1 line
6.6 KiB
Plaintext
1 line
6.6 KiB
Plaintext
原文信息：Why we must move beyond LCOE for renewable energydesign原文链接：https://www.sciencedirect.com/science/article/pii/S2666792422000300Highlights（1）Windand solar generation devaluation occurs for grids with high renewableshares.（2）This effect, due to intermittency, dramatically reduces the value ofgenerated energy.（3）Cost of Valued Energy can be used to account for thisintermittency penalty.（4）Minimizing COVE can improve VRE system design tobetter meet demand.摘要由于Levelized Cost ofEnergy（LCOE）忽略了电力价格随时间的变化，风能和太阳能固有的间歇性对其未来设计的平准化能源成本（LCOE）的相关性提出了挑战。Cost ofValuedEnergy（COVE）是一个改进的评估指标，它考虑了电价随时间的变化。需要注意的是，它整合了短期（如每小时）风能和太阳能的“发电量贬值”，由此，对于具有较高可再生能源渗透率的电网而言，较高的风能或太阳能发电可能导致较低，甚至是负能源价格。这些方面通过两个具有较高可再生能源份额的大型电网例子来证明和量化，并使用三种方法来模拟每小时的价格：（1）剩余需求，（2）风能和太阳能发电，以及（3）统计价格-发电的相关性。这三种方法都显示出明显的发电量贬值。剩余需求方法提供了最准确的价格信息，而统计相关性表明，发电量贬值对主导市场份额的VariableRenewableEnergy（VRE）最为明显（例如，加州的太阳能和德国的风能）。在一些情况下，与LCOE相比，太阳能的估值能源成本高出43%（CAISO），风能高出129%（ERCOT）。这表明在这些市场中，COVE是一个比LCOE更有价值的指标。这是因为COVE是基于年度系统成本与年度市场收入的关系，从而考虑了成本与收入以及供应与需求的经济效应。因此，建议用COVE（而不是LCOE）来设计和评估下一代可再生能源系统，包括集成储能的权衡。然而，在走向能源碳中和的未来中，为预计电网和市场开发发电量贬值模型需要更多的工作，这可以更好的对电网特征进行分类。AbstractTheinherent intermittency of wind and solar energy challenges the relevance ofLevelized Cost of Energy (LCOE) for their future design since LCOE neglectsthe time-varying price of electricity. The Cost of Valued Energy (COVE) is animproved valuation metric that takes into account time-dependent electricityprices. In particular, it integrates short-term (e.g., hourly) wind and solarenergy “generation devaluation”, whereby high wind and/or solar energygeneration can lead to low, and even negative, energy prices for grids withhigh renewable penetration. These aspects are demonstrated and quantified withexamples of two large grids with high renewable shares using three approachesto model hourly price: (1) residual demand, (2) wind and solar generation, and(3) statistical price-generation correlation. All three approaches indicatesignificant generation devaluation. The residual demand approach provides themost accurate price information while statistical correlations show thatgeneration devaluation is most pronounced for the Variable Renewable Energy(VRE) that dominates market share (e.g., solar for California and wind forGermany). In some cases, the cost of valued energy relative to levelized costcan be 43% higher for solar (CAISO) and 129% higher for wind (ERCOT). Thisindicates that COVE is a much more relevant metric than LCOE in such markets.This is because COVE is based on the annualized system costs relative to theannualized spot market revenue, and thus considers economic effects of costsvs. revenue as well as those of supply vs. demand. As such, COVE (instead ofLCOE) is recommended to design and value next-generation renewable energysystems, including storage integration tradeoffs. However, more work is neededto develop generation devaluation models for projected grids and markets andto better classify grid characteristics as we head to a carbon-neutral energyfuture.KeywordsLCOE; COVE; Cost of energy; Wind; Solar; Renewable energy;Devaluation; DemandGraphical abstractFig. 1. Energy generation for CAISO foran example 24-h day in 2021 CAISO data showing large variations in renewablepenetration.Fig. 2. Normalized hourly electricity prices as a function ofresidual demand (carbon-based demand) showing that the mean trends arerepresented by a linear price model shown by blue lines (and equations) ofaverage price for a given residual value for: (a) Germany data (green symbols)for 2019, and (b) CAISO data (red symbols) for 2021. For Germany and CAISO,respectively, the linear models have an R2=0.667 and R2=0.234 when based onall data, and an R2=0.998 and R2=0.981 when based only on the mean price for agiven normalized residual.Fig 3. Normalized electricity hourly spot price as afunction of percentage of wind and solar (combined) relative to all generationshowing that a quadratic price model represents the average price trends for:(a) Germany (green symbols) with R2 of 0.508 for all data and (b) CAISO (redsymbols) with R2 of 0.159 for all data.Fig 4. Normalized electricity hourlyspot price as a function of percentage of wind (top row) or solar (bottom row)generation relative to all generation along with quadratic model curves: (a)Germany (green symbols) for all data and (b) CAISO (red symbols) for alldata.Fig 5. Influence of operating capacity factor ranges for various energygeneration sources, showing that COVE>LCOE for intermittent sources butCOVE<LCOE for dispatchable sources关于Applied Energy本期小编：王昊博；审核人：张俊涛《AppliedEnergy》是世界能源领域著名学术期刊，在全球出版巨头爱思唯尔 (Elsevier) 旗下，1975年创刊，影响因子11.446，CiteScore20.4，高被引论文ESI全球工程期刊排名第4，谷歌学术全球学术期刊第50，本刊旨在为清洁能源转换技术、能源过程和系统优化、能源效率、智慧能源、环境污染物及温室气体减排、能源与其他学科交叉融合、以及能源可持续发展等领域提供交流分享和合作的平台。开源（OpenAccess）姊妹新刊《Advances in Applied Energy》现已正式上线。在《AppliedEnergy》的成功经验基础上，致力于发表应用能源领域顶尖科研成果，并为广大科研人员提供一个快速权威的学术交流和发表平台，欢迎关注！公众号团队小编招募长期开放，欢迎发送自我简介（含教育背景、研究方向等内容）至wechat@applied-energy.org点击“阅读原文”提交文章吧喜欢我们的内容？点个“赞”或者“再看”支持下吧！ |