE in 2050 by about one-third in baseline and ambitious mitigation scenarios. Explicit modeling of variability increases mitigation costs by about one fifth, but power-to-gas storage can alleviate this increase by one third. Implementing the RLDC approach in a long-term energy-economy model would allow improving the robustness and credibility of scenarios results, such as mitigation costs estimates and the role of VRE. (C) 2015 Elsevier Ltd. All rights reserved.
机译:我们引入了一种将电力需求和VRE(可变可再生能源)的短期时间可变性纳入长期能源经济模型的新方法:RLDC方法。该实施方案的核心是RLDC(残余负载持续时间曲线)的表示,其会根据VRE的份额和混合而发生内在变化。该方法在不显着增加数值复杂性的情况下,捕获了主要的VRE集成挑战以及能源系统对不断增长的VRE份额的响应。该方法还可以内在地表示电力到天然气的存储,并同时优化非VRE工厂的长期投资和短期调度决策。例如,我们将RLDC方法应用于德国的长期能源经济模型REMIND-D,该模型基于全球模型REMIND-R 1.2。具有代表性的可变性导致非VRE容量显着增加,并且在基线和雄心勃勃的缓解方案中,到2050年,VRE的生成量将减少约三分之一。显式的可变性建模将缓解成本提高了约五分之一,但电能存储可以将这种增长降低三分之一。在长期能源经济模型中实施RLDC方法可以提高情景结果的稳健性和可信度,例如减排成本估算和VRE的作用。 (C)2015 Elsevier Ltd.保留所有权利。

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